Member of Technical Staff, Pre-training Systems

Magic; San Francisco (CA); On-site; USD 275,000 - 474,000; Full time

Benefits offered by this job

Equity in total compensation

401(k) with matching

Health, dental and vision insurance

Unlimited PTO

Job summary

Magic is seeking a Research Engineer for the Pre-training Systems team to design and operate the distributed infrastructure that trains its long-context models at scale.

You will own systems ensuring training runs are performant, reliable, and reproducible across massive GPU clusters, addressing memory pressure, communication overhead, and fault tolerance while pushing training throughput.

Qualifications

• Strong software engineering fundamentals and distributed systems knowledge.
• Experience training large models on multi-node GPU environments.
• Proficiency in debugging cross-layer issues in production ML systems.

Responsibilities

• Scale distributed training across large GPU clusters.
• Optimize communication patterns and gradient synchronization.
• Improve checkpointing, fault tolerance, and job recovery systems.
• Profile and remove performance bottlenecks across compute, networking, storage.

Skills

Software engineering

Distributed systems

Multi-node training

Performance debugging

Ownership mindset

Job description

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 pre-training, domain-specific RL, ultra-long context, and inference-time compute to achieve this goal.

About the role

As a Research Engineer on the Pre-training Systems team, you will design and operate the distributed infrastructure that trains Magic’s long-context models at scale.

This role focuses on large-scale model training across massive GPU clusters. You will work at the boundary between deep learning and distributed systems, ensuring that training runs are performant, reliable, and reproducible under extreme scale.

Magic’s long-context models create non-trivial systems challenges: sustained memory pressure, communication overhead across thousands of devices, long-running jobs that must survive failures, and efficient sequence packing under hardware constraints. You will own the systems that make large-scale pre-training stable and fast.

What you’ll work on

• Scale distributed training across large GPU clusters (data, tensor, pipeline parallelism)

• Optimize communication patterns and gradient synchronization

• Improve checkpointing, fault tolerance, and job recovery systems

• Profile and eliminate performance bottlenecks across compute, networking, and storage

• Improve experiment reproducibility and orchestration workflows

• Increase hardware utilization and training throughput

• Collaborate with Kernels and Research to align model architecture with systems realities

What we’re looking for

• Strong software engineering and distributed systems fundamentals

• Experience training large models in multi-node GPU environments

• Deep understanding of parallelism strategies and performance trade-offs

• Experience debugging cross-layer issues in production ML systems

• Strong ownership mindset and ability to operate critical infrastructure

• Track record of improving performance or reliability of large-scale systems

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