Research Engineer - Reinforcement Learning
Conducts frontier research and builds scalable synthetic-data and distributed reinforcement-learning infrastructure for large AI models. Requires strong AI/ML engineering experience, distributed inference expertise with tools such as vLLM or SGLang, and MLOps knowledge.
About the job
Responsibilities
- Lead and participate in novel research for large-scale synthetic data generation pipelines and orchestration solutions.
- Optimize AI inference workloads for performance, cost, and resource utilization using compute and memory optimization techniques.
- Contribute to open-source libraries and frameworks for synthetic data generation and distributed reinforcement learning.
- Publish research in top-tier AI conferences such as ICML and NeurIPS.
- Translate technical project outcomes into accessible technical blogs for customers and developers.
- Track advances in AI/ML infrastructure, tools, and synthetic data generation research, identifying opportunities to improve platform capabilities and user experience.
Requirements
- Strong AI/ML engineering background and extensive 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, including vLLM and SGLang.
- Solid understanding of MLOps practices, including model versioning, experiment tracking, and CI/CD pipelines.
- Passion for advancing reasoning capabilities and broadening access to AI systems.
Compensation and Benefits
- Cash compensation of $150,000–$350,000, including equity incentives.
- Flexible work arrangements with remote or in-person work options in San Francisco.
- Visa sponsorship and relocation assistance for international candidates.
- Quarterly team off-sites, hackathons, conferences, and learning opportunities.
Skills
Ai/Ml Engineering, Synthetic Data, Distributed Inference, vLLM, Sglang, MLOps, Model Versioning, Experiment Tracking, CI/CD, Reinforcement Learning, Large-Scale Ai Models, Compute Optimization, Memory Optimization, Python, Open-Source Frameworks
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