Software Engineer, Machine Learning Systems

WindBorne Systems

California, Northern (MO, KY)

Hybrid

USD 136,000 - 184,000

Full time

5 days ago

Be an early applicant

Application generator

Don’t send a generic resume — generate a resume and cover letter tailored to this exact role.

Get past ATS filters

Benefits offered by this job

401(k) plan

Health insurance

Dental insurance

Vision insurance

Unlimited PTO

Stock options

Job summary

WindBorne Systems, based in Redwood Shores, CA, seeks a Software Engineer to maintain and enhance our WeatherMesh inference pipelines and data processing tools. You’ll collaborate with DL, backend, and frontend engineers to tackle immediate problems and ideate future capabilities.

No ML background required; strong learning ability and software craft are valued. You will own production software, build reliable systems, and help shape reusable infrastructure while delivering fast, robust tooling

Qualifications

Owned software in production: performance, failure handling, and recovery are key.
Built substantial software and can explain problem solving, code reading, and debugging across systems.
Ability to foresee reusable infrastructure and plan long-term improvements.
Curiosity, motivation to build, and hands-on learning approach with AI tools.

Responsibilities

Keep forecasts running by building tooling for weather models and ensuring recovery after node failures.
Process atmospheric data in real time; build pipelines that handle late/missing data and highlight issues.
Show system progress and failures with visualization tools for monitoring model inference.

Skills

Production software

System reliability

Bug fixing

UI design

Tools

UI design

Job description

Software Engineer, Machine Learning Systems

WindBorne Systems designs, manufactures, and operates Atlas, a global constellation of smart weather balloons. The atmospheric observations they collect help drive WeatherMesh, our AI weather model.

Our model, WeatherMesh, runs 24/7, producing global forecasts every hour. Our deep learning research team is small and iterates quickly, but sometimes R&D gets ahead of the internal tools that support it. We’re looking for a talented and curious software engineer to keep model inference running reliably, process atmospheric data as it arrives, and build tools that show us what’s happening in both.

In this role, you’ll work with our deep learning team and backend and frontend engineers. We have problems for you to tackle right away, and we also want someone with ideas about what to build next. You’ll help figure out what’s needed, build it, and be responsible for how it works once it’s running.

Prior machine learning experience is not required! You can come in with no ML background at all and be successful. Where you lack understanding, you’ll dig into model code, ask researchers questions, and use AI tools to get up to speed. Essentially, learn what you need to build and go build it. We care much more about how well you learn and your approach to problem solving than whether you have RAG or LLM fine‑tuning on your resume. [Note: We’re not trying to shade ML engineers - we have many great ones on our team. Yes, those skills can be useful in other domains, but they’re not prerequisites for this role. We’re bringing them up to emphasize that, without good software taste and a knack for learning, ML skills alone are not what we're looking for.

Snapshot of the balloon constellation ONMARCH 31, 2026

Responsibilities

What you’ll own

Keep forecasts running. Build the tooling that runs our weather models and detects failures. If a cloud node goes down, our systems need to recover and keep producing forecasts.

Process atmospheric data in real time. Build pipelines for incoming observations, account for late or missing data, and make it clear when something needs attention.

Show us what’s happening. Build tools to visualize data processing and model inference as they run. The team should be able to see how a run is progressing, where it’s slowing down, and what went wrong if it fails.

Skills and Qualifications

You’d be a good fit if:

You’ve owned software in production. You’ve been responsible for a system after launch: how it performs, how it fails, and how it recovers. That experience has given you a feel for the monitoring, reliability, and operational discipline a critical system needs.

You’ve built substantial software. You can walk us through a hard technical problem you solved, why you chose your approach, and what you learned. You’re comfortable reading unfamiliar code and tracking down bugs that involve more than one part of a system. Bonus points for good UI design and a portfolio we can see.

You think beyond the immediate fix. You can solve today’s problem while recognizing what should become reusable infrastructure for future work. You have ideas about how to improve the systems you own and want to help decide where we go next.

You’re curious and motivated to build. You ask questions, investigate how things work, and turn what you learn into something useful. You don’t sit idly while waiting for guidance; you can’t help but poke around.

You use AI and agentic tools like a bandit. You use them everywhere you can: to explore code, learn about unfamiliar systems, test ideas, and build quickly. You understand the code you ship and can debug it when it doesn’t work. Most importantly, you know these tools are powerful, but you can’t outsource thinking to them entirely. After all, you’re still on the hook for what gets built.