Software Engineer, Systems ML

Summary:Meta is seeking a Research Engineer specializing in Systems Machine Learning to help design and build the infrastructure and algorithmic foundations that power large-scale AI systems across Meta's product ecosystem. In this role, you will work at the intersection of machine learning research and systems engineering, developing novel approaches to training efficiency, model serving, distributed computation, and hardware-software co-design. You will collaborate with research scientists and product engineers to translate cutting-edge ML research into production-grade systems that operate at massive scale, directly shaping the performance and reliability of Meta's AI-driven products.Required Skills:Software Engineer, Systems ML Responsibilities:Design and implement scalable systems for distributed ML training and inference, including optimizations across compute, memory, and communication bottlenecksDevelop and evaluate novel techniques for accelerating AI research workflows such as training, inference, RL, evals on latest generation hardware platformsLead the architecture and end-to-end delivery of major systems ML initiatives, coordinating across research scientists, product engineers, and external partnersEstablish performance benchmarking frameworks and profiling pipelines to identify bottlenecks and drive measurable improvements in training throughput and inference latencyDefine service level objectives and reliability standards for ML training and serving systems, building dashboards and runbooks to reduce incident response timeApply AI-assisted development workflows to accelerate implementation, code review, and systems analysis, serving as a model for AI-native engineering practices within the teamCollaborate with cross-functional partners in infrastructure, and product engineering to co-design ML systems that maximize research velocity and researcher experienceMentor other engineers on systems ML best practices, distributed training patterns, and debugging methodologies for large-scale ML infrastructureCommunicate technical trade-offs, architectural decisions, and experimental results clearly to both engineering and research audiences through design documents and presentationsContribute to the broader research community by publishing findings on systems ML advances at leading venuesMinimum Qualifications:Minimum Qualifications:Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experienceBachelor's degree in Computer Science, Electrical Engineering, or a related technical field8+ years of experience in systems engineering, machine learning infrastructure, or a closely related fieldExperience designing and optimizing distributed ML training or inference systems at scale, including proficiency with frameworks such as PyTorch, JAX, or TensorFlowExperience with low-level systems programming in C++ or CUDA, including performance profiling, kernel optimization, or compiler-level ML optimizationsExperience leading the technical design and delivery of complex, cross-functional systems ML projects from inception through production deploymentExperience using data-driven methods and experimentation to evaluate and validate systems performance improvementsPreferred Qualifications:Preferred Qualifications:Master's or PhD degree in Computer Science, Electrical Engineering, Machine Learning, or a related technical fieldTrack record of publishing research on systems ML topics at venues such as MLSys, OSDI, SOSP, NeurIPS, or ICMLDemonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologiesDemonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)Experience with ML compiler stacks such as MLIR, XLA, TVM, or Triton, and familiarity with hardware-software co-design for AI acceleratorsExperience building automated tooling or frameworks that improve engineering efficiency across ML infrastructure teamsExperience with model parallelism strategies including tensor parallelism, pipeline parallelism, and expert parallelism for large-scale model trainingPublic Compensation:$183,997/year to $257,000/year + bonus + equity + benefitsIndustry: InternetEqual Opportunity:Meta is proud to be an Equal Employment Opportunity and Affirmative Action employer. We do not discriminate based upon race, religion, color, national origin, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender, gender identity, gender expression, transgender status, sexual stereotypes, age, status as a protected veteran, status as an individual with a disability, or other applicable legally protected characteristics. We also consider qualified applicants with criminal histories, consistent with applicable federal, state and local law. Meta participates in the E-Verify program in certain locations, as required by law. Please note that Meta may leverage artificial intelligence and machine learning technologies in connection with applications for employment.Meta is committed to providing reasonable accommodations for candidates with disabilities in our recruiting process. If you need any assistance or accommodations due to a disability, please let us know at accommodations-ext@meta.com.