Prime Intellect is building the open superintelligence stack, aiming to enable anyone to create, train, and deploy frontier agentic models by aggregating and orchestrating global compute into a single control plane and pairing it with the full RL post-training stack.
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.
Responsibilities
- Designing and iterating on next-generation AI agents that tackle real workloads—workflow automation, reasoning-intensive tasks, and decision-making at scale.
- Developing the distributed systems, evaluation pipelines, and coordination frameworks that enable these agents to operate reliably, efficiently, and at massive scale.
- Building data capture, processing, and versioning workflows for feedback, model traces, and reward signals.
- 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.
- Architect and maintain distributed training and inference pipelines, ensuring scalability and cost efficiency.
Other
- This is a customer facing role at the intersection of cutting-edge RL/post-training methods, applied data, and agent systems.
- Translating customer needs and insights from applied data into clear technical requirements that guide product and research priorities.
- 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.
