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Mozilla Logo Mozilla · IT - Software Development

Senior Machine Learning Engineer, AI Platform

United States; Canada Employment contract 139000.00 - 218000.00 USD / Year · Job Posted June 16, 2026
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Job Description

The AI Platform team is responsible for building the foundational infrastructure that powers intelligent experiences across Mozilla products. This includes model training pipelines, high-throughput inference services, GPU orchestration, and secure, privacy-respecting AI systems that operate reliably at global scale. We’re looking for a Machine Learning Engineer with a strong platform mindset to help design, build, and operate Mozilla’s AI platform.

Job Responsibility

  • Design, build, and operate core AI platform components used to train, deploy, and serve machine learning models in production environments
  • Own model serving and inference workflows end-to-end, driving improvements in reliability, scalability, performance, and operational excellence
  • Lead efforts to optimize inference systems for throughput, latency, and cost efficiency across CPU and GPU workloads
  • Design and manage GPU-based inference and training workloads, including performance tuning, capacity planning, and resource utilization optimization
  • Own and improve critical parts of the model lifecycle, including packaging, versioning, testing strategies, validation, and deployment automation
  • Implement and evolve observability practices (metrics, logging, tracing, alerting) to improve visibility and operational resilience of ML services and pipelines
  • Partner closely with product, infrastructure, security, and data teams to design scalable platform capabilities that enable AI-powered features
  • Contribute to technical design discussions, propose architectural improvements, and mentor junior engineers through code reviews and knowledge sharing
  • Participate in and help improve operational processes, including incident response, on-call rotations, and post-incident reviews

Requirements

  • Bachelor’s degree with 4–6 years of relevant industry experience, or Master’s degree with significant hands-on experience building and operating production ML systems, or work experience equivalent
  • Strong experience developing in Python for machine learning systems, backend services, or distributed data processing
  • Proven experience deploying and operating ML workloads in cloud environments, including production-grade infrastructure
  • Solid understanding of model serving architectures, inference pipelines, and performance tradeoffs (latency, throughput, cost, scaling strategies)
  • Hands-on experience working with GPU-based workloads and accelerated computing in production settings
  • Experience designing CI/CD pipelines and development workflows that support reliable ML system deployment
  • Ability to independently scope and drive technical initiatives while balancing product and operational priorities
  • Strong problem-solving skills and the ability to debug performance and reliability issues in distributed systems
  • Clear and effective communication skills, with experience collaborating across engineering, product, and infrastructure teams

Nice to have

  • Experience implementing inference optimization strategies such as batching, quantization, compilation, model conversion, or hardware-specific tuning
  • Familiarity with containerization and orchestration systems (e.g., Docker, Kubernetes) in production environments
  • Experience designing observability systems for distributed services, including metrics strategy and performance profiling
  • Exposure to privacy-preserving ML techniques, security best practices, or responsible AI system design
  • Contributions to open-source ML infrastructure projects or leadership in building reusable internal ML tooling

What we offer

  • Generous performance-based bonus plans
  • Rich medical, dental, and vision coverage
  • Generous retirement contributions with 100% immediate vesting
  • Quarterly all-company wellness days
  • Country specific holidays plus a day off for your birthday
  • One-time home office stipend
  • Annual professional development budget
  • Quarterly well-being stipend
  • Considerable paid parental leave
  • Employee referral bonus program
  • Other benefits (life/AD&D, disability, EAP, etc.)
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