Senior ML Engineer (Client Solutions)
New
A
AZXEnterprise AI
Fully remote culture with a cluster of teammates in SeattleFull-TimeSenior
Salary$140K - $230K
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Job Details
- Experience
- 5+ years
- Required Skills
- DockerPostgreSQLPythonSQLMachine LearningTypeScriptData engineeringFastAPIReact
Requirements
- 5+ years of shipping applied machine learning to production including forecasting, detection/classification, or optimization.
- Strong data engineering skills to discover, clean, and profile data independently.
- Rigorous validation discipline including chronological splits, walk-forward validation, and as-of correctness.
- Software engineering proficiency: Python, SQL, tests, Docker, scheduling, API/app surfaces, and monitoring.
- Client-facing capability: experience running discovery, leading demos, and advising stakeholders.
- Practical fluency with Python 3.12+, pandas, polars, DuckDB, scikit-learn, statsmodels, and gradient boosting.
- Experience with SQL/Postgres (TimescaleDB/PostGIS) and time-series feature engineering.
- Ability to build model interfaces using FastAPI and basic React/TypeScript.
- Experience deploying with Docker and basic cloud tooling (Azure/AWS).
- Working fluency with LLMs for extraction and retrieval tasks.
- Bachelor's Degree required; Master's is a plus.
- Must be able to travel 2x/year for company summits.
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.
- Backtest and evaluate models to support operational decisions and defend precision/recall tradeoffs.
- Design systems that distinguish 'no prediction' from 'wrong prediction' for end users.
- Ship usable model capabilities using FastAPI, React surfaces, and scheduled jobs.
- Own the measurement story by defining baselines and KPIs and performing post-deployment readouts.
- Maintain client-facing engineering presence by running discovery, working sessions, and demos.
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