Research Engineer - Reinforcement Learning
Prime Intellect2 months ago
Responsibilities
- Lead and participate in research to build large-scale synthetic data generation and orchestration pipelines.
- Optimize the performance, cost, and resource utilization of AI inference workloads using compute and memory optimization techniques.
- Develop open-source libraries and frameworks for synthetic data generation and distributed reinforcement learning.
- Publish research in top-tier AI conferences including ICML and NeurIPS.
- Explain technical project outcomes through accessible technical blogs for customers and developers.
- Track advances in AI/ML infrastructure, tools, and synthetic data generation research to improve the platform.
Requirements
- Extensive AI/ML engineering experience designing and implementing end-to-end pipelines for inference or training of large-scale AI models.
- Deep expertise in distributed inference techniques and frameworks such as vLLM and SGLang.
- Solid understanding of MLOps practices, including model versioning, experiment tracking, and CI/CD pipelines.
- Passion for advancing reasoning capabilities and expanding access to AI technologies.
- Candidates unfamiliar with the listed resources may still apply if they believe they can contribute and are willing to learn.
Benefits
- Flexible work arrangements with remote work or in-person work at the San Francisco offices.
- Visa sponsorship and relocation assistance for international candidates.
- Quarterly team off-sites, hackathons, conferences, and learning opportunities.
- Opportunity to work with a mission-driven AI and infrastructure team.
- Cash compensation range of $150-350k, including equity incentives.
Categories
AI ResearchML Engineering