# Senior ML Engineer (Energy & Utilities) at Azx

- Company: Azx
- What the company does: Tech transformation, societal shifts, and environmental disruption put enormous pressure on critical industries like energy, infrastructure, real estate, and others to adapt, pursue new growth opportunities, and optimize operations. Backed by Powerhouse Ventures.
- Company website: https://azx.io
- Type: Startups (AI role)
- Level: Senior
- Location: Seattle
- Work setup: Remote
- Pay: $140K to $230K base salary per year (USD)
- Posted: 2026-08-31
- Apply by: 2026-10-15
- Apply: https://jobs.ashbyhq.com/careers.azx.io/19109ff0-e6bb-4cf9-bedd-5510705a5b70
- Page: https://www.1752.vc/careers/jobs/azx-senior-ml-engineer-energy-and-utilities/

## About the role

We are seeking a Senior ML Engineer that will build the AI models and underlying tools that power AZX's work with utility clients — reusable capabilities used across many client engagements. Underneath the models, you'll also build the infrastructure that makes them possible: a fast building-energy simulation engine, tools for reading real-time grid sensor data, and utilities for working with standardized building data formats — much of which we publish as open source, so some of the people using your work are...

## What they're looking for

- 5+ years of shipping applied ML on real-world signals — forecasting, disaggregation, detection/classification on interval or sensor data, survival/reliability modeling, or an adjacent-industry equivalent
- Strong numerical and scientific computing skills: feature engineering from raw interval data, solver-level numerics when needed, and a healthy distrust of your own metrics
- Python plus a systems language — the models and tooling are Python, the engine is Rust, depth in one, working ability in the other, and the appetite to close the gap (Rust is teachable here, modeling judgment isn't)
- Library craft: you build things other engineers consume — versioned, tested, documented, with an API you'd want to call yourself
- Willingness to do your own data engineering — finding, cleaning, joining, and profiling inputs yourself rather than trusting a prepared dataset
- Practical fluency in our core stack — Python 3.12+ (numpy, pandas/polars, scikit-learn, statsmodels), time-series feature engineering, forecasting/clustering libraries (sktime/statsforecast-class), and SQL/Postgres or TimescaleDB-class hypertables

Tags: Engineering