Applied Research - Evals & Data Job at Prime Intellect (San Francisco)

Job Description

Prime Intellect builds the infrastructure that frontier AI labs build internally, and makes it available to everyone. Our platform, Lab, unifies environments, evaluations, sandboxes, and high-performance training into a single full-stack system for post-training at frontier scale, from RL and SFT to tool use, agent workflows, and deployment. This is a customer facing role at the intersection of cutting-edge RL/post-training methods, applied data, and agent systems. You’ll have a direct impact on shaping how advanced models are aligned, evaluated, deployed, and used in the real world.

Job Responsibility

Advancing Agent Capabilities: Designing and iterating on next-generation AI agents that tackle real workloads—workflow automation, reasoning-intensive tasks, and decision-making at scale
Building Robust Infrastructure: Developing the distributed systems, evaluation pipelines, and coordination frameworks that enable these agents to operate reliably, efficiently, and at massive scale
Bridge Between Customers & Research: Translating customer needs and insights from applied data into clear technical requirements that guide product and research priorities
Prototype in the Field: Rapidly designing and deploying agents, evals, and harnesses alongside customers to validate solutions
Customer-Facing Engineering: Work side-by-side with customers to deeply understand workflows, data sources, and bottlenecks
Post-training & Reinforcement Learning: Design and implement novel RL and post-training methods (RLHF, RLVR, GRPO, etc.) to align large models with domain-specific tasks
Agent Development & Infrastructure: Rapidly prototype and iterate on AI agents for automation, workflow orchestration, and decision-making

Requirements

Strong background in machine learning engineering, with experience in post-training, RL, or large-scale model alignment
Experience with applied data workflows and evaluation frameworks for large models or agents (e.g., SWE-Bench, HELM, EvalFlow, internal eval pipelines)
Deep expertise in distributed training/inference frameworks (e.g., vLLM, sglang, Ray, Accelerate)
Experience deploying containerized systems at scale (Docker, Kubernetes, Terraform)
Track record of research contributions (publications, open-source contributions, benchmarks) in ML/RL
Passion for advancing the state-of-the-art in reasoning, measurement, and building practical, agentic AI systems

What we offer

Competitive Compensation + equity incentives
Flexible Work (remote or San Francisco)
Visa Sponsorship & relocation support
Professional Development budget
Team Off-sites & conference attendance

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Applied Research - Evals & Data

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