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Salary: USD 120,000 - 200,000 / annual
WindBorne Systems designs and operates Atlas, a global constellation of smart weather balloons that collect atmospheric observations to power WeatherMesh, an AI weather model that produces global forecasts every hour.
You'll join a small, fast-moving deep learning research team as a software engineer focused on machine learning systems. Your primary responsibilities will include: (1) keeping model inference running reliably and building tooling to detect and recover from failures; (2) processing atmospheric data in real time, building pipelines that handle late or missing observations and flag issues requiring attention; and (3) creating visualization and monitoring tools so the team can see data processing and model inference progress, identify bottlenecks, and debug failures.
You'll work cross-functionally with deep learning researchers, backend engineers, and frontend engineers. The role emphasizes ownership—you'll be responsible for systems after they launch, understanding how they perform, fail, and recover. You'll have immediate problems to solve and the autonomy to propose and build new infrastructure.
The company explicitly states that prior machine learning experience is not required. They value learning ability, problem-solving approach, and software craftsmanship over specific ML credentials. You're expected to dig into model code, ask researchers questions, and use AI tools to get up to speed. The role rewards curiosity, production-system thinking, and the ability to see beyond immediate fixes to build reusable infrastructure.
You'll use AI and agentic tools extensively—to explore code, learn unfamiliar systems, test ideas, and build quickly—while maintaining understanding and accountability for what you ship.
REQUIREMENTS:
- Demonstrated ownership of software in production: responsibility for system performance, failure modes, and recovery
- Ability to build substantial software and walk through hard technical problems, including debugging across multiple system components
- Comfort reading unfamiliar code and thinking beyond immediate fixes to identify reusable infrastructure
- Curiosity and self-motivation; ability to investigate and learn without constant guidance
- Proficiency using AI and agentic tools while maintaining code understanding and debugging capability
- Flexibility on programming languages, frameworks, and years of experience
NICE TO HAVE:
- Experience with data pipelines, distributed systems, or infrastructure
- Background in scientific computing, weather data, or geospatial systems
- Familiarity with ML inference, GPU computing, or PyTorch
