Research Engineer - Reinforcement Learning & Distributed AI

Prime Intellect; San Francisco, Northern (CA, KY); Hybrid; USD 150,000 - 350,000; Full time

Benefits offered by this job

Visa sponsorship

Relocation assistance

Remote work option

Office in San Francisco

Equity incentives

Team off-sites & conferences

Job summary

Prime Intellect is building the open superintelligence stack and seeking an AI/ML engineering lead to advance synthetic data generation at scale. You will design end-to-end pipelines, optimize inference workloads, and contribute to open-source libraries and RL frameworks.

You'll publish in ICML/NeurIPS, distill complex results for customers and developers, and stay at the forefront of AI infrastructure. Flexible remote or SF office with sponsorship for international candidates.

Qualifications

• Strong background in AI/ML engineering with end-to-end pipelines for large-scale models.
• Deep expertise in distributed inference techniques and frameworks.
• Solid understanding of MLOps practices including CI/CD and experiment tracking.
• Passion for advancing AI and democratizing access for researchers and developers.
• Willing to engage with cutting-edge resources and join a mission-driven team.

Responsibilities

• Lead and participate in novel research to build a massive-scale synthetic data generation pipeline and orchestration solution.
• Optimize performance, cost, and resource utilization of AI inference workloads using recent advances.
• Contribute to the development of open-source libraries and frameworks for synthetic data generation and distributed RL.
• Publish research in top-tier AI conferences such as ICML and NeurIPS.
• Distill technical outcomes in layman blogs for customers and developers.
• Stay up-to-date with AI/ML infrastructure advances and identify platform improvements.

Skills

AI/ML engineering

End-to-end pipelines

Distributed inference

Tools

vllm

sglang

Job description

Prime Intellect is building the open superintelligence stack and seeking an AI/ML engineering lead to advance synthetic data generation at scale. You will design end-to-end pipelines, optimize inference workloads, and contribute to open-source libraries and RL frameworks.

You'll publish in ICML/NeurIPS, distill complex results for customers and developers, and stay at the forefront of AI infrastructure. Flexible remote or SF office with sponsorship for international candidates.