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About the Role

Prime Intellect is building the open superintelligence stack, providing infrastructure for ambitious AI teams. Our platform 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 our research team uses to build them. We seek individuals passionate about building at the intersection of frontier research, real infrastructure, and go-to-market for a nascent category.

Responsibilities

Advancing Agent Capabilities: Designing and iterating on next-generation AI agents for workflow automation, reasoning-intensive tasks, and decision-making at scale.
Building Robust Infrastructure: Developing systems and frameworks for reliable, efficient, and massive-scale agent operation.
Bridge Between Applications & Research: Translating ambiguous objectives into clear technical requirements for product and research priorities.
Prototype in the Field: Rapidly designing and deploying agents, evals, and harnesses for real-world tasks to validate solutions.
Shape the direction and feature set for verifiers, the Environments Hub, training services, and other research platform offerings.
Build high-quality examples, reference implementations, and “recipes” to simplify stack extension.
Prototype agents and eval harnesses tailored to real-world use cases and external systems.
Pair with technical end-users to design environments, evals, and verifiers reflecting real workloads.
Design and implement novel RL and post-training methods (RLHF, RLVR, GRPO, etc.) to align large models with domain-specific tasks.
Build evaluations and harnesses to measure reasoning, robustness, and agentic behavior in real-world workflows.
Prototype multi-agent and memory-augmented systems to expand capabilities for downstream applications.
Experiment with post-training recipes to optimize downstream performance.
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/inference pipelines, ensuring scalability and cost efficiency.
Develop observability and monitoring (Prometheus, Grafana, tracing) for production deployment reliability and performance.

Requirements

Strong background in machine learning engineering, with experience in post-training, RL, or large-scale model alignment.
Experience with agent frameworks and tooling (e.g. DSPy, LangGraph, MCP, Stagehand).
Familiarity with distributed training/inference frameworks (e.g., vLLM, sglang, Accelerate, Ray, Torch).
Track record of research contributions (publications, open-source contributions, benchmarks) in ML/RL.
Passion for advancing the state-of-the-art in reasoning and building practical, agentic AI systems.
Strong technical writing abilities (documentation, blogs, papers) and research taste.
Eagerness to drive collaborations with external partners and engage with the broader open-source community.
Experience with web programming (React, TypeScript, Next.js).
Experience running LLM evaluations and/or synthetic data generation.
Experience deploying containerized systems at scale (Docker, Kubernetes, Terraform).

Skills

Post-training
RL
Large-scale model alignment
Agent frameworks
DSPy
LangGraph
MCP
Stagehand
Distributed training
Inference frameworks
vLLM
sglang
Accelerate
Ray
Torch
ML
RL
Technical writing
Web programming
React
TypeScript
Next.js
LLM evaluations
Synthetic data generation
Containerized systems
Docker
Kubernetes
Terraform
Observability
Monitoring
Prometheus
Grafana
Tracing

Location

San Francisco
Hybrid-remote

Work Type

Flexible Work

Salary/Compensations

$150-300k

Benefits

Equity incentives
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.