Research Engineer - Reinforcement Learning

Highlights

• Build massive-scale synthetic data pipeline for frontier RL post-training.
• Help build the open superintelligence stack for frontier AI teams.
• Publish novel research at top venues like ICML and NeurIPS.

What they offer

• Equity incentives aligning success with growth and impact
• Flexible work arrangements with remote or in-person option in San Francisco
• Visa sponsorship and relocation assistance for international candidates
• Quarterly team off-sites, hackathons, conferences and learning opportunities

Ideal candidate

High-energy AI/ML builder passionate about open frontier AI and democratizing reasoning. You thrive scaling large models and turning cutting-edge research into real-world infrastructure.

Requirements

• Strong background in AI/ML engineering with end-to-end inference or training pipelines for large-scale models
• Deep expertise in distributed inference techniques and frameworks for optimizing performance and scalability
• Solid understanding of MLOps best practices including versioning, experiment tracking and CI/CD
• Passion for advancing state-of-the-art reasoning and democratizing AI capabilities