Senior ML Engineer – Client Solutions

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

📋 Description

• Develop machine learning systems within client environments, from data discovery to scheduled deployment in production.

• Take ownership of the entire ML delivery process: data discovery, cleaning, modeling, evaluation, deployment, scheduling, monitoring, and retraining policies.

• Create forecasting and detection models that are resilient to delayed data feeds, revised entries, and missing labels.

• Conduct backtesting and model evaluation, articulating precision/recall trade-offs to operational stakeholders.

• Design systems that differentiate between “no prediction” and “incorrect prediction” for end users.

• Deliver usable product interfaces such as FastAPI services, React applications, and scheduled jobs.

• Set baselines and key performance indicators prior to deployment, implement monitoring, and provide post-deployment readouts with attribution limits.

• Act as the client-facing engineering representative during discovery sessions, demonstrations, and collaboration with client IT/data teams.

• Relay client data structures and failure scenarios back to the platform team.

• Collaborate across DevOps, infrastructure, front-end, and back-end engineering as part of a small team.

⛳️ Requirements

• Over 5 years of experience in deploying applied machine learning in production environments.

• Familiarity with forecasting, detection/classification in time series, survival/reliability modeling, or optimization.

• Strong data engineering capabilities, including data discovery, cleaning, joining, and profiling at scale.

• Knowledge of chronological splits, walk-forward validation, and as-of correctness.

• Proficiency in Python, SQL, testing, Docker, scheduling, API/app development, and monitoring.

• Experience in client-facing roles, including discovery, demonstrations, and addressing incorrect requests.

• Ability to discern when machine learning is not the appropriate solution.

• Proficiency in Python 3.12+ with libraries such as pandas/polars/DuckDB, scikit-learn, statsmodels, and gradient boosting techniques.

• Experience with SQL/Postgres, and familiarity with TimescaleDB/PostGIS for grid-related tasks.

• Expertise in time-series feature engineering and validation.

• Familiarity with FastAPI and sufficient knowledge of React/TypeScript to present results.

• Experience with Docker and basic cloud tools from Azure/AWS.

• Working knowledge of LLMs for extraction and retrieval tasks.

• A Bachelor's Degree is required; a Master's Degree is preferred.

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

• Domain experience in Energy, Utilities, Infrastructure, and Commercial Real Estate is advantageous.

• Willingness to travel twice a year for company summits.

• Completion of a written take-home assignment followed by a live two-hour technical interview.

• Limited to a maximum of 2 role applications at a time; applying for more than 2 roles within 6 months will lead to disqualification.

🏝️ Benefits

• Competitive compensation package suitable for an early-stage startup, based on skills, experience, and location.

• Eligibility for bonuses.

• Health insurance with substantial coverage for dependents.

• Flexible paid time off policy.

• Equity options.

• Fully remote work culture with a team based primarily in Seattle.

• Participation in company summits twice a year.