Applied Research - RL & Agents

Highlights

• Design next-gen AI agents tackling real workloads and reasoning-intensive tasks.
• Build open frontier models for long-horizon tasks like autonomous research.
• Shape verifiers, evals, and training services powering post-training at scale.

What they offer

• Cash compensation range of $150-300k + equity incentives
• Flexible work (San Francisco or hybrid-remote)
• Visa sponsorship & relocation support
• Professional development budget
• Team off-sites & conference attendance

Ideal candidate

ML engineer passionate about open frontier AI and practical agentic systems, eager to prototype fast, collaborate with researchers and users, and turn ambiguous real-world goals into robust deployed agents.

Requirements

• Strong background in machine learning engineering, with experience in post-training, RL, or large-scale model alignment
• Experience with agent frameworks and tooling (e.g. DSPy, LangGraph, MCP, Stagehand)
• Familiarity with distributed training/inference frameworks (e.g., vLLM, sglang, Accelerate, Ray, Torch)
• Track record of research contributions (publications, open-source contributions, benchmarks) in ML/RL
• Passion for advancing the state-of-the-art in reasoning and building practical, agentic AI systems
• Strong technical writing abilities (documentation, blogs, papers) and research taste
• Eagerness to drive collaborations with external partners and engage with the broader open-source community