Senior Machine Learning Engineer, AI Platform

Mozilla Corporation is a non-profit-backed technology company that has shaped the internet over the last 25 years, focusing on diverse areas including AI and security. They are seeking a Senior Machine Learning Engineer to design, build, and operate the AI platform, ensuring efficient and secure deployment of machine learning models across their products.

Responsibilities:

• 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
• 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