Staff Machine Learning Engineer

Join our fast-growing company as a Staff Machine Learning Engineer. In this key technical role, you will identify trends and insights across large data sets to improve our product outcomes and operations. You will own the end-to-end ML lifecycle, build and maintain the data layer, drive measurable business impact, and partner closely with various teams. This position offers flexible time off, continued education opportunities, a 401K, employer healthcare contribution, and equity in the company.

• Identifying trends and insights across large data sets to improve product outcomes and operations.
• Owning the end-to-end ML lifecycle, including designing, building, deploying, and evolving data sets and models.
• Building lightweight data pipelines and new dbt tables to get raw data model-ready, without owning heavy ETL infrastructure.
• Pragmatic: We prioritize impact and delivery. You balance speed and quality, making thoughtful trade-offs to solve problems effectively. You leverage off-the-shelf solutions (and foundational models) so we don’t reinvent the wheel, but you understand when a custom solution is appropriate
• 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. You aren't just interested in the research; you have thoughtful opinions about where the data leads and how to maximize the business impact of your work
• 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. This results in strong intuition for when an analysis is wrong and leads you to suggest ideas and insights that nobody thought to ask for. You’re not the type of engineer who wants fully fleshed-out specs thrown over the wall for you to implement
• Experienced: 5+ years experience applying Machine Learning methods to production problems. We expect you to be able to dive into a complex codebase without too much spin-up. Past experience as a team lead or owning end-to-end deployment is definitely a plus
• 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
• Experience with dbt or similar modern data transformation tools
• Experience in computer vision and image analysis
• Actuarial experience, or experience applying models to risk evaluation and aggregation problems