Machine Learning Infrastructure Engineer

We are hiring on behalf of our client, a global innovator in fitness and wellness technology. Their mission is to empower people to live fit, strong, long, and happy lives by delivering integrated experiences to millions of members anytime, anywhere.

We are looking for a Machine Learning Infrastructure Engineer to join the Personalization team, which owns the systems powering content recommendations across the company’s digital ecosystem. In this role, you will design, build, and maintain low-latency, highly scalable services that make real-time personalization possible. You will work hands-on with backend services, cloud infrastructure, model serving, observability, and performance optimization, partnering closely with ML Engineers, API Engineers, Platform Engineers, and Product Managers to bring ML-powered product features into production.

• 3+ years of professional software engineering experience and a degree in Computer Science, Engineering, or a related technical field.
• Strong software engineering fundamentals, including data structures, algorithms, clean code, testing, and reproducibility.
• Professional experience building backend services in Python; experience with Java, Kotlin, Go, C, or C++ is welcome.
• Experience designing and building RESTful APIs, gRPC services, or microservices from the ground up.
• Strong experience deploying and managing production services on AWS or GCP, or Azure.
• Experience with relational and non-relational databases such as Postgres, MySQL, DynamoDB, or Redis.
• Experience with event-driven architectures and message queues such as Kafka, RabbitMQ, or SQS.
• Strong debugging, profiling, and performance tuning skills, including latency tracking, scalability analysis, and production troubleshooting.

• Design, build, and maintain Python microservices powering personalized content recommendations.
• Productionize, deploy, monitor, and operate machine learning services in cloud-based production environments.
• Partner with ML Engineers to integrate models into scalable backend services and real-time recommendation workflows.
• Ensure high availability, low latency, and strong performance through caching, load balancing, auto-scaling, and capacity planning.
• Own and improve personalization services, including reliability, testability, observability, scalability, and operational readiness.
• Conduct performance tuning, profiling, and latency optimization for high-traffic recommendation workloads.
• Collaborate with platform teams to use infrastructure, tooling, and deployment workflows that support fast product iteration.
• Work with Product Managers, ML Engineers, API Engineers, and Data Engineers to launch ML-powered personalization features.