Senior Software Engineer, RL Post-Training Frameworks

Requirements:

• MS or PhD in Computer Science, Computer Engineering, or related field (or equivalent experience)
• 5+ years of professional experience in distributed systems, high-performance computing, deep learning infrastructure, or ML systems engineering
• Strong proficiency in Python and C/C++
• Demonstrated experience building or contributing to large-scale distributed systems or runtime frameworks in production at a frontier AI lab, hyperscaler, or major technology company
• Strong verbal and written communication skills and the ability to collaborate across organizational and geographic boundaries

What you'll be doing:

• Architect and build RL post-training infrastructure that scales efficiently from experimentation on a single GPU to production across thousands of nodes
• Tune RL training-inference-rollout loops on GPUs, CPUs, and LPUs for performance optimization
• Contribute to and improve the performance and usability of open-source RL frameworks
• Partner with teams for fault tolerance, elastic scaling, and fast restarts in distributed training jobs

Nice to haves:

• Deep expertise in networking, advanced multi-dimensional parallelisms, or memory optimizations
• Experience integrating high-performance inference engines into RL training loops for GPU-accelerated rollout
• Strong background in actor- and task-based distributed programming
• Familiarity with specific technical areas like Kubernetes runtime internals, PyTorch internals, or distributed systems design

Perks and Benefits:

• Highly competitive salaries
• Comprehensive benefits package