Machine Learning Systems Administrator / DevOps Engineer: Training Clusters & Inference Cloud
Zyphra · San Francisco
Frontier labonsite
At a glance
Highlights
- Maintain core infrastructure behind model training and inference cloud.
- Split time between large-scale training clusters and production deployments.
- Own critical systems affecting training throughput and inference reliability.
What they offer
- Comprehensive medical, dental, vision, and FSA plans
- Competitive compensation and 401(k) plan
- Relocation and immigration support on a case-by-case basis
- In-office snacks and meals provided
- Unlimited PTO and company holidays
Ideal candidate
A hands-on systems thinker who loves owning critical infrastructure end to end and keeping complex systems fast and reliable. You thrive collaborating with research and product teams and care deeply about developer productivity.
Requirements
- Experience with Linux system administration, user and access management, and automation
- Expertise in scripting for system tooling and automation (bash, Python)
- Experience with containers and scheduling/orchestration systems (Docker, Slurm, Kubernetes)
- Experience running production services including monitoring, alerting, deployment pipelines and incident response
- Experience building tooling for cluster validation and reliability health checks
- Experience setting up and managing developer tools and third-party services
- Strong debugging and troubleshooting skills across compute, storage and networking
- Strong communication skills and ability to collaborate across teams
- remote policy
- in-person
- visa sponsorship
- Immigration support on a case-by-case basis
Lab profile
About Zyphra
San Francisco AI lab building open language and audio models and autonomous desktop agents.
What they build
- Desktop and browser-based autonomous agents
- Open language models
- Open-source audio models (ASR, TTS, speech-to-speech)
Known for
- Agentic systems and human-AI interaction
- Reinforcement learning, post-training and preference learning
- Large-scale language model pre-training
- Novel model architectures and training methods
Key facts
- Headquartered in San Francisco, California
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