Sr. Machine Learning Engineer

This position is posted by Jobgether on behalf of a partner company. We are currently looking for a Sr. Machine Learning Engineer in the United States.

This is a high-impact engineering role at the intersection of machine learning, data infrastructure, and large-scale production systems. You will be responsible for designing and owning the core ML infrastructure that powers data-driven decision-making across millions of daily user interactions. Working closely with data scientists, data engineers, and product teams, you will transform experimental models into scalable, production-grade systems. The role requires deep technical expertise in building robust ML pipelines, feature stores, and cloud-native architectures. You will play a critical role in shaping the future of the data platform, ensuring reliability, scalability, and performance of machine learning systems in production. This position is ideal for a hands-on engineer who thrives in complex, high-scale environments and enjoys bridging research and production engineering.

• Architect and own the end-to-end machine learning infrastructure, ensuring scalable and production-ready systems.
• Partner with data science teams to productionize models and transition algorithms from research to real-world applications.
• Design, build, and maintain feature stores (offline and online) to support real-time and batch model inference.
• Develop and optimize ML pipelines and data workflows using modern cloud-native architectures.
• Collaborate with data engineering teams to enhance data lake, ETL, and streaming data infrastructure.
• Lead system monitoring, observability, and performance optimization for production ML models.
• Contribute to architectural decisions and define best practices for scalable data and ML systems.
• Ensure reliability, fault tolerance, and efficiency across all machine learning services in production.
• Support cross-functional collaboration by translating data science needs into scalable engineering solutions.

Requirements:

• 5+ years of experience in Machine Learning Engineering, with strong focus on production systems and data engineering.
• Strong expertise in AWS cloud services (e.g., SageMaker, DynamoDB) and infrastructure-as-code tools such as Terraform, CDK, or CloudFormation.
• Deep experience with containerization and orchestration technologies including Docker and Kubernetes.
• Strong programming skills in Python and experience with ML frameworks such as TensorFlow, PyTorch, or Scikit-learn.
• Advanced knowledge of ETL pipelines, database systems, and large-scale data processing.
• Experience with big data and distributed systems such as Snowflake, Databricks, or Kafka is highly desirable.
• Strong understanding of SQL and data modeling for analytical and operational use cases.
• Proven ability to collaborate across data science, engineering, and product teams.
• Strong problem-solving skills with a focus on scalability, reliability, and performance.
• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field (or equivalent experience).

Benefits:

• Competitive annual salary ranging from $164,000 to $194,000
• Fully remote work setup with all necessary tools and equipment provided
• Unlimited paid time off (PTO) for flexibility and work-life balance
• Comprehensive medical, dental, and vision insurance coverage
• 401(k) retirement plan through Charles Schwab
• Health Savings Account (HSA), Flexible Spending Account (FSA), and Limited FSA options
• Company-paid short-term and long-term disability insurance and basic life insurance
• Paid parental leave for maternity and paternity support
• Employee Assistance Program (EAP) offering mental health, legal, and financial support services
• Wellness benefits including access to a wellness coaching app for employees and family members
• Employee discount program with savings across travel, retail, and services
• Paid volunteer time off to support community engagement