Senior Machine Learning Engineer (AI Platform)

Role Overview

Join Mozilla’s AI Platform team as a Senior Machine Learning Engineer (AI Platform). You will design, build, and operate key AI platform components, optimize inference systems, and improve the end-to-end model lifecycle to enable AI-powered features.

Responsibilities

• Design, build, and operate core AI platform components used to train, deploy, and serve machine learning models in production.
• Own model serving and inference workflows end-to-end, improving reliability, scalability, performance, and operational excellence.
• Optimize inference systems for throughput, latency, and cost efficiency across CPU and GPU workloads.
• Collaborate with product, infrastructure, security, and data teams to deliver AI capabilities.

Requirements

• Ability to independently scope and drive technical initiatives while balancing product and operational priorities.
• Clear communication and experience collaborating across engineering, product, and infrastructure.
• Solid understanding of model serving architectures, inference pipelines, and performance tradeoffs (latency, throughput, cost, scaling).
• Experience deploying and operating ML workloads in cloud environments with production-grade infrastructure.
• Strong Python development experience for ML systems, backend services, or distributed data processing.
• Experience with CI/CD pipelines and development workflows supporting reliable ML deployment.
• Hands-on experience with GPU-based workloads and accelerated computing in production.
• Experience implementing inference optimization strategies such as:
  • Batching, quantization, compilation, model conversion, or hardware-specific tuning.
• Familiarity with containerization and orchestration in production (e.g., Docker, Kubernetes).
• Experience designing observability for distributed services (metrics strategy, performance profiling).
• Bachelor’s degree with 4–6 years relevant experience, or Master’s degree with significant hands-on experience building/operating production ML systems (or equivalent).

Nice-to-Haves

• Contributions to open-source ML infrastructure, or leadership in building reusable internal ML tooling.
• Exposure to privacy-preserving ML, security best practices, or responsible AI system design.