Software Engineer, Systems Generalist
Builds and scales core infrastructure for AI model training, data systems, and developer tools in a high-impact team. Requires backend proficiency (Python/Rust), experience with large-scale clusters like Kubernetes, and end-to-end project ownership.
About the job
What You’ll Do
We interview generally, but during project selection we’ll take into account your interests and experience alongside organizational needs.
Example areas you may contribute to:
Core Infrastructure: Support teams that train, research, and serve AI models. Build systems for large Kubernetes clusters with GPU workloads, or infrastructure to support Tinker.
Data Infrastructure: Design and optimize data pipelines using tools like Spark and other modern data infrastructure technologies. Build scalable, reliable data infrastructure with governance best practices.
Developer Productivity: Build tooling, systems, frameworks for optimized developer environments.
Skills and Qualifications
Minimum qualifications:
- Bachelor’s degree or equivalent experience in computer science, engineering, or similar.
- Proficiency in at least one backend language (Python or Rust).
- Experience operating large-scale clusters and container orchestration systems (Kubernetes or Slurm).
- Comfort operating across the stack and owning projects end-to-end.
- Thrive in highly collaborative environment with cross-functional partners.
- Bias for action and initiative across stacks and teams.
Preferred qualifications:
- Strong debugging across application, OS, and network layers.
- Proficiency in Python or Rust, containers, and modern CI.
- Experience with Kubernetes, controllers/operators, or performance profiling.
- Familiarity with GPU/ML workflows or large-scale data/eval pipelines.
Logistics
Compensation: $350,000 - $475,000 USD annual salary, depending on background, skills and experience.
Benefits: Generous health, dental, and vision benefits, unlimited PTO, paid parental leave, relocation support.
Skills
Python, Rust, Kubernetes, Slurm, Spark, Containers, CI/CD, GPU, Distributed Systems, Data Pipelines
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