As a Staff Machine Learning Engineer at Steadily, 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 have outsized influence on our architecture, tooling, and ML strategy. This is a true end-to-end role where you’ll own researching, 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.
🏢 About Steadily
Steadily is an early-stage, fast-growing company where you’ll wear a lot of hats and shape product decisions. Our founders have three successful startups under their belt and have recruited a stellar team to match. We’re growing fast, we manage over $20 billion in risk, and we’re exceptionally well-funded.
🎯 The Role
You will 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).
✅ Key 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.
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: 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.
Partner closely with Engineering, Product, Operations, and Business teams to design reliable solutions across systems and ensure your models are solving real-world problems.
Provide excellent metrics and visibility into model quality, bias, and performance to assess how it’s helping the business, ensuring a high bar of scientific rigor and evaluation.
📌 Required Qualifications
Experienced: 5+ years experience applying Machine Learning methods to production problems.
Full-stack with data: You're comfortable starting from a raw, unrefined data source that no one has previously worked with, building the lightweight pipeline or dbt table to make it usable, and carrying it all the way through feature engineering, modeling, and deployment.
Builder with a Business Mindset: You like the product-side of data and think about how to apply modeling and evaluation techniques to real-world problems.
Pragmatic: You prioritize impact and delivery, balancing speed and quality, making thoughtful trade-offs to solve problems effectively.
Curious: You are not just an order-taker. You are curious about what makes the business tick and you learn the intricacies of how it runs.
⭐ Desirable Experience
Actuarial experience, or experience applying models to risk evaluation and aggregation problems.
Experience in computer vision and image analysis.
Experience with dbt or similar modern data transformation tools.
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