7 months ago
Base Salary
$225k - $550k/yr
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
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 latency, throughput, and cost trade-offs
- Track record of owning critical production infrastructure
Benefits
- Equity in addition to salary
- 401(k) plan with 6% salary matching
- Health, dental, and vision insurance for employees and dependents
- Unlimited paid time off
- Visa sponsorship and relocation stipend to San Francisco, if possible
- Small, fast-paced, highly focused team
Categories
About Magic
Magic builds frontier-scale code models designed to act as an AI coworker for software developers and engineering teams, automating code generation and research tasks. Its products center on developer-facing models and tooling that integrate into software workflows for teams seeking higher velocity and reliability. Founded in 2022 and headquartered in San Francisco, the privately held company focuses on AI-driven developer tools spanning information technology and machine learning.