Research Scientist / Research Engineer – LLM Post-Training & Reinforcement Learning

Location: San Francisco Bay Area
Industry: Frontier AI / Foundation Models
Compensation: Highly competitive
Seniority: Multiple levels

The Opportunity

We're working with an ambitious frontier AI research company in the Bay Area, building next-generation foundation models and exploring new approaches to advancing model intelligence, reasoning and computational efficiency.

As part of their continued growth, they're looking to hire talented Research Scientists and Research Engineers specialising in LLM Post-Training and Reinforcement Learning.

You'll have the opportunity to tackle complex, open-ended research challenges, contribute to the development of novel training methodologies and play a key role in shaping the capabilities of future foundation models.

What You'll Be Working On

The research spans several areas of modern foundation model development, including:

  • Post-Training & Reinforcement Learning: Developing and improving RL-based training approaches, including PPO, GRPO, RLHF, preference optimisation and reward modelling.

  • Large-Scale RL Systems: Building and optimising distributed training infrastructure, asynchronous training pipelines and high-throughput inference and rollout systems.

  • Reasoning & Agentic Capabilities: Exploring methods to improve complex reasoning, multi-step decision-making, tool use and autonomous agent behaviour.

  • Evaluations & Reward Design: Creating robust evaluation methodologies, synthetic environments and verifiable reward mechanisms to drive measurable improvements in model performance.

  • Research & Experimentation: Taking ownership of research initiatives, from identifying promising ideas and designing experiments through to training, analysing results and implementing successful approaches.

What We're Looking For

We're interested in researchers and engineers who combine strong technical foundations with the ability to independently investigate and solve challenging research problems.

Ideally, you'll bring:

  • Practical experience training, fine-tuning or post-training large language models.

  • Strong knowledge of reinforcement learning algorithms, particularly PPO, GRPO, RLHF and reward modelling.

  • Experience with distributed training, large-scale GPU infrastructure or optimising RL training and inference workloads.

  • Proficiency in Python, PyTorch and modern deep learning frameworks.

  • Experience developing evaluations, designing experiments and interpreting model behaviour.

  • A track record of meaningful contributions to AI research, whether through publications, open-source projects or production-scale model development.

Experience with frameworks such as verl, OpenRLHF, TRL, NeMo RL, DeepSpeed or similar distributed training technologies would be particularly relevant.

Why This Opportunity?

This is a chance to join a research-driven organisation working at the forefront of foundation model development, where new ideas can directly influence the models being built.

You'll work alongside highly capable researchers, have significant ownership over your research direction and contribute to solving fundamental challenges in AI.

The team is expanding across multiple levels and welcomes both research-focused scientists and hands-on research engineers with experience developing and scaling advanced AI systems.

Interested in pushing the boundaries of LLM post-training and reinforcement learning?

Apply directly to us with your resume or get in touch for a confidential conversation to learn more about the company, research and opportunities available.