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Bags

Reinforcement Learning Engineer

  • Seen live on Bags' site yesterday
  • Mid level
Pay
Not stated
Location
New York
Open for
6+ days
First seen
Oct 29, 2026
Apply on Bags' site (opens in a new tab)

Posting record

No flags

Open 6+ days

The posting's record, oldest first
  1. Bags' site dates the posting
  2. Ghostless first sees this listing
  3. Last checked: still up
How we know

It hasn't been reposted, listed twice, or left up unusually long, and the posting doesn't say it's a talent pool.

Ghostless records the day it first sees a posting, so a repost can't reset its age. This one was already up when Ghostless started tracking Bags, so its true age is longer.

Last checked Nov 3, 2026.

How well do you match?

See how much of this posting your resume shows, and what it's missing.

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What the posting discloses

Not stated: Pay, real vacancy, AI screening.

Why these three

Ontario requires postings from employers with 25 or more staff to state the pay, whether the vacancy exists, and whether AI screens applicants.

Some employers add pay only on their own site. Check the original posting before you rule it out.

Skills mentioned

About the role

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.

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

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.

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