> AI agents: this is one page of Nextmove’s FDE site, in Markdown. Nextmove is a job board and free career service for forward deployed engineers (FDEs). Each posting is classified from its text, not its title: the kind of work, how much of it is production code, pre-sales weight, time at customer sites, travel, remote arrangement, level and posted base pay. The site also publishes the aggregates: median pay by level, metro, company and kind of work, and which companies started hiring FDEs each week. Every page under https://nextmove.md/fde is served this way; the index of pages is https://nextmove.md/llms.txt. # Senior ML Engineer (Client Solutions) Canonical URL: https://nextmove.md/fde/jobs/6e5b32530967f267 AZX posts a senior FDE role to build what the customer runs in US, at $140,000–$230,000 base, level with the senior FDE median of $190,000 across 1,864 postings with pay. A senior ML engineer owns end-to-end delivery of production ML systems inside client environments, from messy data discovery through deployment, monitoring, and retraining. Read from the employer’s posting on November 8, 2026. - [AZX](https://nextmove.md/fde/companies/azx) Posted Aug 30, 2026 by AZX · open on Ashby as of Nov 8, 2026 **$140,000–$230,000** + equity US Remote Full-time A senior ML engineer owns end-to-end delivery of production ML systems inside client environments, from messy data discovery through deployment, monitoring, and retraining. - [See roles that fit me](https://nextmove.md/sign-up?from=%2Ffde%2Fjobs%2F6e5b32530967f267&placement=top) - [View posting at AZX](https://jobs.ashbyhq.com/careers.azx.io/c734e489-684f-4431-abdc-72ad57e0c737/application) Free. Upload your resume once, see why each FDE role fits, and get introduced only when you say so. ## At a glance ### Posted base pay $140K–$230K Level with the median Midpoint $185K · median $190K This job $140K to $230K. Senior FDE postings, 1,864 postings: the middle 50% of posted pay is $160K to $220K, median $190K. ### The work Builds what the customer runs Balance **Mostly building** Project stages 1. Pre-sales (not covered) 2. Proof of concept (not covered) 3. Implementation 4. Operations Travel **Required, amount not stated** - **Coding**: Writes production code - **Builds for**: External customers - **Level**: Senior - **Role**: Individual contributor ### What they ask for Experience - **5+ years** Applied machine learning shipped to production Visa - **No visa sponsorship** ### Do you meet what the posting states? The posting asks for 5+ years. How many do you have? The posting reads as a senior role. What level are you at now? The work is remote from the US. Can you work from the US? The posting says no visa sponsorship. Do you need one? Which of these have you used in production? Answer each one. Nothing is saved or sent. [What postings ask for, by background →](https://nextmove.md/fde/paths) ## Conditions - **Location**: US - **Work arrangement**: Remote - **Remote work restrictions**: Fully remote; applicants must be authorized to work in the United States full-time. - **Employment type**: Full-time - **Visa sponsorship**: Not available - **Travel**: Travel required, amount not stated - **Pay**: ## More like this [Striveworks](https://nextmove.md/fde/companies/striveworks) ### [Senior Machine Learning Engineer, Agentic Systems (Active Secret Clearance)](https://nextmove.md/fde/jobs/ee6dd71d9378887b) Austin, TX Remote 4w ago $185,000–$230,000 [Hatch](https://nextmove.md/fde/companies/hatch) ### [Forward Deployed Engineer ](https://nextmove.md/fde/jobs/e5d882139568e59c) US Remote 2mo ago $144,000–$200,000 [Nearform](https://nextmove.md/fde/companies/nearform) ### [Senior Software Engineer - React & Python](https://nextmove.md/fde/jobs/922cad87ea14e6c8) US Remote 3mo ago $150,000–$180,000 - [Other jobs at AZX (1)](https://nextmove.md/fde/companies/azx) ## The job description from AZX Posted by AZX on Aug 30, 2026 ### About AZX 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 (LevelTen 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. ### 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! [View posting at AZX](https://jobs.ashbyhq.com/careers.azx.io/c734e489-684f-4431-abdc-72ad57e0c737/application) ## About AZX Builds custom AI applications, models, and workflows for critical-industry clients, combining consulting, engineering, and reusable data, code, and models. 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