Machine Learning Research Engineer

Responsibilities
What you’d own:

• AI-based data assimilation — Develop methods for incorporating real-time observations from balloons, satellites, weather stations, and other sources into our forecasts.

• A foundation model for weather — Work toward a single model capable of predicting many weather-related datasets, including variables and data products not covered by traditional global forecasts.

• Messy, large weather datasets — Find, understand, clean, and combine large datasets with inconsistent formats, resolutions, coverage, and quality. Determine which data is actually useful and build systems that make it easier to use again.

• Rapid experiments — Test ideas quickly, learn from failures, and follow promising results into the weeds and chase down another 1% of model improvement.

• Infrastructure and systems — Turn successful experiments into reusable systems that accelerate future research.

• Research direction — Form hypotheses, design convincing experiments, keep up with relevant ML research, and help decide which ideas are worth pursuing.

• Whatever the problem needs — Venture into operations, infrastructure, evaluation, data engineering, or other technical side quests when needed to get the research working in practice.

• Strong research taste: you can identify important questions, design experiments that answer them, and recognize when a result is real.

• Deep enthusiasm for machine learning and a desire to understand models in detail rather than treating them as black boxes.

• Strong Python and PyTorch skills, with experience developing and debugging nontrivial ML systems.

• Experience working with large, messy datasets and building dependable pipelines or abstractions around them.

• Able to iterate quickly while thinking systematically about which work should become durable infrastructure.

• Comfortable crossing boundaries between research and engineering and learning unfamiliar tools or domains as needed.

• Experience with weather, climate, geospatial data, scientific machine learning, computer vision, or time-series forecasting is helpful, but not required.

• 401(k)

• Dental, health, and vision insurance

• Unlimited PTO

• Stock Option Plan

• Office food and beverages

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

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