Train, deploy, and continuously improve your own models on an integrated compute, training, inference, and sandbox stack. Backed by Menlo.
About the role
Quantitative finance is a demanding setting for agentic reinforcement learning: observations are noisy and non-stationary, actions have delayed and path-dependent consequences, naive objectives invite exploitation, and a convincing historical result can disappear under costs, constraints, or a new regime. You will build environments, evaluations, agents, and post-training methods for quantitative research and systematic decision-making. We have active programs with quantitative funds and other finance teams. Found on 1752vc Careers, the job board for startup and VC roles.
What they're looking for
- We are looking first for depth in RL, agents, or post-training systems and the ability to become fluent in quantitative research
- Prior work in systematic trading, market microstructure, time-series modeling, econometrics, or financial simulation is useful but not required
- Hands-on experience building and debugging model-training, post-training, agent, evaluation, or closely adjacent experimental loops
- Strong experimental judgment: meaningful baselines, careful trace inspection, clear failure attribution, and a willingness to change direction when the evidence changes
- Strong Python and the ability to build artifacts that other researchers and engineers can run, inspect, and extend
- Comfort owning ambiguous problems and keeping partners and teammates current as you resolve them
More about this role
Prime Intellect is building the open superintelligence stack: the infrastructure frontier AI labs build internally, made available to every ambitious AI team.
Our platform, Lab, unifies compute, environments, evaluations, secure sandboxes, high-performance training, and deployment into one full-stack system for post-training at frontier scale - from SFT and RL to tool use, agent workflows, and continuously improving production models. We are building open frontier AI: open-source models trained end to end for long-horizon tasks like autonomous research, and the full-stack platform our own research team uses to build them. The next generation of AI companies, enterprises, and research teams do not just need more GPUs. They need the ability to turn their own workflows, tools, data, and feedback loops into superintelligence they own.
Prime Intellect has raised $150M in total funding from Founders Fund, Radical Ventures, NVIDIA, and exceptional AI, infrastructure, and enterprise operators — including Andrej Karpathy, Dwarkesh Patel, and leaders and founders from Ramp, Perplexity, Harvey, Mercor, Zapier, Datadog, Cognition, OpenAI, Thinking Machines, Together AI, SemiAnalysis,...
Browse similar: AI jobs · AI startup jobs · Startup jobs · San Francisco Bay Area