Member of Technical Staff, Inference & RL Systems

The role

You will design and operate distributed systems that serve models in production and power large-scale post-training workflows. The role works at the boundary between model execution and distributed infrastructure.

What you will do

• Design and scale high-performance inference serving systems
• Optimize KV-cache management, batching strategies, and scheduling
• Improve throughput and latency for long-context workloads
• Build and maintain distributed RL and post-training infrastructure
• Improve reliability of rollout, evaluation, and reward pipelines
• Automate fault detection and recovery for serving and RL systems
• Profile and eliminate performance bottlenecks across GPU, networking, and storage layers
• Collaborate with Kernels and Research to align execution systems with model architecture

Required qualifications

• Strong software engineering and distributed systems fundamentals
• Experience building or operating large-scale inference or training systems
• Deep understanding of GPU execution constraints and memory trade-offs
• Experience debugging performance issues in production ML systems
• Ability to reason about system-level trade-offs between latency, throughput, and cost
• Track record of owning critical production infrastructure

Benefits

• 401(k) plan with 6% salary matching
• Generous health, dental and vision insurance for you and your dependents
• Unlimited paid time off
• Visa sponsorship and relocation stipend to bring you to SF, if possible

Before you apply

• Visa sponsorship and relocation stipend to bring you to SF, if possible

About the company

Magic's mission is to build safe AGI that accelerates humanity's progress on the world's most important problems. The company believes the most promising path to safe AGI lies in automating research and code generation.