About This Role:We are seeking an ML Engineer who builds ML systems directly inside client environments. Your job starts with the client's actual data spread across multiple systems - and ends with a model running on a schedule inside their environment. You will bring a strong area of expertise, but expect to wear many hats as part of a small team - some DevOps, some infrastructure, some front end and back end - because you are the engineering face of AZX to your client.
Responsibilities:- Own the full ML delivery lifecycle: data discovery and cleaning, modeling, evaluation, deployment into the client environment, scheduling, monitoring, and retraining policy.
- Build forecasting and detection models that hold up against real-world data quality issues (late feeds, revised rows, missing labels).
- Backtest and evaluate models honestly enough to stake real operational decisions on them, and defend your precision/recall tradeoffs to the people who bear the cost of false alarms.
- Design systems that distinguish "no prediction" from "wrong prediction," so a missing answer reads differently to the end user than an incorrect one.
- Ship enough product to make the model usable - a FastAPI service, a small React surface, a scheduled job - whatever "usable capability" means for that client.
- Own the measurement story: agree on baselines and KPIs before deployment, instrument for monitoring, and deliver a post-deployment readout with attribution limits clearly stated.
- Maintain client-facing engineering presence and a feedback loop into the platform team - running discovery, working sessions with client IT/data teams, demos, and surfacing the data shapes and failure modes only visible from inside client data.
Core Qualifications:- 5+ years of shipping applied machine learning to production - forecasting, detection/classification on time series, survival/reliability modeling, or optimization - with an evaluation you defended to someone whose job depended on it.
- Strong data engineering skills and willingness to use them: you find, clean, join, and profile data yourself at awkward scale, without a dedicated data team.
- Rigorous validation discipline - chronological splits, walk-forward validation, as-of correctness, and an instinct to be suspicious of a suspiciously good metric.
- Enough software engineering to ship real systems: Python, SQL, tests, Docker, a scheduler, an API or app surface, and monitoring - type-strict, tested, reviewable code, even in a pod of two.
- Client-facing capability and the assertion to use it - running discovery, leading demos, and pushing back early and plainly when an ask is wrong, with an alternative already in hand.
- Judgment about when ML is the wrong tool, and the willingness to say so to a client who wants AI regardless.
- Practical fluency with our core stack - Python 3.12+ (pandas/polars/DuckDB, scikit-learn, statsmodels, gradient boosting), SQL/Postgres (with TimescaleDB/PostGIS for grid work), and time-series feature engineering and validation.
- Comfort building the surfaces that make a model usable - FastAPI plus enough React/TypeScript to expose results - and deploying it with Docker and basic cloud tooling (Azure/AWS).
- Working fluency with LLMs for the agentic edges of client work (extraction, retrieval) - depth isn't required, but honesty about your actual experience is.
- Bachelor's Degree; Master's is a Plus
- Domain experience in Energy, Utilities, Infrastructure, and Commercial Real Estate is a plus
Why AZX! - Be part of a fast-growing, profitable, mission-driven company with industry-leading clients tackling the massive opportunity of AI transformation in critical industries.
- Competitive early-stage startup compensation (based on capabilities, experience, and location)
- Bonus eligibility
- Health insurance with meaningful coverage for dependents
- Flexible paid time off
- Equity
- Fully remote culture with a cluster of teammates in Seattle
Additional Information:- Must be able to travel 2x/year for company summits
- Applicants must be currently authorized to work in the United States on a full-time basis.
- We are unable to sponsor or take over sponsorship of employment visas at this time.
- Please note that our interview process includes a written take-home assignment followed by a live two-hour technical session with our engineering team, so if that format isn't a good fit, we'd ask that you not apply
- Please only apply to a maximum of 2 roles at a time, any applicants who apply to more then 2 roles within a 6 month period will automatically be disqualified
Next Steps:If this job sounds like a great fit but you don't check
ALL of these qualification boxes, we'd still love to hear from you!
