Member of Technical Staff - ML Infrastructure Engineer, RL Post-training

Highlights

• Build scalable infrastructure powering post-training on in-house RL environments.
• Shape how frontier research runs and push self-directed learning further.
• Partner directly with researchers to accelerate reproducible experimentation.

What they offer

• Competitive cash and equity compensation (>90th percentile)
• Ownership and autonomy in a fast moving startup environment
• Opportunity to work alongside senior and staff engineers from frontier labs
• Health, vision, dental benefits
• 401K match
• Lunch provided everyday onsite
• Weekly snack orders
• Visa sponsorship & relocation support available

Ideal candidate

Senior infrastructure-minded engineer who thrives at research pace and loves enabling frontier RL. You balance production rigor with speed and enjoy partnering closely with researchers.

Requirements

• Strong software engineering fundamentals and hands-on experience building production-grade LLM inference and training infrastructure
• Experience building LLM training/inference internals such as transformers, distributed training, and inference libraries like vLLM, SGLang, Megatron
• Experience working on RL training frameworks like Slime, veRL, Ray Train, SkyRL, Miles
• Significant experience with distributed systems principles and cloud platforms (AWS, GCP) and container orchestration (Kubernetes)
• Experience with data engineering tools and building robust, scalable data pipelines
• Proficiency in core ML frameworks such as PyTorch or JAX
• Can balance production rigor with fast-moving research pace and communicate tradeoffs clearly