Member of Technical Staff, Inference & RL Systems

Magic•San Francisco, CA

About The Position

As a Software Engineer on the Inference & RL Systems team, you will design and operate the distributed systems that serve our models in production and power large-scale post-training workflows. This role sits at the boundary between model execution and distributed infrastructure. You will work on systems that determine inference latency, throughput, stability, and the reliability of RL and post-training training loops. Magic’s long-context models introduce demanding execution constraints: KV-cache scaling, memory pressure under long sequences, batching trade-offs, long-horizon trajectory rollouts, and sustained throughput under real-world workloads. You will own the infrastructure that makes both production inference and large-scale RL iteration fast and reliable.

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

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 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