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You’ll work alongside a small, experienced team that ships continuously and stays close to customers. We use a modern stack, accelerate with AI, keep onboarding fast, and aim to have every new engineer ship a production feature on their first day. Every engineer owns meaningful product areas with direct customer impact.
We value completed projects, sound judgment, learning speed, and clear communication over a perfect résumé or exact stack experience. New graduates and candidates with up to three years of experience are encouraged to apply.
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Please only post if you personally are part of the hiring company—no\nrecruiting firms or job boards. One post per company. If it isn't a household name,\nexplain what your company does.
Please only post if you are actively filling a position and are committed\nto replying to applicants.
Commenters: please don't reply to job posts to complain about\nsomething. It's off topic here.
Readers: please only email if you are personally interested in the job.
Searchers: try https://nthesis.ai/public/hn-who-is-hiring, https://dheerajck.github.io/hnwhoishiring/,\nhttp://nchelluri.github.io/hnjobs/, https://hnjobs.emilburzo.com.
Don't miss this other fine thread: Who wants to be hired? https://news.ycombinator.com/item?id=49156682","time":1785769254,"title":"Ask HN: Who is hiring? (August 2026)","type":"story"}}},{"id":199,"item_id":174,"score":72,"category":"b2b_lead","summary":"Q00/ouroboros","status":"new","note":"","ai_summary":"原文核心：Agent OS: the agent gets smarter on its own. We just hold the line: the grading command and expected result never make it into the success contract we hand it. Interview-gated, staged evaluation, budgeted evolution loop.","recommended_action":"先根据原文核心判断是否值得打开来源；如果相关，再补充人工判断。","proposal_draft":"未说明","risk_note":"未说明","immediate_pushed_at":null,"digest_pushed_at":null,"created_at":"2026-08-16T08:07:21.022420+00:00","updated_at":"2026-08-16T08:18:22.987257+00:00","llm_analyzed_at":"2026-08-16T08:18:22.987257+00:00","source_id":"github_mcp_repos","url":"https://github.com/Q00/ouroboros","title":"Q00/ouroboros","content":"Agent OS: the agent gets smarter on its own. 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Wow, that was a mouthful... If you found it interesting, join Reef Technologies and tackle complex technical challenges like this on your own terms: - Contribute from wherever you like; we are fully remote - Set your own time commitment, as long as it's at least 30h per week - See how we work in our handbook: github.com/reef-technologies/handbook - Influence the way we operate through our Sociocracy 3.0 decision-making process Salary on a B2B contract: - 45-70 USD or 180-280 PLN per hour - Flexible work schedule (as measured with a time tracker) - We automatically adjust rates based on inflation twice a year We're looking for someone with 5+ years of programming experience, including at least a year with Python (including open-source and significant personal projects). So, what do you think? You don't even need a CV; just go to https://careers.reef.pl/?utm_source=hackernews and shoot a few quick commands to apply. Edit: If you applied and haven't heard back, please check the spam folder of the email associated with the GitHub account you used to apply — our replies sometimes end up there.","author":"mwapinska","published_at":"2026-08-04T09:24:45Z","fetched_at":"2026-08-16T08:03:09.494751Z","item_created_at":"2026-08-16T08:04:20.376969+00:00","matched_keywords":["python","github","contract","looking for","apply","remote","project","usd","salary","rate","per hour"],"positive_signals":["contract","looking for","apply","contract","remote","looking for","project","usd","salary","rate","per hour","remote"],"negative_signals":[],"llm_analysis":{},"raw":{"comment":{"by":"mwapinska","id":49166151,"kids":[49193474],"parent":49156683,"text":"Senior Python Backend Engineer | REMOTE (EMEA/APAC)
We're looking for Python backend engineers to work on a trustless supercluster of performance-proofed GPU-enabled sandboxed docker container runners controlled by truly decentralized algorithms (not just PAXOS or RAFT).
Wow, that was a mouthful...
If you found it interesting, join Reef Technologies and tackle complex technical challenges like this on your own terms:
- Contribute from wherever you like; we are fully remote
- Set your own time commitment, as long as it's at least 30h per week
- See how we work in our handbook: github.com/reef-technologies/handbook
- Influence the way we operate through our Sociocracy 3.0 decision-making process
Salary on a B2B contract:
- 45-70 USD or 180-280 PLN per hour
- Flexible work schedule (as measured with a time tracker)
- We automatically adjust rates based on inflation twice a year
We're looking for someone with 5+ years of programming experience, including at least a year with Python (including open-source and significant personal projects).
So, what do you think? You don't even need a CV; just go to https://careers.reef.pl/?utm_source=hackernews and shoot a few quick commands to apply.
Edit: If you applied and haven't heard back, please check the spam folder of the email associated with the GitHub account you used to apply — our replies sometimes end up there.","time":1785835485,"type":"comment"},"story":{"by":"whoishiring","descendants":374,"id":49156683,"kids":[49315850,49313253,49159312,49165394,49296620,49159002,49159996,49158050,49269646,49291876,49267485,49265260,49282255,49163160,49267985,49244466,49280288,49163576,49284614,49230580,49260542,49278096,49185103,49207434,49275045,49213747,49270021,49212742,49292852,49227265,49289931,49167547,49215237,49246737,49194503,49167059,49173892,49198416,49171913,49295167,49164297,49164303,49262437,49271778,49175620,49167837,49175789,49169663,49200756,49169157,49163907,49245007,49158552,49166151,49170090,49235310,49180088,49162817,49176763,49170963,49168047,49166970,49162565,49171202,49156711,49174463,49164382,49165870,49157095,49157959,49223303,49216288,49162914,49212807,49283824,49199157,49166975,49158655,49160057,49234667,49215960,49197178,49207409,49227630,49164461,49193298,49169577,49224620,49191343,49267747,49166092,49165031,49162047,49174485,49196678,49175131,49169017,49302719,49157781,49162501,49184927,49165916,49160256,49189557,49166080,49178671,49163358,49163308,49157647,49166262,49156737,49163337,49163520,49156819,49163658,49197049,49164584,49160993,49158703,49172407,49160662,49184766,49216911,49158886,49168019,49162608,49187999,49162833,49163946,49165651,49159064,49166082,49156730,49160095,49166165,49174765,49163458,49167647,49164894,49183442,49170014,49160628,49162990,49162401,49179433,49157646,49159942,49166000,49157827,49158895,49180017,49164350,49171072,49167691,49156781,49156716,49163176,49163107,49157080,49217319,49174053,49163301,49158131,49212543,49163678,49162696,49158982,49158689,49159784,49162950,49160086,49169841,49160170,49248055,49160751,49200866,49164730,49173075,49161849,49161858,49163060,49156691,49161868,49158727,49159686,49156689,49202116,49180607,49157505,49194132,49197515,49192593,49254584,49185343,49168356,49165024,49162509,49157970,49164213,49185076,49165622,49203310,49165309,49184940,49194021,49157178,49157182,49206998,49165353,49162776,49156724,49157674,49164060,49164409,49211731,49169971,49157888,49172196,49232553,49156845,49186245,49159519,49157129,49187963,49177149,49164146,49158072,49159281,49161851,49164668,49215140,49262026,49261992,49204595,49163927,49169371,49193246,49164540,49157265,49163435,49305158,49156696,49157567,49157201,49157063,49159899,49157058,49160018,49192311,49173314,49166131,49159028,49173891,49165359,49160915,49157454,49160848,49157437,49166077,49159115,49156702,49163131],"score":236,"text":"Please state the location and include REMOTE for remote work, REMOTE (US)\nor similar if the country is restricted, and ONSITE when remote work is not an option.
Please only post if you personally are part of the hiring company—no\nrecruiting firms or job boards. One post per company. If it isn't a household name,\nexplain what your company does.
Please only post if you are actively filling a position and are committed\nto replying to applicants.
Commenters: please don't reply to job posts to complain about\nsomething. It's off topic here.
Readers: please only email if you are personally interested in the job.
Searchers: try https://nthesis.ai/public/hn-who-is-hiring, https://dheerajck.github.io/hnwhoishiring/,\nhttp://nchelluri.github.io/hnjobs/, https://hnjobs.emilburzo.com.
Don't miss this other fine thread: Who wants to be hired? https://news.ycombinator.com/item?id=49156682","time":1785769254,"title":"Ask HN: Who is hiring? (August 2026)","type":"story"}}},{"id":99,"item_id":97,"score":63,"category":"b2b_lead","summary":"hertz-ai/HARTOS","status":"new","note":"","ai_summary":"","recommended_action":"","proposal_draft":"","risk_note":"","immediate_pushed_at":null,"digest_pushed_at":null,"created_at":"2026-08-16T08:05:46.831972+00:00","updated_at":"2026-08-16T08:05:46.831972+00:00","llm_analyzed_at":null,"source_id":"github_ai_agent_repos","url":"https://github.com/hertz-ai/HARTOS","title":"hertz-ai/HARTOS","content":"An AI-native OS. Models run on your own hardware, nodes federate peer-to-peer with no broker, and the API is OpenAI-compatible. Boots, has its own Wayland compositor, and runs on 8GB. Apache 2.0. language:Python stars:52 open source github developer tool\n\nREADME: HART OS What it is? 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Please only post if you personally are part of the hiring company—no\nrecruiting firms or job boards. One post per company. If it isn't a household name,\nexplain what your company does.
Please only post if you are actively filling a position and are committed\nto replying to applicants.
Commenters: please don't reply to job posts to complain about\nsomething. It's off topic here.
Readers: please only email if you are personally interested in the job.
Searchers: try https://nthesis.ai/public/hn-who-is-hiring, https://dheerajck.github.io/hnwhoishiring/,\nhttp://nchelluri.github.io/hnjobs/, https://hnjobs.emilburzo.com.
Don't miss this other fine thread: Who wants to be hired? https://news.ycombinator.com/item?id=49156682","time":1785769254,"title":"Ask HN: Who is hiring? (August 2026)","type":"story"}}},{"id":320,"item_id":290,"score":60,"category":"product_idea","summary":"Demystifying evals for AI agents Jan 09, 2026","status":"new","note":"","ai_summary":"","recommended_action":"","proposal_draft":"","risk_note":"","immediate_pushed_at":null,"digest_pushed_at":null,"created_at":"2026-08-16T08:07:49.674191+00:00","updated_at":"2026-08-16T08:07:49.674191+00:00","llm_analyzed_at":null,"source_id":"anthropic_engineering_blog","url":"https://www.anthropic.com/engineering/demystifying-evals-for-ai-agents","title":"Demystifying evals for AI agents Jan 09, 2026","content":"Demystifying evals for AI agents Jan 09, 2026 official Anthropic engineering blog Claude developer platform coding agent tech trend\n\nDETAIL: Introduction Good evaluations help teams ship AI agents more confidently. Without them, it’s easy to get stuck in reactive loops—catching issues only in production, where fixing one failure creates others. Evals make problems and behavioral changes visible before they affect users, and their value compounds over the lifecycle of an agent. As we described in Building effective agents , agents operate over many turns: calling tools, modifying state, and adapting based on intermediate results. These same capabilities that make AI agents useful—autonomy, intelligence, and flexibility—also make them harder to evaluate. Through our internal work and with customers at the frontier of agent development, we’ve learned how to design more rigorous and useful evals for agents. Here's what's worked across a range of agent architectures and use cases in real-world deployment. The structure of an evaluation An evaluation (“eval”) is a test for an AI system: give an AI an input, then apply grading logic to its output to measure success. In this post, we focus on automated evals that can be run during development without real users. Single-turn evaluations are straightforward: a prompt, a response, and grading logic. For earlier LLMs, single-turn, non-agentic evals were the main evaluation method. As AI capabilities have advanced, multi-turn evaluations have become increasingly common. In a simple eval, an agent processes a prompt, and a grader checks if the output matches expectations. For a more complex multi-turn eval, a coding agent receives tools, a task (building an MCP server in this case), and an environment, executes an \"agent loop\" (tool calls and reasoning), and updates the environment with the implementation. Grading then uses unit tests to verify the working MCP server. 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These same capabilities that make AI agents useful—autonomy, intelligence, and flexibility—also make them harder to evaluate. Through our internal work and with customers at the frontier of agent development, we’ve learned how to design more rigorous and useful evals for agents. Here's what's worked across a range of agent architectures and use cases in real-world deployment. The structure of an evaluation An evaluation (“eval”) is a test for an AI system: give an AI an input, then apply grading logic to its output to measure success. In this post, we focus on automated evals that can be run during development without real users. Single-turn evaluations are straightforward: a prompt, a response, and grading logic. For earlier LLMs, single-turn, non-agentic evals were the main evaluation method. As AI capabilities have advanced, multi-turn evaluations have become increasingly common. In a simple eval, an agent processes a prompt, and a grader checks if the output matches expectations. 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I've been experimenting with a new approach to supervising language models that we’re calling \"agent teams.\" With agent teams, multiple Claude instances work in parallel on a shared codebase without active human intervention. This approach dramatically expands the scope of what's achievable with LLM agents. To stress test it, I tasked 16 agents with writing a Rust-based C compiler, from scratch, capable of compiling the Linux kernel. Over nearly 2,000 Claude Code sessions and $20,000 in API costs, the agent team produced a 100,000-line compiler that can build Linux 6.9 on x86, ARM, and RISC-V. The compiler is an interesting artifact on its own, but I focus here on what I learned about designing harnesses for long-running autonomous agent teams: how to write tests that keep agents on track without human oversight, how to structure work so multiple agents can make progress in parallel, and where this approach hits its ceiling. Enabling long-running Claudes Existing agent scaffolds like Claude Code require an operator to be online and available to work jointly. If you ask for a solution to a long and complex problem, the model may solve part of it, but eventually it will stop and wait for continued input—a question, a status update, or a request for clarification. To elicit sustained, autonomous progress, I built a harness that sticks Claude in a simple loop (if you’ve seen Ralph-loop, this should look familiar). When it finishes one task, it immediately picks up the next. 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This approach dramatically expands the scope of what's achievable with LLM agents. To stress test it, I tasked 16 agents with writing a Rust-based C compiler, from scratch, capable of compiling the Linux kernel. Over nearly 2,000 Claude Code sessions and $20,000 in API costs, the agent team produced a 100,000-line compiler that can build Linux 6.9 on x86, ARM, and RISC-V. The compiler is an interesting artifact on its own, but I focus here on what I learned about designing harnesses for long-running autonomous agent teams: how to write tests that keep agents on track without human oversight, how to structure work so multiple agents can make progress in parallel, and where this approach hits its ceiling. Enabling long-running Claudes Existing agent scaffolds like Claude Code require an operator to be online and available to work jointly. If you ask for a solution to a long and complex problem, the model may solve part of it, but eventually it will stop and wait for continued input—a question, a status update, or a request for clarification. To elicit sustained, autonomous progress, I built a harness that sticks Claude in a simple loop (if you’ve seen Ralph-loop, this should look familiar). When it finishes one task, it immediately picks up the next. 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Finding work worth doing is harder. A.Team is an invite-only network of senior AI engineers, ML engineers, and AI architects building production AI systems for startups, enterprises, and global companies. Since 2020, builders in the network have earned more than $200M working with companies including Lyft, Google X, HCA, Unilever, Sightful, D-ID, and others. The Work You'll join small teams solving real problems. Not demos. Not proof-of-concepts that never ship. Not another thin wrapper around an API. Recent work has included: AI-native products built from 0→1 Agent architectures and orchestration systems Enterprise AI deployments Retrieval, evaluation, and reliability infrastructure LLM-powered workflows and products Machine learning platforms operating at scale You'll work directly with founders, CTOs, and product leaders, helping shape both technical direction and execution. Why Builders Join The best opportunities rarely make it to the open market. A.Team exists to solve that. No bidding. No prospecting. No race to the bottom. Just ambitious companies, difficult problems, and experienced peers. Who Thrives Here People who have already shipped. Engineers who have deployed AI systems into production, understand the tradeoffs behind the hype, and know how to turn technical complexity into business value. What matters is a track record of building and shipping. The Details Typical rates: $120-$170/hr Remote You keep 100% of your rate Not a Fit Early-career engineers People without production AI experience Small gig seekers Developers focused primarily on simple AI wrappers or templated implementations Apply Tell us what you've built. 👉 build.a.team/apply-ai . 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Senior Independent AI Engineer / Architect
\nRemote | Americas, Europe, or Israel
\n\n
Most AI engineers can find work.
\n\n
Finding work worth doing is harder.
\n\n
A.Team is an invite-only network of senior AI engineers, ML engineers, and AI architects building production AI systems for startups, enterprises, and global companies.
\n\n
Since 2020, builders in the network have earned more than $200M working with companies including Lyft, Google X, HCA, Unilever, Sightful, D-ID, and others.
\n\n
The Work
\nYou'll join small teams solving real problems.
\n\n
Not demos.
\nNot proof-of-concepts that never ship.
\nNot another thin wrapper around an API.
\n
Recent work has included:
\n
\n
You'll work directly with founders, CTOs, and product leaders, helping shape both technical direction and execution.
\n\n
Why Builders Join
\nThe best opportunities rarely make it to the open market.
\n\n
A.Team exists to solve that.
\n\n
No bidding.
\n\n
No prospecting.
\n\n
No race to the bottom.
\n\n
Just ambitious companies, difficult problems, and experienced peers.
\n\n
Who Thrives Here
\nPeople who have already shipped.
\n\n
Engineers who have deployed AI systems into production, understand the tradeoffs behind the hype, and know how to turn technical complexity into business value.
\n\n
What matters is a track record of building and shipping.
\n\n
The Details
\n\n
\n
Not a Fit
\n\n
Apply
\nTell us what you've built.
\n\nWe'll take it from there.
\n![\"\"/](\"https://remotive.com/job/track/1919266/blank.gif?source=public_api\")
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Here’s what we’ve learned building containment for claude.ai, Claude Code, and Cowork. official Anthropic engineering blog Claude developer platform coding agent tech trend\n\nDETAIL: Twelve months ago, we'd have rejected out of hand the idea of granting Claude access sufficient to take down an internal Anthropic service. Today that level of access is routine, and Anthropic developers are more productive for it. The risk of these deployments has two components: how likely a failure is, and how much damage one could do. Progress on safeguards and model training has steadily driven down the first; the second—the theoretical blast radius—only grows as capabilities and access expand. Yet as agents become capable of doing work that once required a person or even a team, the cost of not deploying grows large enough that the risk-reward calculation tips heavily toward adoption, as long as products can be made safe. The engineering question becomes how to cap the blast radius. When bounds can be placed on the relative damage of an autonomous agent—such as through control over its environment—high-utility capabilities can motivate deployment. Claude Mythos Preview is an example of a model whose blast radius was deemed too high to ship in April 2026. However, we expect broader release of models with similar levels of capability to become appropriate as defenders harden critical systems and safeguards mature—even though some risk will always remain. Model capability is an important factor in the total risk of an agent’s deployment. There are broadly two ways to do this. The first is to supervise the agent’s behavior via a human-in-the-loop. Claude Code previously protected against agents taking unintended actions by asking users for permission at each turn. Theoretically that works, but we’ve found the approach to be fallible. Our telemetry showed users approved roughly 93% of permission prompts. 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The only way in is a relationship and a public-records request, and you cannot download a relationship. We turned that into the intelligence layer for the built world: permits, contractors, parcels, government decisions. Customers pay for it today.
One warning about the careers page: the job posts have a reverse CAPTCHA. Read access: agents only. Bring yours: https://www.shovels.ai/careers
Try the door even if you don't plan to apply, I promise it's fun!
Receipts on how we build: we published the working notes from planning our newest API, rival architectures and refuted claims included: https://shovelsai.github.io/team-notes/2026-07-07-a9e2-how-w...
Do you want to make software cheaper to build, or build the thing that stays valuable when software is cheap? :)","time":1785872367,"type":"comment"},"story":{"by":"whoishiring","descendants":374,"id":49156683,"kids":[49315850,49313253,49159312,49165394,49296620,49159002,49159996,49158050,49269646,49291876,49267485,49265260,49282255,49163160,49267985,49244466,49280288,49163576,49284614,49230580,49260542,49278096,49185103,49207434,49275045,49213747,49270021,49212742,49292852,49227265,49289931,49167547,49215237,49246737,49194503,49167059,49173892,49198416,49171913,49295167,49164297,49164303,49262437,49271778,49175620,49167837,49175789,49169663,49200756,49169157,49163907,49245007,49158552,49166151,49170090,49235310,49180088,49162817,49176763,49170963,49168047,49166970,49162565,49171202,49156711,49174463,49164382,49165870,49157095,49157959,49223303,49216288,49162914,49212807,49283824,49199157,49166975,49158655,49160057,49234667,49215960,49197178,49207409,49227630,49164461,49193298,49169577,49224620,49191343,49267747,49166092,49165031,49162047,49174485,49196678,49175131,49169017,49302719,49157781,49162501,49184927,49165916,49160256,49189557,49166080,49178671,49163358,49163308,49157647,49166262,49156737,49163337,49163520,49156819,49163658,49197049,49164584,49160993,49158703,49172407,49160662,49184766,49216911,49158886,49168019,49162608,49187999,49162833,49163946,49165651,49159064,49166082,49156730,49160095,49166165,49174765,49163458,49167647,49164894,49183442,49170014,49160628,49162990,49162401,49179433,49157646,49159942,49166000,49157827,49158895,49180017,49164350,49171072,49167691,49156781,49156716,49163176,49163107,49157080,49217319,49174053,49163301,49158131,49212543,49163678,49162696,49158982,49158689,49159784,49162950,49160086,49169841,49160170,49248055,49160751,49200866,49164730,49173075,49161849,49161858,49163060,49156691,49161868,49158727,49159686,49156689,49202116,49180607,49157505,49194132,49197515,49192593,49254584,49185343,49168356,49165024,49162509,49157970,49164213,49185076,49165622,49203310,49165309,49184940,49194021,49157178,49157182,49206998,49165353,49162776,49156724,49157674,49164060,49164409,49211731,49169971,49157888,49172196,49232553,49156845,49186245,49159519,49157129,49187963,49177149,49164146,49158072,49159281,49161851,49164668,49215140,49262026,49261992,49204595,49163927,49169371,49193246,49164540,49157265,49163435,49305158,49156696,49157567,49157201,49157063,49159899,49157058,49160018,49192311,49173314,49166131,49159028,49173891,49165359,49160915,49157454,49160848,49157437,49166077,49159115,49156702,49163131],"score":236,"text":"Please state the location and include REMOTE for remote work, REMOTE (US)\nor similar if the country is restricted, and ONSITE when remote work is not an option.
Please only post if you personally are part of the hiring company—no\nrecruiting firms or job boards. One post per company. If it isn't a household name,\nexplain what your company does.
Please only post if you are actively filling a position and are committed\nto replying to applicants.
Commenters: please don't reply to job posts to complain about\nsomething. It's off topic here.
Readers: please only email if you are personally interested in the job.
Searchers: try https://nthesis.ai/public/hn-who-is-hiring, https://dheerajck.github.io/hnwhoishiring/,\nhttp://nchelluri.github.io/hnjobs/, https://hnjobs.emilburzo.com.
Don't miss this other fine thread: Who wants to be hired? https://news.ycombinator.com/item?id=49156682","time":1785769254,"title":"Ask HN: Who is hiring? (August 2026)","type":"story"}}},{"id":388,"item_id":353,"score":57,"category":"tech_trend","summary":"daintreehq/daintree","status":"new","note":"","ai_summary":"","recommended_action":"","proposal_draft":"","risk_note":"","immediate_pushed_at":null,"digest_pushed_at":null,"created_at":"2026-08-16T08:11:10.866441+00:00","updated_at":"2026-08-16T08:11:10.866441+00:00","llm_analyzed_at":null,"source_id":"github_trending_daily","url":"https://github.com/daintreehq/daintree","title":"daintreehq/daintree","content":"A delegation environment for orchestrating AI coding agents. 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Senior role, Staff-leaning scope, clear path to Tech Lead. 2/3 of traffic is web, so performance is business-critical (Core Web Vitals, SEO, caching), and this codebase is also our launchpad for AI features (concierge chatbot, AI spa booker). Stack: Next.js 16 (App Router), React 19, TypeScript, Tailwind, shadcn/ui. Backend: Rails, Node/AWS, Postgres, MongoDB. Playwright + Datadog. You'll own: technical design (RFCs) for major rewrites, reusable App Router patterns, code quality/mentorship for a junior-leaning team, and API contracts with backend. Need: 6+ yrs production web apps, deep React/Next.js expertise, proven track record leading high-traffic rewrites with measurable results. Strong full-stack engineers open to leveling up on React also welcome. 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We're rewriting resortpass.com — starting with our highest-traffic surfaces (SRP/DLP) — and need a Senior Frontend Engineer to be the technical anchor. ~75% of pages get touched this year: international expansion (Mexico & Caribbean now, Europe next), in-housing search from Algolia, a new Hotel SaaS tool, and Spa as a major new vertical.
Senior role, Staff-leaning scope, clear path to Tech Lead. 2/3 of traffic is web, so performance is business-critical (Core Web Vitals, SEO, caching), and this codebase is also our launchpad for AI features (concierge chatbot, AI spa booker).
Stack: Next.js 16 (App Router), React 19, TypeScript, Tailwind, shadcn/ui. Backend: Rails, Node/AWS, Postgres, MongoDB. Playwright + Datadog.
You'll own: technical design (RFCs) for major rewrites, reusable App Router patterns, code quality/mentorship for a junior-leaning team, and API contracts with backend.
Need: 6+ yrs production web apps, deep React/Next.js expertise, proven track record leading high-traffic rewrites with measurable results. Strong full-stack engineers open to leveling up on React also welcome.
Visa: not standard, possible exception for exceptional candidates.\nJD + Apply: https://uctalent.io/referral/Huynh_Nhu_Bao_Nhan221138/v8QmsN...","time":1786206553,"type":"comment"},"story":{"by":"whoishiring","descendants":374,"id":49156683,"kids":[49315850,49313253,49159312,49165394,49296620,49159002,49159996,49158050,49269646,49291876,49267485,49265260,49282255,49163160,49267985,49244466,49280288,49163576,49284614,49230580,49260542,49278096,49185103,49207434,49275045,49213747,49270021,49212742,49292852,49227265,49289931,49167547,49215237,49246737,49194503,49167059,49173892,49198416,49171913,49295167,49164297,49164303,49262437,49271778,49175620,49167837,49175789,49169663,49200756,49169157,49163907,49245007,49158552,49166151,49170090,49235310,49180088,49162817,49176763,49170963,49168047,49166970,49162565,49171202,49156711,49174463,49164382,49165870,49157095,49157959,49223303,49216288,49162914,49212807,49283824,49199157,49166975,49158655,49160057,49234667,49215960,49197178,49207409,49227630,49164461,49193298,49169577,49224620,49191343,49267747,49166092,49165031,49162047,49174485,49196678,49175131,49169017,49302719,49157781,49162501,49184927,49165916,49160256,49189557,49166080,49178671,49163358,49163308,49157647,49166262,49156737,49163337,49163520,49156819,49163658,49197049,49164584,49160993,49158703,49172407,49160662,49184766,49216911,49158886,49168019,49162608,49187999,49162833,49163946,49165651,49159064,49166082,49156730,49160095,49166165,49174765,49163458,49167647,49164894,49183442,49170014,49160628,49162990,49162401,49179433,49157646,49159942,49166000,49157827,49158895,49180017,49164350,49171072,49167691,49156781,49156716,49163176,49163107,49157080,49217319,49174053,49163301,49158131,49212543,49163678,49162696,49158982,49158689,49159784,49162950,49160086,49169841,49160170,49248055,49160751,49200866,49164730,49173075,49161849,49161858,49163060,49156691,49161868,49158727,49159686,49156689,49202116,49180607,49157505,49194132,49197515,49192593,49254584,49185343,49168356,49165024,49162509,49157970,49164213,49185076,49165622,49203310,49165309,49184940,49194021,49157178,49157182,49206998,49165353,49162776,49156724,49157674,49164060,49164409,49211731,49169971,49157888,49172196,49232553,49156845,49186245,49159519,49157129,49187963,49177149,49164146,49158072,49159281,49161851,49164668,49215140,49262026,49261992,49204595,49163927,49169371,49193246,49164540,49157265,49163435,49305158,49156696,49157567,49157201,49157063,49159899,49157058,49160018,49192311,49173314,49166131,49159028,49173891,49165359,49160915,49157454,49160848,49157437,49166077,49159115,49156702,49163131],"score":236,"text":"Please state the location and include REMOTE for remote work, REMOTE (US)\nor similar if the country is restricted, and ONSITE when remote work is not an option.
Please only post if you personally are part of the hiring company—no\nrecruiting firms or job boards. One post per company. If it isn't a household name,\nexplain what your company does.
Please only post if you are actively filling a position and are committed\nto replying to applicants.
Commenters: please don't reply to job posts to complain about\nsomething. It's off topic here.
Readers: please only email if you are personally interested in the job.
Searchers: try https://nthesis.ai/public/hn-who-is-hiring, https://dheerajck.github.io/hnwhoishiring/,\nhttp://nchelluri.github.io/hnjobs/, https://hnjobs.emilburzo.com.
Don't miss this other fine thread: Who wants to be hired? https://news.ycombinator.com/item?id=49156682","time":1785769254,"title":"Ask HN: Who is hiring? 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The project compensation rate is $0.55 USD per accepted image.
The objective of this project is to collect a large and diverse dataset of current neutral selfies, head-pose captures, and historical facial images to support machine-learning research and facial recognition model training at TELUS.
\n
The focus is on capturing real-world variation across lighting, poses, expressions, accessories, environments, and aging to improve model accuracy and robustness. The collection includes:
\n\n
• Current Neutral Selfies – clean frontal selfies serving as high-quality identity references, with natural variation in appearance and surroundings.
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\n• Historical Images – older photos from participants’ galleries to capture natural aging and long-term appearance changes.
\n\n
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\n\n
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\n\n
\n
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\n![\"\"/](\"https://remotive.com/job/track/2091093/blank.gif?source=public_api\")
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- LLM Inference Optimization Engineer: Improve the inference engine powering our API service and maintain PLaMo implementations in open source projects such as vLLM. [Apply here: https://open.talentio.com/r/1/c/preferred/pages/119173]
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Please only post if you personally are part of the hiring company—no\nrecruiting firms or job boards. One post per company. If it isn't a household name,\nexplain what your company does.
Please only post if you are actively filling a position and are committed\nto replying to applicants.
Commenters: please don't reply to job posts to complain about\nsomething. It's off topic here.
Readers: please only email if you are personally interested in the job.
Searchers: try https://nthesis.ai/public/hn-who-is-hiring, https://dheerajck.github.io/hnwhoishiring/,\nhttp://nchelluri.github.io/hnjobs/, https://hnjobs.emilburzo.com.
Don't miss this other fine thread: Who wants to be hired? https://news.ycombinator.com/item?id=49156682","time":1785769254,"title":"Ask HN: Who is hiring? 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