Software Engineer,Systems ML Engineering Job Bellevue area,Washington USA,Software Development

Summary

Meta is seeking a Staff Software Engineer to join the Systems ML Engineering team, focused on building and scaling the infrastructure and software systems that power large-scale machine learning workloads across Meta's production fleet. In this role, you will architect and own critical components of the ML systems stack, spanning training infrastructure, model serving, distributed computing frameworks, and ML platform tooling.

You will work at the intersection of systems engineering and machine learning to drive reliability, performance, and efficiency for some of the world's most demanding AI workloads, including large language models and generative AI systems.

Software Engineer, Systems ML Engineering Responsibilities

Design and implement scalable ML systems infrastructure components, including distributed training frameworks, model serving pipelines, and ML platform tooling used across Meta's production AI workloads

Lead technical design and architecture for major initiatives in the ML systems stack, evaluating trade-offs across performance, reliability, and engineering complexity

Identify and resolve performance bottlenecks in distributed ML training and inference systems through instrumentation, profiling, and targeted optimization

Define and drive service level objectives for ML infrastructure services, building dashboards, alerting, and runbooks to reduce mean time to mitigation during incidents

Collaborate with machine learning researchers, product engineers, and infrastructure teams to translate model development requirements into robust, production-grade systems

Leverage AI-assisted development workflows to accelerate implementation, code review, and system analysis, applying sound judgment on when to rely on AI tooling versus deep domain expertise

Mentor other engineers on ML systems best practices, distributed computing patterns, and engineering craft, including AI-native development workflows

Drive adoption of engineering standards across the team, including testing strategies, staged rollout practices using feature flagging and experimentation frameworks, and proactive monitoring

Contribute to roadmap definition and stakeholder alignment for multi-quarter ML infrastructure investments, communicating technical options and trade-offs to both engineering and cross-functional audiences

Conduct thorough code reviews and establish coding standards that improve maintainability and scalability of the ML systems codebase

Minimum Qualifications

Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience

8+ years of experience in software engineering with a focus on systems software, distributed computing, or ML infrastructure

Experience designing and implementing large-scale distributed systems, including components such as training orchestration, model serving, or data pipeline infrastructure

Experience with performance analysis and optimization of compute-intensive or distributed workloads, including profiling, benchmarking, and bottleneck identification

Experience leading end-to-end delivery of complex technical projects, including cross-team coordination, milestone planning, and risk mitigation

Experience with C++, Python, or equivalent systems programming languages applied to production ML or infrastructure systems

Preferred Qualifications

Experience contributing to or maintaining open-source ML systems or distributed computing projects

Experience building or operating ML platform services including experiment tracking, model registries, feature stores, or inference serving infrastructure

Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)

Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)

Experience with ML frameworks such as PyTorch, including distributed training paradigms such as data parallelism, model parallelism, or pipeline parallelism

Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and…