Magic

Member of Technical Staff, Inference & RL Systems

San Francisco · Staff+

Sponsorship not specified$225k-$550kDetected 144 days ago

Distributed SystemsMachine LearningMLOpsResearch

About the role

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.

Design and scale high-performance inference serving systems
Optimize KV-cache management, batching strategies, and scheduling
Build and maintain distributed RL and post-training infrastructure
Collaborate with Kernels and Research to align execution systems with model architecture
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.
You will own the infrastructure that makes both production inference and large-scale RL iteration fast and reliable.

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

Magic's mission is to build safe AGI that accelerates humanity's progress on the world's most important problems.

This listing is sourced directly from Magic's careers page and normalized into a canonical job model.