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About the Role

Prime Intellect is building the open superintelligence stack, providing infrastructure for AI labs. Our platform, Lab, unifies compute, environments, evaluations, secure sandboxes, high-performance training, and deployment for post-training at frontier scale. We are developing open frontier AI, including open-source models for long-horizon tasks and the platform used by our research team. We seek individuals passionate about building at the intersection of frontier research, real infrastructure, and go-to-market for a nascent category.

Responsibilities

Advance agent capabilities by designing and iterating on next-generation AI agents for workload automation, reasoning, and decision-making.
Develop robust infrastructure, including distributed systems, evaluation pipelines, and coordination frameworks for reliable and scalable agent operation.
Build data capture, processing, and versioning workflows for feedback, model traces, and reward signals.
Act as a bridge between customers and research by translating customer needs into technical requirements and collaborating with RL and eval teams.
Prototype and deploy agents, evaluations, and harnesses alongside customers to validate solutions and iterate on model performance.
Work with customers to understand workflows, data sources, and bottlenecks.
Prototype agents, data pipelines, and eval harnesses tailored to real use cases.
Translate customer insights and evaluation results into roadmap and research direction.
Design and implement novel RL and post-training methods (RLHF, RLVR, GRPO, etc.) to align large models.
Build evaluation harnesses and verifiers to measure reasoning, robustness, and agentic behavior.
Integrate applied data collection and analytics into the post-training process.
Prototype multi-agent and memory-augmented systems.
Rapidly prototype and iterate on AI agents for automation, workflow orchestration, and decision-making.
Extend and integrate with agent frameworks.
Architect and maintain distributed training and inference pipelines.
Develop observability and monitoring (Prometheus, Grafana, tracing) for production deployments.

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.

Skills

Machine Learning Engineering
Post-training
Reinforcement Learning (RL)
Large-scale model alignment
Applied data workflows
Evaluation frameworks
Distributed training/inference frameworks
Containerized systems deployment
Docker
Kubernetes
Terraform
ML/RL research
Reasoning
Measurement
Agentic AI systems
RLHF
RLVR
GRPO
vLLM
sglang
Ray
Accelerate
Prometheus
Grafana
Tracing
Agent frameworks

Location

Remote
San Francisco

Work Type

Remote
Onsite

Salary/Compensations

$150-300k

Benefits

Equity incentives
Flexible Work
Visa Sponsorship
Relocation support
Professional Development budget
Team Off-sites
Conference attendance

About the Company

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.