Machine Learning Engineer

S23 - AI

AI-Native Machine Learning Engineer

Location: Remote (US-based)

Salary: $160,000 – $220,000 + Equity

About the Role

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) and 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.

Key Responsibilities

• Build and run production pipelines that pull structured data out of unstructured inputs using multimodal models, vision, and LLM-based extraction with verification
• Develop and automate engineering-grade energy modeling and calibration, grounding learned components in real building physics and validating against actual metered consumption
• Build the recommendations and costing engine, covering equipment replacement guidance, costing, and capital planning an engineer would sign off on
• Connect engineering outputs to financial analytics: ROI, scenario sensitivity, operating cost impact, incentives, and avoided emissions penalties
• Use AI and coding agents aggressively to move fast, while keeping the right level of verification for anything customers and engineers rely on
• Own features end to end as part of a small team: scoping ambiguous problems, shipping, and iterating on real feedback

What We're Looking For

• 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

Nice to Have

• 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

Why Join?

• 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