Research Engineer

Reinforcement Learning Engineer | San Francisco

I’m currently supporting a fast-growing AI company working at the intersection of frontier AI, healthcare and drug discovery, and they’re looking for an RL Engineer to join their team in San Francisco.

This is a highly hands-on role focused on building reinforcement learning environments and post-training systems for healthcare AI. You’ll work across the full RL lifecycle, designing environments, rewards and evaluation systems, running experiments, training models and agents, analysing failures and continually improving the data and feedback signals that models learn from.

• 2+ years of experience in reinforcement learning, post-training, agents or related ML systems
• Experience with areas such as RL, RLHF/RLAIF, RLVR, reward modelling, agent environments or LLM post-training
• Strong Python and ML engineering skills
• Experience building experiments, evaluation frameworks and training pipelines
• Strong understanding of data quality, dataset design and model evaluation
• Ability to work with large ML codebases and debug training runs, data pipelines and model behaviour
• A strong research mindset combined with the ability to move quickly from concept to working system
• ICML, ICLR or NeurIPS publications are highly desirable

What matters most is strong RL/ML expertise, excellent technical judgment and an interest in applying frontier AI techniques to healthcare and biomedical problems.

If you're an RL/ML engineer excited by the opportunity to work on challenging problems with real-world impact, I'd be interested in hearing from you.