[Hiring] Applied Research - Agentic RL @Prime Intellect

Role Description

You will work on reinforcement learning for agents that reason over long horizons, use tools, coordinate with other agents, recover from failure, and improve from experience. This is Prime's broad, cross-domain Applied Research role, focused on RL and ML systems. Your work should hold up across several application domains:

• Algorithms
• Environments
• Harnesses
• Reward and credit-assignment methods
• Evaluation
• Scalable experimental systems

You will work regularly with application teams, partners, and open-source users while remaining focused on methods and systems that transfer across domains.

Responsibilities

• 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.

Recent Prime work includes:

• Verifiers v1
• An algorithms layer for prime-rl
• Multi-agent training
• Prime Agent
• RL systems tested at trillion-parameter scale

You will extend this line of work and pressure-test it against live application programs.

Representative early problems include:

• Build agentic judges and credit-assignment methods that inspect complete trajectories, use tools, and assign useful learning signals beyond one final score.
• Make multi-agent and long-horizon environments train reliably at scale while preserving debuggable traces and comparable baselines.
• Detect when apparent gains come from reward hacking, harness behavior, data leakage, or infrastructure artifacts, then redesign the loop so the result survives.

What you will own:

• RL and post-training methods for tool-using, multi-agent, memory-augmented, and long-horizon systems.
• Reusable environments, harnesses, rewards, and evaluations.
• Experiments that connect algorithm choices to training and inference behavior.
• Open-source systems and papers built from problems surfaced by application researchers and external users.

Requirements

• Relevant experience: hands-on RL training, scalable ML systems, agent environments, evaluation, inference/training infrastructure, or research crossing methods and systems.
• 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.

Benefits

• Cash Compensation Range of $150-300k + equity incentives
• Flexible Work (San Francisco, New York, London, or remote)
• Visa Sponsorship & relocation support
• Professional Development budget
• Team Off-sites & conference attendance