Senior Machine Learning Engineer, Forecasting

Responsibilities

• Collaborate with engineering, product, and data science teams to identify business challenges suited to machine learning and AI solutions.
• Develop tools and automate manual processes to improve operational efficiency, experimentation velocity, and reliability.
• Build, integrate, and monitor end-to-end lifecycles for large-scale distributed machine learning systems.
• Investigate model performance and identify data-quality and system-performance issues.
• Enhance the forecasting ML pipeline, including weekly automated model retraining and deployment across production models.
• Improve team capabilities in MLOps, deployment automation, and production machine learning systems.
• Drive projects from concept through production and maintain and continuously improve their reliability.

Requirements

• At least 5 years of experience building and maintaining production machine learning systems.
• Deep expertise in MLOps, deployment automation, and model-serving infrastructure.
• Strong software engineering skills with proficiency in Python, Docker, Kubernetes, GitHub Actions, ArgoCD, Terraform, and Helm.
• Experience with model-training orchestration, automated retraining pipelines, and A/B testing or variant management using Dagster, Airflow, or similar tools.
• Experience designing scalable microservices, APIs, and complex service dependencies.
• Ability to collaborate with data scientists to productionize research, backend teams on API integration, and product teams on customer needs.
• Experience writing tested, maintainable, and well-documented code.
• Preferred experience in healthcare or other regulated industries, forecasting or time-series algorithms, computer vision, DAG frameworks, or Flink.

Benefits

• Competitive salary and stock options.
• Flexible vacation policy.
• Remote-first work environment with virtual and in-person team events.
• Comprehensive health, dental, and vision insurance.
• 16 weeks of parental leave for all parents.