Member of Technical Staff Inference and RL Systems

You will design and operate distributed systems that serve models in production and support large-scale post-training workflows. You will optimize inference performance, manage reliability across RL pipelines, automate recovery, and remove bottlenecks across GPU, networking, and storage layers.

Responsibilities

• 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 the 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

Requirements

• 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

• Equity as a significant part of total compensation
• 401(k) plan with 6% salary matching
• Health, dental, and vision insurance for employees and dependents
• Unlimited paid time off
• Visa sponsorship
• Relocation stipend to San Francisco