Applied Researcher - Agentic RL

Prime Intellect; Oregon; Hybrid; USD 150.000 - 300.000; Vollzeit

Benefits dieser Stelle

Flexible work locations (remote or in‑

Visa sponsorship & relocation support

Professional development budget

Team off-sites & conference attendance

Zusammenfassung

Prime Intellect, Inc. is building an open frontier AI platform that unifies compute, environments, evaluations, sandboxes, and deployment for frontier-scale models.

This applied research role focuses on RL and ML systems that enable agents to reason over long horizons, use tools, coordinate with other agents, and learn from experience. You will develop reusable environments and evaluation methods, work with customers and researchers, and contribute to open-source systems and papers that transfer

Qualifikationen

• Hands-on reinforcement learning training experience.
• Experience building scalable ML systems for research and production.
• Experience with agent-environment simulations and long-horizon reasoning.
• Familiarity with evaluation pipelines and ablation studies.
• Experience with post-training/inference infrastructure in research settings.
• Evidence of interdisciplinary research across methods and systems.
• Proficiency in Python and artifacts that others can run and inspect.

Aufgaben

• Develop RL and post-training methods for tool-using, multi-agent, memory-augmented, long-horizon systems.
• Create reusable environments, harnesses, rewards, and evaluations.
• Run experiments linking algorithm choices to training and inference behavior.
• Open-source systems and papers derived from application research and external users.
• Collaborate with customers, users, and partners while maintaining a research agenda.

Kenntnisse

Hands-on RL training

Scalable ML systems

Agent environments

Evaluation

Inference infrastructure

Research across methods

Strong Python

Jobbeschreibung

Own Your Intelligence

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, LangChain, Browserbase, Cloudflare, Sierra, Databricks, Airbnb, OpenRouter, Standard Intelligence, Fleet, Core Auto, and more.

Role Impact

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, and 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

Applied Research at PrimeWe 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.

Representative early problems

• Build agentic judges and credit-assignment methods that inspect complete trajectories, use tools, and assign useful learning signal 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 workWe 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 and Perks

• 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

If you're excited about the opportunity to build the foundation for the future of decentralized AI and create a platform that empowers developers and researchers to push the boundaries of what's possible, we'd love to hear from you.