Senior Machine Learning Engineer, AI Platform

Details

• 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.
• 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.
• Generous performance-based bonus plans to all eligible employees - we share in our success as one team
• Rich medical, dental, and vision coverage
• Generous retirement contributions with 100% immediate vesting (regardless of whether you contribute)
• Quarterly all-company wellness days where everyone takes a pause together
• 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. - varies by country)