Applied Researcher

Proximal is building the research systems needed to identify what models can't yet do, build the tasks required to teach them, measure whether those capabilities improve, and continuously produce the data that frontier models need. We work with frontier AI labs to provide the data and evaluations behind their most capable models.

Our early team has built coding agents and RL infrastructure at companies like Cursor and Prime Intellect, worked at firms like Jane Street, and founded companies that raised millions.

We're growing extremely fast and are backed by top-tier funds including General Catalyst, alongside angel investors from OpenAI, Anthropic, xAI, Meta Superintelligence, Google Deep Mind, and Thinking Machines.

Applied researchers work closely with our customers to help them build evals, identify model weaknesses and curate post-training data to fix these weaknesses. This role is perfect for researchers and engineers that want to be more commercial, lead projects and act like a founder within Proximal.

• Experiment with new training data to improve a targeted model capability and post-train models to demonstrate improvements
• Analyze the performance of frontier models on internal tasks to identify weaknesses and communicate them to customers
• Own a project end-to-end by specifying requirements with customers and coordinating internal teams to ensure that these requirements are met

• Strong technical and engineering abilities; we find good engineering judgment matters far more than formal research experience
• Creative problem solver with strong experimental instincts, you thrive on ambiguous, open-ended problems
• Strong research judgment; you can identify promising directions, design rigorous experiments, and draw useful conclusions from messy results
• Self-directed and relentless, you take ownership of problems end-to-end without heavy process or oversight
• Execution with excellence, even at high speed. We don't think that shipping fast has to come at the cost of quality and reliability.