{"id":3275356,"url":"https://alion.io/job/bags-reinforcement-learning-engineer","title":"Reinforcement Learning Engineer","company":{"id":1965675,"name":"Bags","domain":"bags.fm","url":"https://alion.io/company/bags-fm","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["New York, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":158000,"max_usd":353000,"period":"year","method":"role_country_seniority_unknown","sample_n":4425},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Reinforcement Learning","optional":false}],"status":"live","first_seen_at":"2026-08-18T02:21:48Z","employer_posted_date":"2026-08-18","last_verified_at":"2026-11-04T00:25:00Z","board_verified":true,"closed_at":null,"days_open":77,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":77},"description":"Who We Are\nBags.fm is the development company that builds and operates the entire technology stack behind bags.fm, the leading launchpad for new projects and ideas. The systems are low latency, high throughput, live under constant load, and break if you get them wrong.\nWhat You’ll Do\nAs our Reinforcement Learning Engineer, you will own a production trading system that directly deploys real capital. This is not a research role - it’s about building learning systems that are robust, measurable, and safe under real-world constraints.\nOwn and ship an RL-driven trading agent using real capital to increase trading volume and user participation in a memecoin ecosystem\n\nDesign reward functions and policies aligned with product goals while enforcing strict downside risk constraints\n\nBuild evaluation and validation frameworks (simulation, offline analysis) to minimize reliance on live sequential testing\n\nSafely transition an existing heuristic-based production system toward learning-based approaches\n\nTake end-to-end ownership and technical leadership as the sole RL expert, from data and modeling through deployment, monitoring, and safeguards\n\nWho You Are:\nYou have previously put an autonomous learning system into production that directly controlled capital, pricing, traffic, or resources and can explain what broke and how they fixed it\n\nHave personally designed and enforced hard risk limits (capital caps, loss bounds, circuit breakers) in a live system, not just talked about “risk-aware objectives.\n\nHave built a policy evaluation loop from scratch (simulators, replay, counterfactuals, shadow deployments) before trusting live rollout.\n\nCan make and defend uncomfortable tradeoffs (e.g. heuristic > RL, bandit > deep RL) based on empirical results instead of ideology\n\nHave operated as the single owner of a complex ML system in a small team, with no safety net of research orgs, infra teams, or “ML platforms.”\n\nWhat it's like to work here\nWe work in person\n\nHours can be long and unconventional\n\nThe pace is intense\n\nExpectations are high, and impact is immediate\n\nWorking at Bags.fm is not for everyone\n\nWhy Join Us?\nUnmatched ownership and autonomy\n\nExposure to systems operating at the edge of crypto scale\n\nThe ability to ship fast and see real-world impact immediately\n\nIf you’re motivated by responsibility, speed, and building products 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