Senior ML Engineer – Energy & Utilities

This is a fully remote position, open to applicants in Washington.

📋 Description

• Develop AI models and foundational reusable tools for utility customers.

• Manage reusable machine learning libraries tailored for forecasting, disaggregation, demand response, detection, and asset health.

• Deliver each library accompanied by evaluation harnesses and comprehensive documentation.

• Create tested libraries equipped with validation harnesses for client pods.

• Oversee the building-energy simulation engine, which includes Rust crates and Python bindings.

• Validate the simulation engine against the reference oracle and ensure its optimal performance.

• Utilize representative-archetype simulation to effectively handle city-scale building stock.

• Manage grid-data toolkits, encompassing protocol codecs, synthetic scenario generators, and verification utilities.

• Produce synthetic yet credible meter data in collaboration with domain experts.

• Develop planning and dispatch support for demand-response initiatives.

• Supervise the publish path for versioned crates and Python packages, including evaluations and benchmarks.

• Monitor capability failures in the field with client pods and convert feedback into future enhancements.

• Participate in specific client projects when tools face real-world data that necessitate adjustments.

• Release a significant portion of the work as open source.

⛳️ Requirements

• A minimum of 5 years of experience in deploying applied machine learning on real-world signals.

• Proficient numerical and scientific computing skills.

• Proficiency in Python and a systems programming language; practical experience in Python and Rust or an equivalent systems language.

• Expertise in library development: versioned, tested, well-documented libraries with functional APIs.

• Willingness to engage in data engineering tasks independently.

• Experience with Python 3.12+ along with numpy, pandas/polars, scikit-learn, and statsmodels.

• Skills in time-series feature engineering.

• Familiarity with forecasting/clustering libraries such as sktime/statsforecast-class.

• Knowledge of SQL/Postgres or TimescaleDB-class hypertables.

• Comfort in learning Rust through PyO3/maturin.

• Experience in creating evaluation harnesses and continuous integration for scientific software.

• Readiness to quickly familiarize oneself with energy-domain terminology.

• Bachelor's Degree; a Master's Degree is advantageous.

• Must be authorized to work in the United States on a full-time basis.

• Not eligible to sponsor or take over sponsorship of employment visas.

• Required to complete a written take-home assignment followed by a live two-hour technical session.

• Applicants may submit a maximum of 2 applications at a time; applying to more than 2 roles within a 6-month period will lead to automatic disqualification.

🏝️ Benefits

• Competitive compensation package typical of early-stage startups (dependent on skills, experience, and location).

• Eligibility for bonuses.

• Comprehensive health insurance with significant coverage for dependents.

• Flexible paid time off policy.

• Equity opportunities.

• A fully remote work culture with a group of teammates located in Seattle.

• Biannual company summits.