Head of Machine Learning

Principal (Head of) Machine Learning Systems Engineer
Fully Remote · United States · Meaningful Equity
Build the technology. Shape the team. Stay hands-on.
You’ve spent years turning difficult technical problems into systems that work. This is an opportunity to put that experience at the center of a health product, with real influence over what gets built and how as their lead ML engineer.
A newly Series A-funded healthtech company is developing a connected device that turns real-world sensor data into personalized health insights. Its ambition is to help people better understand their health and, ultimately, improve and save lives.
As its first Principal ML Systems Engineer, you’ll become the senior technical owner of its ML capability. You’ll set direction and build alongside an existing junior ML engineer, with scope to help shape an additional hire as the team develops with budget for additional headcount already allocated to you.
You’ll be writing code, training models and making the architectural decisions yourself. The appeal is having the authority to shape the work while staying close to the engineering.
Why join?
Your decisions will shape the product. You’ll influence the models, datasets, infrastructure and evaluation methods that underpin the technology.
You’ll own problems through to the outcome. From investigating sensor data to deploying a reliable system, you’ll have room to follow the evidence and solve the underlying issue.
You’ll help build the capability around you. Mentor the existing mid level engineers, establish strong technical practices and influence how the team grows.
Your work will reach people. This is a physical health-monitoring product, with a direct connection between your engineering and the insights users receive.
Work remotely, stay connected. Fully remote across the US, with a preference for candidates in Boston or Austin and occasional in-person meetups.
Share in what you build. Meaningful equity and compensation aligned with your experience and the responsibility you’ll take on.
What you’ll build
The work spans machine learning and the software that makes it useful.
You’ll create well-curated datasets, from collection through annotation. Train, evaluate and improve models. Build the pipelines and backend services that move device data into production inference and customer-facing insights.
Some problems will call for a better model. Others will require cleaner labels, a different data pipeline or a simpler architecture. You’ll be trusted to work out where the problem sits and take responsibility for solving it.
You’ll also work closely with hardware, firmware, product and R&D colleagues to evaluate sensing approaches and turn experiments into practical improvements.
What you’ll bring
You have substantial experience delivering real-world ML and software systems, typically gained over seven or more years.
You’ve personally trained and improved models, built the data foundations behind them, and deployed systems that needed to keep working after launch.
Your experience should include:
Strong Python and hands-on model development using frameworks such as PyTorch.
Backend engineering, APIs and distributed data-processing systems.
Dataset construction, annotation, evaluation and failure analysis.
Cloud infrastructure and production ML inference.
Sound judgment when the objective is clear but the route to achieving it isn’t.
Computer vision, sensor data or connected-device experience would be particularly relevant. Healthcare or medtech experience would also be valuable.
The environment includes AWS, PostgreSQL and Terraform, with Go experience desirable.
Academic depth is welcome. What matters most is what you’ve built, the decisions you’ve owned and your ability to make complex technology work in practice.
I'm looking forward to hearing from you.

Work arrangement
Yes