Senior ML Engineer; Client Solutions

Our mission is to accelerate positive impact in critical industries through AI transformation. We specialize in physics-informed ML and enterprise AI solutions that directly address climate and sustainability challenges.

We’re growing quickly and already work with category-leaders in real estate (CBRE), energy (Level Ten Energy), logistics (Flexe) and utilities.

We bootstrapped profitably for our first year and are now backed by leading investors focused on AI, climate and energy.

We work on challenges in clean energy, decarbonization, climate risk, energy systems, and global economics. We’re building our company for long-term success and aim to create the ultimate place to work for those passionate about AI and making a positive impact.

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 Dev Ops, some infrastructure, some front end and back end — because you are the engineering face of AZX to your client.

• 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.

• 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, stats models, gradient boosting), SQL/Postgres (with Timescale DB/PostGIS for grid work), and time-series feature engineering and validation.
• Comfort…