Machine Learning Infrastructure Engineer

Responsibilities:

• Research to Operations pipelines — Our models serve real-time forecasts to customers with strict latency requirements. You'd own uptime end-to-end: build health monitoring, improve logging, diagnose failures across nodes.
• Inference scaling & compute strategy — We have an on-prem cluster but also use cloud providers, especially for production deployments. You'd evaluate cost/performance tradeoffs across cloud options as we scale, and also help manage growing on-prem resources for compute and storage.
• Data pipelines & upstream reliability — Weather data comes from dozens of sources (satellites, government agencies, our own balloon observations) with varying schedules, incomplete documentation and sometimes failing or changing quality. You'd build pipelines for training and realtime data that gracefully handle upstream delays, do QC checks on data, and add logging and alerting for a zoo of edge cases.
• Training infrastructure — Make distributed training runs reliable. They die from silent OOMs, network faults, and storage issues. Build monitoring, auto-recovery, and job scheduling so researchers can launch experiments with less need for babysitting them.

Skills and Qualifications: Requirements:

• Have experience running production ML systems — you’re not just good at fighting fires but also know how to build systems that don’t catch on fire.
• Experience with large datasets.
• Comfortable keeping up with fast-paced model releases and building reliable custom deployments for them.
• Experience with PyTorch, Docker, cursed memory management, compression and debugging network saturation.
• Affinity for systems and structure — you can counterbalance a research team’s natural state of chaos with well-organized infrastructure and clear processes.

Nice to haves:

• Experience with weather data, geospatial pipelines, or scientific computing.
• Experience with very large datasets, on the petabyte scale.
• Experience managing GPU clusters or job schedulers.

Benefits:

• 401(k)
• Dental, health, and vision insurance
• Unlimited PTO
• Stock Option Plan
• Office food and beverages

Salary: $140k–$240k. We consider a range of backgrounds and experience levels and adjust offers to be competitive with market rates.

Location: 1600 Bridge Pkwy, Redwood City, CA. Hybrid or in-person.