Reinforcement Learning Engineer
- Location
- New York City, NY
- Listed
- 2 days ago
What You’ll Do
As 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.
- Own and ship an RL-driven trading agent using real capital to increase trading volume and user participation in a memecoin ecosystem
- Design reward functions and policies aligned with product goals while enforcing strict downside risk constraints
- Build evaluation and validation frameworks (simulation, offline analysis) to minimize reliance on live sequential testing
- Safely transition an existing heuristic-based production system toward learning-based approaches
- Take end-to-end ownership and technical leadership as the sole RL expert, from data and modeling through deployment, monitoring, and safeguards
Who You Are
- You 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
- Have personally designed and enforced hard risk limits (capital caps, loss bounds, circuit breakers) in a live system, not just talked about “risk-aware objectives.
- Have built a policy evaluation loop from scratch (simulators, replay, counterfactuals, shadow deployments) before trusting live rollout.
- Can make and defend uncomfortable tradeoffs (e.g. heuristic > RL, bandit > deep RL) based on empirical results instead of ideology
- Have 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.”
What It's Like to Work Here
- We work in person
- Hours can be long and unconventional
- The pace is intense
- Expectations are high, and impact is immediate
- Working at Bags.fm is not for everyone
Who We Are
Bags.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.
Why Join Us?
- Unmatched ownership and autonomy
- Exposure to systems operating at the edge of crypto scale
- The ability to ship fast and see real-world impact immediately
If you’re motivated by responsibility, speed, and building products used by massive audiences, you’ll feel at home here.
From Bagsfm's careers page, first listed on Rolev.ai on November 7, 2026. Rolev.ai is not the employer.
How Rolev.ai helps with this application
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Common questions
Where is the Reinforcement Learning Engineer job at Bagsfm located?
This Reinforcement Learning Engineer position at Bagsfm is located in New York City, NY. Always check Bagsfm's application page for specific state eligibility or relocation policies.
How do I apply for the Reinforcement Learning Engineer position at Bagsfm?
This listing links directly to Bagsfm's Ashby application page. You can apply directly on Bagsfm's site, or use Rolev.ai to check your resume fit, generate a zero-hallucination tailored resume and cover letter for this posting, and autofill the Ashby application form in 1 click with the Rolev.ai Chrome extension. You always review the form and press Submit yourself.