Senior Machine Learning (ML) Engineer (AI Insurtech)

EvolutionIQ builds technology for insurance claims teams. The Senior Machine Learning Engineer develops and deploys production ML models, including claim time series forecasting and NLP, and translates client business problems into scalable machine learning solutions.

Responsibilities:

• Build ML models for production use
• Perform in-depth exploratory data analysis to scope ML opportunities, identify potential issues in the data, and lay the groundwork for model design, training & evaluation strategies
• Suggest and implement new features to improve model performance and business logic
• Write and review production-quality PRs daily
• Translate client business problems into ML problems
• Build scalable machine learning models for claim time series forecasting and NLP text understanding
• Continue to grow and learn in the data & ML space with a focus on business problems that require making sense of large-scale structured and unstructured datasets

Requirements:

• 2+ years of experience as a Machine Learning Engineer or Data Scientist
• deep exposure to data science & ML tech stacks (sklearn, Keras / PyTorch, tuning frameworks), data analysis & modeling, feel at home in Python/Pandas/Jupyter, and strive for scalable technical concepts and solutions
• excellent written and verbal communication skills
• can effectively correspond between technical and non-technical audiences
• a team player who is solutions-oriented
• preferably in highly scalable production environments
• Experience leveraging Claude Code and related tools
• Knowledge of Dagster, BigQuery, Vertex AI, GCP, Terraform, Spark, Kubernetes
• Owned and deployed, and maintained model-serving pipelines in production
• Expert skills at writing clean, efficient, and easy-to-understand code with unit tests and functional design patterns
• Experience with advanced statistical modeling, especially probabilistic programming
• Ambitious, collaborative, and empathetic values
• A work style that is open to giving and receiving critical feedback and collaborating effectively across teams