Build reinforcement learning systems from the ground up, including environments, reward functions, training pipelines, evaluation frameworks, and production systems. Train and fine-tune models using RL methods, scale training across GPU clusters, conduct experiments, and guide technical direction. The role also contributes to engineering culture and future hiring while working closely with company founders in an on-site early-stage environment.
Founding RL Engineer
San Francisco, CA, on-site · Full-time
The build: Our client is hiring a Founding Reinforcement
Learning Engineer to build the core RL systems from the ground up. You'll work
directly with the founders, own the full loop from environment design to
training to evaluation, and help shape the technical direction of the company.
What you'll do
- Design
and build RL environments, reward functions, and training pipelines.
- Train
and fine-tune models with RL methods (PPO, GRPO, DPO, RLHF/RLAIF and
similar).
- Build
evaluation frameworks to measure model and agent performance.
- Run
experiments fast, read results, and decide what to try next.
- Scale
training on GPU clusters and keep pipelines reliable.
- Turn
research ideas into production systems.
- Help
set engineering culture and hire the next engineers.
Requirements
What we're looking for
- 2+
years of hands-on experience in reinforcement learning or ML engineering.
- Strong
Python skills and deep experience with PyTorch (or JAX).
- Hands-on
work training models with RL (policy gradient methods, reward modeling,
RLHF or similar).
- Experience
with LLM post-training or agent training is a big plus.
- Comfortable
with distributed training and GPU infrastructure.
- Builder
mindset: you ship fast and are happy in a small, early team.
- Degree
in CS, Math, Physics, or a related field (MS/PhD a plus).
Nice to have
- Publications
or open-source work in RL.
- Experience
at an AI lab or an early-stage AI startup.
- Experience
with simulation or environment frameworks (Gymnasium, Ray RLlib, Isaac,
MuJoCo).
- Experience
with training libraries like TRL, verl, or OpenRLHF.
Logistics: San Francisco, CA, on-site.
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