Machine Learning Infrastructure Engineer

WindBorne Systems; Atlanta (GA); Hybrid; USD 140,000 - 240,000; Full time

Benefits offered by this job

401(k)

Dental, health, and vision insurance

Unlimited PTO

Stock Option Plan

Office food and beverages

Job summary

WindBorne Systems is building AI weather models and real-time forecasts from a global balloon network. We seek a systems-minded ML engineer to reduce firefighting by improving data pipelines, training infra, and deployment reliability.

You’ll own end-to-end uptime, scale inference, and manage data from dozens of sources, with a fast-paced research team and on-prem plus cloud deployments.

Qualifications

• Experience running production ML systems.
• Experience with large datasets.
• Experience with fast-paced model releases and reliable deployments.
• Experience with PyTorch, Docker, memory management, compression, and debugging network saturation.
• Affinity for systems and structured infra.

Responsibilities

• Research to Operations pipelines — own uptime end-to-end: build health monitoring, logging, diagnose failures across nodes.
• Inference scaling & compute strategy — evaluate cost/performance across cloud options and manage on‑prem resources as we scale.
• Data pipelines & upstream reliability — build pipelines for training and realtime data with QC checks and logging/alerts for edge cases.
• Training infrastructure — make distributed training runs reliable with monitoring, auto-recovery, and job scheduling so researchers can launch experiments with less babysitting.

Skills

Production ML

Large datasets

Model release

PyTorch

Docker

Memory management

Network debugging

System reliability

Tools

Docker

Job description

WindBorne Systems is supercharging weather forecasts with a proprietary data source: a global constellation of next-generation smart weather balloons targeting critical atmospheric data. We design, manufacture, and operate our own balloons, using their observations to generate otherwise unattainable weather intelligence.

Our mission is to eliminate weather uncertainty and help humanity adapt to climate change—whether by predicting hurricanes or speeding the adoption of renewables. The founding team of Stanford engineers was named Forbes 2019 30 Under 30 and is backed by top-tier investors, including Khosla Ventures and Footwork VC.

WindBorne builds AI weather models that run 24/7, producing global forecasts every 20 minutes. Our research team is small and moves fast, but too much of their time goes to operationalization and infra firefighting instead of model development. We need someone to fix that.

Responsibilities

What you’d own:

• 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.