Senior Machine Learning Platform Engineer

Quick Facts

• Role: Senior Machine Learning Platform Engineer

• Focus: Cloud-native ML infrastructure for building, training, deploying, and operating production models at scale

Description

Lead the evolution of a Kubernetes-based, cloud-native machine learning platform. Design and optimize distributed ML workflows (including MetaFlow), GPU workloads, LLM hosting, and tooling that helps move research into production. Build the backend services and APIs that manage the ML lifecycle, while improving reliability, observability, security, and cloud cost efficiency.

Responsibilities

• Lead design and evolution of a Kubernetes-based ML platform for large-scale training and deployment

• Design, implement, and optimize distributed ML workflows using MetaFlow and other cloud-native technologies

• Enable reproducible experimentation, automated training, artifact management, and production deployment

• Develop infrastructure for GPU-based workloads for traditional ML, foundational models, and agentic pipelines

• Build backend services and APIs for machine learning lifecycle management

• Evaluate and integrate open-source technologies to improve productivity, reliability, scalability, and operations

• Collaborate with AI scientists to transition prototypes into production-quality systems

• Improve observability, reliability, security, and cloud cost efficiency

• Mentor engineers and contribute to technical strategy and best practices

Benefits

• Opportunity to work on challenging engineering problems at the intersection of distributed systems, cloud infrastructure, ML, and generative AI

• Minimum full-time salary range: $135,000–$155,000 (non-bonus eligible)

Requirements

• Bachelor’s or Master’s degree in Computer Science/Software Engineering or related field, or equivalent experience

• Strong distributed systems experience

• Expert-level Python

• AWS cloud-native application development experience

• Kubernetes and container technologies experience

• REST APIs and microservice architecture experience

• SQL and NoSQL database experience

• CI/CD pipeline, Git workflows, and automated testing experience

• Strong problem-solving and collaboration skills