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 providing the necessary RL post-training stack.
Requirements
- Strong background in machine learning engineering, with experience in post-training, RL, or large-scale model alignment.
- 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 and coordination frameworks that enable these agents to operate reliably, efficiently, and at massive scale.
- Rapidly design and deploy agents, evals, and harnesses alongside customers to validate solutions.
- 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.
- Prototype multi-agent and memory-augmented systems to expand capabilities for customer-facing solutions.
- Architect and maintain distributed training/inference pipelines, ensuring scalability and cost efficiency.
Other
- This is a customer facing role
- Translate customer needs into clear technical requirements that guide product and research priorities.
- Work side-by-side with customers to deeply understand workflows and bottlenecks.
- Flexible Work (remote or San Francisco)
- Visa Sponsorship & relocation support
