3 hours ago
Base Salary
$140k - $225k/yr
Responsibilities
- Own the end-to-end ML delivery lifecycle, including data discovery and cleaning, modeling, evaluation, client-environment deployment, scheduling, monitoring, and retraining policy.
- Build forecasting and detection models that handle real-world data quality issues such as late feeds, revised rows, and missing labels.
- Backtest models, perform rigorous evaluation, and explain precision/recall tradeoffs to operational stakeholders.
- Design systems that distinguish missing predictions from incorrect predictions for end users.
- Build usable product surfaces such as FastAPI services, React interfaces, and scheduled jobs.
- Define baselines and KPIs, instrument monitoring, and deliver post-deployment readouts with clearly stated attribution limits.
- Run client discovery, working sessions, and demos with client IT and data teams while feeding client-specific data shapes and failure modes back to the platform team.
Requirements
- 5+ years of experience shipping applied machine learning to production in areas such as forecasting, time-series detection or classification, survival/reliability modeling, or optimization.
- Strong data engineering skills, including finding, cleaning, joining, and profiling data independently at awkward scale.
- Experience with chronological splits, walk-forward validation, as-of correctness, and rigorous evaluation practices.
- Enough software engineering experience to ship tested, type-strict, reviewable production systems using Python, SQL, Docker, scheduling, APIs or application surfaces, and monitoring.
- Client-facing capability, including leading discovery and demos, challenging incorrect requests, and proposing alternatives.
- Judgment to determine when machine learning is not the appropriate solution.
- Practical experience with Python 3.12+, pandas or polars, DuckDB, scikit-learn, statsmodels, gradient boosting, SQL, Postgres, TimescaleDB or PostGIS, and time-series feature engineering and validation.
- Experience building usable model interfaces with FastAPI and React/TypeScript and deploying with Docker and basic Azure or AWS cloud tooling.
- Working fluency with LLMs for extraction and retrieval use cases.
- Bachelor’s degree required; master’s degree preferred.
- Domain experience in energy, utilities, infrastructure, or commercial real estate is preferred.
- Must be authorized to work full-time in the United States; AZX cannot sponsor or take over employment visa sponsorship.
Benefits
- Competitive early-stage startup compensation based on capabilities, experience, and location, plus bonus eligibility and equity.
- Health insurance with meaningful dependent coverage.
- Flexible paid time off.
- Fully remote culture with a cluster of teammates in Seattle.
- Travel required twice per year for company summits.
- Interview process includes a written take-home assignment and a live two-hour technical session.
- Applicants may apply to a maximum of two roles at a time, and applying to more than two roles within six months results in automatic disqualification.
Tech Stack
Categories
Forward DeployedML Engineering
About AZX
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. AZX was created to meet these challenges head-on. We deliver AI transformation for critical industries providing strategy, execution and acceleration solutions. We prioritize code over powerpoint, applying start-up principles to challenge conventional thinking and develop strategic AI technology and business solutions that can be implemented quickly. We are mission-driven and client-centric, solving hard problems and building value for people, organizations and the planet. We are AZX.