Principal Machine Learning Engineer driving technical direction for core Snap products. Set vision and develop ML technology for millions of Snapchatters.
About the role
- Machine Learning Engineer II developing machine learning solutions for supply chain optimization at Grainger. Collaborating with data scientists and engineers to build robust data pipelines and ML models.
Responsibilities
- Partner with data scientists and data engineers to develop, deploy, and maintain machine learning solutions, from data pipelines to production model serving.
- Build scalable, efficient, and automated processes for large-scale data analysis, model development, validation, and deployment.
- Design and maintain ETL pipelines and workflow orchestration to support production ML systems.
- Deploy and operate machine learning workloads and services on containerized infrastructure (AWS, Kubernetes).
- Automate critical system operations and improve reliability, observability, and performance of ML systems.
- Explore and evaluate emerging technologies and tools to improve ML development velocity and platform capabilities.
- Provide technical support to platform users throughout the ML development lifecycle and assist in resolving production issues.
- Develop documentation and best practices to help users more effectively leverage ML systems and tools.
Requirements
- Master’s degree in computer science, data science, analytics, or a related technical field required.
- 2+ years of experience developing, deploying, and maintaining production machine learning or data-intensive software systems using Python.
- Strong software engineering fundamentals, including version control, testing, and CI/CD practices.
- Experience working with containerized environments (Docker, Kubernetes).
- Experience deploying or supporting machine learning models in production, including batch and/or real-time inference.
- Familiarity with AWS services such as S3, ECR, Secrets Manager, or similar cloud platforms.
- Experience building data pipelines and automating workflows using orchestration tools (e.g., Airflow, Astronomer).
- Working knowledge of databases and data querying (e.g., SQL, Snowflake, DuckDB).
- Understanding of core machine learning concepts and the model development lifecycle, including time series forecasting, clustering, and operations research–based optimization models (e.g., Gurobi, Pyomo).
- Strong communication and collaboration skills, with the ability to work effectively across engineering and data science teams.
- Self-directed, curious, and motivated to learn and apply new technologies.
Benefits
- Medical, dental, vision, and life insurance plans with coverage starting on day one of employment and 6 free sessions each year with a licensed therapist to support your emotional wellbeing.
- 18 paid time off (PTO) days annually for full-time employees (accrual prorated based on employment start date) and 6 company holidays per year.
- 6% company contribution to a 401(k) Retirement Savings Plan each pay period, no employee contribution required.
- Employee discounts, tuition reimbursement, student loan refinancing and free access to financial counseling, education, and tools.
- Maternity support programs, nursing benefits, and up to 14 weeks paid leave for birth parents and up to 4 weeks paid leave for non-birth parents.
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