Prime Intellect - Applied Research
Requirements
• Relevant experience: RL/post-training, private ML systems, high-assurance evaluation, agent safety, regulated-domain ML, public-sector technical work, or deployment under strict data and infrastructure constraints. • You should want to build and evaluate these systems directly. • 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. • Evidence of exceptional work • We value publications, open-source systems, benchmarks, and shipped products as evidence of exceptional work. Show us something difficult you built, what failed, and how the evidence changed your approach.
Responsibilities
• Applied Research at Prime • We turn a workflow into tasks, environments, evaluations, and reward signals, run post-training experiments against real baselines, and deploy what works. When a result looks good, we find out whether the gain came from the model or from the data, harness, inference, or infrastructure. The reusable parts become shared research, open-source systems, and product capabilities. • Every Applied Research role is externally facing. Most researchers work directly with customers, users, or research partners while maintaining a substantive research agenda. • Role scope • The work happens inside partner constraints, sometimes on their infrastructure and under their access rules. • Representative early problems • Build a private improvement loop that turns sensitive traces into environments, training signal, evaluations, and deployable model updates without crossing the operator's data boundary. • Train a tool-using domain agent whose actions are permissioned, traceable, recoverable, and evaluated against real procedures rather than generic benchmarks. • Develop high-assurance evaluations for reliability, calibration, robustness, human escalation, and compliance with domain constraints under adversarial or changing conditions. • Improvement loops that run end to end inside the operator's boundary, from trace selection to deployed update. • Agents with explicit permissions, evidence trails, bounded tools, and human checkpoints. • Evaluations that test reliability, calibration, recovery, and compliance with real procedures. • Experiments on how privacy, access, and infrastructure constraints change learning, with methods that transfer across high-assurance settings.
Benefits
• Flexible Work (San Francisco, New York, London, or remote) • Visa Sponsorship & relocation support • Professional Development budget • Team Off-sites & conference attendance
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