Applied Research - Evals & Data

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. We are looking for people who want to build at the intersection of frontier research, real infrastructure, and go-to-market for a category that does not fully exist yet.

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 by:

• Work side-by-side with customers to deeply understand workflows, data sources, and bottlenecks.

• Prototype agents, data pipelines, and eval harnesses tailored to real use cases, then hand off hardened systems to core teams.

• Translate customer insights and evaluation results into roadmap and research direction.

• 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.

• Design and implement novel RL and post-training methods (RLHF, RLVR, GRPO, etc.) to align large models with domain-specific tasks.

• Build evaluation harnesses and verifiers to measure reasoning, robustness, and agentic behavior in real-world workflows.

• Integrate applied data collection and analytics into the post-training process to surface regressions, emergent skills, and alignment opportunities.

• Prototype multi-agent and memory-augmented systems to expand capabilities for customer-facing solutions.

• Rapidly prototype and iterate on AI agents for automation, workflow orchestration, and decision-making.

• Extend and integrate with agent frameworks to support evolving feature requests and performance requirements.

• Architect and maintain distributed training and inference pipelines, ensuring scalability and cost efficiency.

• Develop observability and monitoring (Prometheus, Grafana, tracing) to ensure reliability and performance in production deployments.

You’ll join a mission-driven team working at the frontier of open, superintelligence infra. In this role, you’ll have the opportunity to:

• Shape the evolution of agent-driven, data-informed solutions—from research breakthroughs to production systems used by real customers.

• Collaborate with leading researchers, engineers, and partners pushing the boundaries of RL, evaluation, and post-training.

• Grow with a fast-moving organization where your contributions directly influence both the technical direction and the broader AI ecosystem.

If you’re excited to move fast, build boldly, and help define how agentic AI is developed and deployed, we’d love to hear from you.

Ready to build the open superintelligence infrastructure of tomorrow?
Apply now to help us make powerful, open AGI accessible to everyone.