Who We Are:

The world is electrifying, and HVAC is at the center of it. Over the next decade, 100 to 200 million new heat pumps and HVAC units will become the backbone of a decarbonized world, but the industry has no way to keep them running well. The technician workforce has barely grown while the equipment base has multiplied, reactive repairs eat most of a tech's time, and half the installed base gets no real maintenance at all, wasting energy and driving billions in emergency costs. Thalo is fixing this. We're building the physical AI layer for HVAC: sensors plus physics-based intelligence that turn static equipment into self-monitoring systems and shift service from guesswork to data. Every sensor we deploy makes the platform smarter and builds a dataset on how equipment truly performs that no one else can.

We're a small team that has built self-driving cars at Waymo, worked on satellite imagery at Google, designed systems for John Deere, developed space missions for NASA, and led manufacturing design for Boom Supersonic jets. Now we're bringing that same rigor to one of the most important buildouts of our lifetime. In this role, the models you build decide whether a technician is sent to the right unit at the right time, and whether a building wastes energy or runs clean. It's a rare chance to work on a generational climate challenge, with first-of-its-kind data and a team of high performers who ship.

About the Role:

As our Senior Machine Learning Engineer, you’ll own the intelligence layer of Thalo’s platform. We generate hundreds of gigabytes of HVAC sensor data no one in the world has seen before, and your job is to turn it into the detection algorithms, physics-based models, and product features that tell our customers exactly what’s wrong with their equipment and what to do about it.

This is a hands-on, end-to-end role for someone who wants to own a problem from raw time-series data all the way to a shipped, customer-facing feature. You’ll build and tune our issue-detection engine, put physics-based, ML, and LLM-powered models into production, establish how we evaluate and trust them, and work closely with our engineering, customer success, and business development teams to make sure the intelligence we ship is accurate, trustworthy, and genuinely useful in the field. You’ll be a senior voice on a small, mighty team!