Machine Learning Engineer

Role Description

An early-stage engineering automation company is fixing a problem that's been done by hand for decades. Most mechanical and electrical engineering work, the analysis, modeling, and documentation that turns raw building data into a real decision, still eats up billable hours and doesn't scale. Their first product tackles engineering due diligence and energy modeling for commercial real estate, a process that can burn 150 hours and $30,000 per project before anyone sees an answer. They're turning that into real-time, engineering-grade analytics: automated data extraction, calibrated energy models, equipment and costing recommendations, and financial analysis that connects decarbonization straight to operating costs and asset value.

This is a small, technical, founder-led team, engineers building for engineers.

As the AI-Native Machine Learning Engineer, you'll build the systems that turn messy real-world building data into energy models and financial decisions engineers can actually trust. It's an unusual mix of skillsets:

• You understand how buildings work (HVAC, thermodynamics, heat transfer).
• You build with modern AI as your default toolset, treating foundation models and coding agents as first-class building blocks.
• You’ll own real surface area end to end, from extraction pipeline through to what an asset manager sees on their screen.

Qualifications

• Genuine fluency in building systems: HVAC, energy modeling, thermodynamics, heat transfer, and how commercial buildings actually consume and lose energy.
• An AI-native way of working, building with foundation models and agentic systems as default tools rather than an afterthought.
• Solid software and ML engineering fundamentals, comfortable taking a model from notebook to production.
• Good judgment on when a learned model should defer to physics or a calibration check.
• Comfortable with early-stage ambiguity, broad ownership, and shipping fast.

Requirements

• Hands-on experience with energy simulation and standards like ASHRAE modeling and calibration guidelines.
• Familiarity with building automation/management systems, MEP, or commercial real estate due diligence.
• Experience with document extraction, OCR, RAG, or multimodal pipelines on noisy real-world data.

Benefits

• Genuine 0 to 1 ownership on a product still being built from the ground up.
• Work at the intersection of real engineering and modern AI, not a bolt-on AI feature.
• Small, technical, founder-led team where your work has direct impact.
• Competitive salary and equity in a company solving a problem worth $30,000 and 150 hours per project today.