Forward-Deployed ML Engineer

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

Our mission is to eliminate weather uncertainty, and in the process help humanity adapt to climate change, be that predicting hurricanes or speeding the adoption of renewables. We are building a future in which the planet is instrumented by thousands of our micro high-altitude balloons, eliminating gaps in our understanding of the atmosphere and giving people and businesses the information they need to make critical decisions. The founding team of Stanford engineers was named Forbes 2019 30 under 30 and is backed by top investors including Khosla Ventures.

This role is unlike a typical machine learning position. You will begin as a regular member of our Deep Learning team, contributing to the development of our cutting-edge global AI weather model, WeatherMesh. After a few months, you will transition into a “forward-deployed” role, becoming the technical point of contact and project manager for key external collaborations, including a DARPA project. This means owning both the technical contributions and the customer relationship, ensuring WindBorne’s models are successfully integrated into high-stakes projects.

You won’t just be training models in isolation. You’ll be learning the science, building the systems, and then sitting across the table from program managers and partners, explaining how and why the model works—and what comes next. The person in this role will bridge worlds: the intensity of deep learning R&D and the practical realities of customer-facing relationships and delivery.

Responsibilities

• Design, train, and evaluate of our AI-based weather models
  

• Work as part of the Deep Learning team on model development and operations
  

• Transition into owning external collaborations, taking responsibility for both technical deliverables and project management
  

• Translate ambiguous customer needs into concrete technical tasks and drive them to completion
  

• Represent WindBorne’s technical work to external partners, providing clarity, accountability, and credibility
  

• Act as the connective tissue between our research team and customer-facing programs, ensuring our science delivers real-world impact
  

Skills and Qualifications

• Strong foundation in machine learning and deep learning (PyTorch preferred), with experience training and debugging models end-to-end
  

• Ability to write high-quality, maintainable code for ML workflows (data processing, modeling, evaluation)
  

• Exceptional communication skills—you can clearly explain complex technical ideas to non-technical stakeholders
  

• Self-directed learner who thrives in ambiguous, high-stakes environments
  

• Interest in, or experience with, customer-facing work such as technical project management, research collaborations, or applied consulting
  

• Strong work ethic and drive for constant improvement, balanced with the ability to collaborate within a high-functioning team
  

This role is for you if…

• You are an ML engineer who doesn’t just want to code in a silo—you want to see your work make an immediate impact in the world
  

• You thrive on both technical depth and human connection: you enjoy debugging CUDA, but you also don’t shy away from getting on a call with a DARPA program manager
  

• You are excited to grow from a strong individual contributor into the technical owner of critical external programs
  

Benefits

• 401(k)

• Dental insurance

• Health insurance

• Vision insurance

• Unlimited PTO

• Stock Option Plan

• Office food and beverages

Salary

• $90k-200k** We are considering a range of backgrounds and experience levels for this position and will adjust our offers accordingly to be competitive with market rates.

Location

858 San Antonio Rd, Palo Alto, CA. In person required.