# Senior ML Engineer (Client Solutions) 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: United States
- 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/c734e489-684f-4431-abdc-72ad57e0c737
- Page: https://www.1752.vc/careers/jobs/azx-senior-ml-engineer-client-solutions/

## About the 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.

## What they're looking for

- 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

Tags: Engineering