The Company

A fast-growing digital health company is transforming how chronic metabolic conditions, including type 2 diabetes and obesity, are managed and prevented.

The company combines AI-powered technology, continuous health data, and personalized clinical care to build a dynamic understanding of each member’s metabolism. By leveraging data from connected devices, wearables, and nutrition tracking, the platform provides highly personalized guidance designed to improve long-term health outcomes.

The business is well-funded, scaling rapidly across the U.S. and internationally, and backed by leading healthcare and technology investors.

The Opportunity

The company is looking for an experienced MLOps Engineer to help build and scale the infrastructure powering its machine learning platform.

This person will sit at the intersection of machine learning, backend engineering, and infrastructure, working closely with AI/ML, Data, and Infrastructure teams to productionize models and build reliable systems supporting training, inference, monitoring, and model management.

The ideal candidate is comfortable working across complex distributed systems, can own projects end-to-end, and has strong experience building production-grade ML infrastructure.

Responsibilities

• Architect, design, and build scalable AI/ML systems for production environments, with an emphasis on backend distributed systems and microservices.
• Lead cross-functional projects from initial scoping through execution, managing timelines, dependencies, and technical alignment.
• Partner closely with AI/ML engineers to improve workflows around model training, real-time inference, monitoring, and troubleshooting.
• Serve as a technical expert on ML infrastructure and provide guidance across engineering teams.
• Own the reliability and operational performance of production ML systems.
• Establish and maintain SLAs around latency, throughput, reliability, and resource utilization.
• Build tooling for model management, continuous monitoring, deployment, and the broader ML lifecycle.
• Mentor other engineers and contribute to engineering best practices.

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related technical discipline.
• 5+ years of industry experience in software engineering, ML infrastructure, MLOps, or related areas.
• Experience designing or working with large-scale, distributed ML systems.
• Strong understanding of MLOps, data structures, and software design principles.
• Strong programming experience with Python.
• Experience with additional backend languages such as Java or Go is valuable.
• Hands-on experience deploying scalable ML models.
• Strong knowledge of Docker, Kubernetes, and microservices architectures.
• Exposure to LLMs and generative AI applications is a major plus.
• Experience with SQL/NoSQL databases and Spark or similar big-data technologies is advantageous.

Compensation

$180,000–$200,000 base salary, plus potential equity and comprehensive benefits.

Benefits

• Competitive compensation package
• Equity participation
• Remote working environment
• Flexible/unlimited vacation policy
• Paid parental leave
• Comprehensive medical, dental, and vision coverage
• Retirement savings plan