Hybrid ML Ops Engineer II

at EEOC

• Job title: ML Ops Engineer II
• Job type: Full Time
• Experience level: Junior, Mid level
• Salary: $97,000 - $149,000 per year
• Degree requirement: Bachelor's Degree
• Tech skills: AWS, Docker, Kubernetes, Python
• Location requirements: Hybrid, San Francisco, United States

About the role

• ML Ops Engineer at Early Warning Services supporting predictive models through scalable infrastructure and tools. Collaborating with teams to enhance model productionalization and monitoring efficiency.

Responsibilities

• Designs, builds, and maintains scalable ML infrastructure and pipelines for model training, deployment, and monitoring.
• Optimize orchestration processes to ensure efficient deployment and management of predictive models.
• Monitors and maintains the performance, security, and scalability of the ML infrastructure.
• Collaborates with data scientists and software engineers to streamline the ML lifecycle from development to production.
• Develops and maintains tools for data analysis, experimentation, model versioning, and artifact management.
• Supports data and model governance requirements as needed.
• Creates robust monitoring systems to measure and trend model performance, detect model drift, and ensure optimal performance of models in production.
• Develops automation scripts and tools to improve the efficiency and reliability of MLOps processes.
• Optimizes ML workflows for efficiency, scalability, and reliability.
• Provide technical assistance to all team members; troubleshoots moderately complex issues and escalates issues as necessary.
• Supports the company commitment to risk management and protecting the integrity and confidentiality of systems and data.

Requirements

• Education and experience typically obtained through completion of a Bachelor's degree in Computer Science, Engineering, or a related field
• Typically minimum 2 years’ experience or internship in Data Science, ML Engineering or ML Ops capacity.
• Intermediate programming skills in Python and experience with Data Science and ML packages and frameworks.
• Demonstrated experience with AWS services.
• Intermediate proficiency with containerization technologies (Docker, Kubernetes) and CI/CD practices
• Demonstrated experience with MLOps tools such as MLflow, Kubeflow, or similar platforms.
• Understanding and application of data management, distributed computing, and software architecture principles.
• Proven experience delivering real-time models in production environments.
• Background and drug screen.

Benefits

• Healthcare Coverage – Competitive medical (PPO/HDHP), dental, and vision plans as well as company contributions to your Health Savings Account (HSA) or pre-tax savings through flexible spending accounts (FSA) for commuting, health & dependent care expenses.
• 401(k) Retirement Plan – Featuring a 100% Company Safe Harbor Match on your first 6% deferral immediately upon eligibility.
• Paid Time Off – Unlimited Time Off for Exempt (salaried) employees, as well as generous PTO for Non-Exempt (hourly) employees, plus 11 paid company holidays and a paid volunteer day.
• 12 weeks of Paid Parental Leave
• Maven Family Planning – provides support through your Parenting journey including egg freezing, fertility, adoption, surrogacy, pregnancy, postpartum, early pediatrics, and returning to work.