Software Engineer - ML Platform (Staff / Sr Staff)

This position is posted by Jobgether on behalf of a partner company. We are currently looking for a Software Engineer - ML Platform (Staff / Sr Staff) in the United States.

This role is designed for an experienced software engineer passionate about operationalizing machine learning at enterprise scale. You will join a fast-growing, mission-driven environment, developing and maintaining a robust ML platform that enables data scientists to experiment, deploy, and monitor forecasting and optimization models efficiently. You will collaborate closely with data, infrastructure, and product teams to ensure seamless integration of ML workflows and deliver high-impact solutions for internal and external stakeholders. This position requires a balance of deep technical expertise, creativity, and problem-solving skills to drive platform innovation and scalability. Your contributions will help shape the future of AI/ML deployment in a high-performance, collaborative environment.

Accountabilities:

• Design, implement, and maintain frameworks for model development, experimentation, and deployment at scale.
• Abstract complexities behind ML workflow orchestration to accelerate the model development lifecycle.
• Integrate with data and compute infrastructure to optimize resource utilization and platform performance.
• Implement automated testing, monitoring, and observability for ML models in production.
• Collaborate with data scientists to translate model requirements into robust, validated, and scalable solutions.
• Partner with product and delivery teams to extend platform capabilities to external customers while maintaining security best practices.
• Stay current with ML engineering advancements and incorporate best practices into the platform.

• 5+ years of professional experience in Python software development and ML platform engineering.
• Familiarity with CI/CD, containerization, orchestration, and workflow tools (Docker, Kubernetes, Argo, Metaflow, Pants).
• Strong understanding of data pipelines, ETL processes, and data infrastructure.
• Experience operationalizing machine learning workflows and model deployment.
• Proficiency with ML libraries and frameworks (scikit-learn, PyTorch, XGBoost, pandas, polars, pandera).
• Experience with observability tooling such as Grafana, Prometheus, or Honeycomb.
• Ability to collaborate effectively in remote-first, cross-functional teams across multiple time zones.
• Familiarity with agile methodologies and a willingness to adapt to new workflows.
• Commitment to clean energy and climate change initiatives is highly desirable.
• Nice-to-have: advanced degree in computer science or ML, time series forecasting experience, Databricks/Spark, Dagster, or energy sector background.