Magic’s mission is to build safe AGI that accelerates humanity’s progress on the world’s most important problems. We believe the most promising path to safe AGI lies in automating research and code generation to improve models and solve alignment more reliably than humans can alone. Our approach combines frontier-scale pretraining, domain-specific RL, ultra-long context, and inference-time compute to achieve this goal.

About the role

As a Research 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.

What you’ll work on

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

What we’re looking for

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

Our culture

Integrity. Words and actions should be aligned

Hands-on. At Magic, everyone is building

Teamwork. We move as one team, not N individuals

Focus. Safely deploy AGI. Everything else is noise

Quality. Magic should feel like magic

Magic strives to be the place where high-potential individuals can do their best work. We value quick learning and grit just as much as skill and experience.

Compensation, benefits, and perks (US)

Annual salary range: $275K - $550K

Equity is a significant part of total compensation, in addition to salary

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

A small, fast-paced, highly focused team