Senior Machine Learning Engineer

About the role

Overview

Location: Austin, TX (4 days in-office)

Employment Type: Full-time

Department: Engineering & Product

RELOCATION ASSISTANCE PROVIDED

As a Senior Machine Learning Engineer:

You will play a key technical role on our Engineering team, identifying trends and insights across large data sets to discover where refined data or internal ML/AI models can improve our product outcomes and operations.

As the second engineer joining our dedicated ML team, you will add input on our architecture, tooling, and ML strategy. Because we are a fast-growing, agile company, this is a true end-to-end role. You’ll own building, evaluating, and deploying your models to production, as well as monitoring them for quality and accuracy over time. We operate across data types including public, proprietary, and a large volume of image data.

Because we currently operate without a dedicated Data Engineering team, you will also own the data layer for your models. In practice, that means you'll often be the first person to touch a given raw data source; you're comfortable going from an unrefined, previously unexplored data set through feature engineering and into a production ML model. You can expect roughly a 30% data pipeline / new dbt table building (lightweight, not heavy ETL) and 70% feature engineering, modeling, deployment, and monitoring split in your day-to-day work.

You’ll operate with a high degree of autonomy and serve as a trusted technical owner for business problems across the organization. Steadily is still early in our exploration of where AI/ML models can drive the biggest value, making this role ideal for engineers who thrive in ambiguous environments and want their technical work to translate directly into massive business impact.

This is a full-time position based in our Austin, TX office (4 days a week in-office). We are located near Mopac and W. Anderson Lane.

Job Responsibilities

- Own the end-to-end ML lifecycle: Design, build, deploy, and evolve data sets and models with an emphasis on scalability, quality, and maintainability. Focus areas could include estimating property-level risk, accurately assessing costs, and using aerial image analysis or modeling techniques to identify attributes that feed into other models.

- Build and maintain the data layer: Build lightweight data pipelines and new dbt tables to get raw data model-ready, without owning heavy ETL infrastructure.

- Drive measurable business impact: Lead the exploration and implementation of new ML applications in our product ecosystem to better predict risk on a per-insured level and in aggregate across the entire portfolio.

- Write clean, maintainable code in our stack with the help of AI: We build on an event-driven architecture using Kafka, AWS (EKS), Python, Django/FastAPI, and Postgres, with a full CI/CD pipeline via GitHub Actions. You will set a high bar for engineering quality and architectural design within this ecosystem.