5 months ago
Base Salary
$180k - $230k/yr
Responsibilities
- Build and own scalable infrastructure for training underwriting machine learning models.
- Develop the API and serving layer for real-time quotes with strong latency, reliability, and scalability.
- Make model training reliable, reproducible, and scalable as data volumes and model complexity increase.
- Build tooling for feature testing, experiment tracking, backtesting, and model validation.
- Enable data scientists and actuaries to move ideas from experimentation to production more efficiently.
- Own systems end-to-end, establish standards, and operate production infrastructure including monitoring and on-call support.
- Collaborate with data scientists and actuaries and contribute hands-on to modeling and data science.
Requirements
- Strong track record building production ML or data infrastructure at scale.
- Deep proficiency in Python and experience processing large datasets with Spark, Databricks, or equivalent tools.
- Experience with production model training pipelines and/or low-latency model serving.
- Experience building infrastructure or tooling that accelerates data scientists, researchers, or developers.
- Ability to own systems end-to-end and operate reliable production infrastructure.
- Interest in working directly with modeling and data science rather than only supporting infrastructure.
- Preferred: experience in regulated industries such as healthcare or insurance.
- Preferred: experience with MLOps tooling such as MLflow, feature stores, or experimentation platforms.
- Preferred: experience supporting data science or actuarial teams in production environments.
Benefits
- Equity is included in the compensation package.
- High ownership and responsibility from day one.
- Opportunity to work on healthcare access and affordability at scale.
- Growth opportunities as the company and role scope expand.
- Collaborative, high-pace, high-trust working environment.