Software Engineer, GenAI Frameworks

Meta•Menlo Park, CA

21h•$121,992 - $181,000

About The Position

Meta is building the infrastructure and systems that power machine learning at scale across its family of products, including Feed, Reels, Ads ranking, and generative AI services. The Systems ML Engineering team sits at the intersection of ML and systems software, designing and optimizing the training and inference pipelines, distributed execution frameworks, and data processing systems that enable researchers and product teams to iterate quickly and deploy reliably. In this role, you will contribute to the full lifecycle of ML systems software — from designing scalable data pipelines and distributed training infrastructure to optimizing model serving latency and throughput — directly impacting the quality and speed of ML-powered experiences for billions of people.

Requirements

Currently has, or is in the process of obtaining a Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience. Degree must be completed prior to joining Meta
2+ years of experience in software engineering with a focus on machine learning systems, distributed systems, or high-performance data infrastructure
Experience writing production-quality code in Python and at least one compiled language such as C++ or Java, including performance-sensitive systems code
Experience designing and implementing components of ML training or inference pipelines, including data preprocessing, model execution, or serving systems
Experience with distributed computing concepts such as data parallelism, model parallelism, or parameter server architectures as applied to ML workloads
Experience writing automated tests, building logging and alerting, and participating in production incident response for large-scale systems

Nice To Haves

Experience optimizing ML workloads for throughput and latency, including profiling GPU or CPU utilization, memory bandwidth, and communication overhead in distributed training or inference settings
Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
Experience with ML framework internals such as PyTorch or JAX, including custom operator development, execution graph optimization, or compiler integration
Experience with stream or batch data processing systems such as Apache Spark, Flink, or Kafka in the context of ML feature pipelines
Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
1+ years production experience in GenAI post-training, RLHF, RLVR, comms/collectives, parallelism, accuracy evaluation, and performance profiling
Familiarity with model quantization, mixed-precision training, or other techniques for reducing compute and memory costs in production ML systems

Responsibilities

Design and implement scalable systems for distributed ML training and inference, including data ingestion pipelines, feature processing, and model serving infrastructure
Develop and optimize ML platform components such as training orchestration, gradient communication, and checkpoint management across large-scale distributed environments
Profile and diagnose performance bottlenecks across the ML stack, including data loading, preprocessing, forward and backward passes, and serving latency
Write automated tests covering expected behaviors, failure modes, and error paths for ML systems components, and build monitoring and alerting for production anomalies
Collaborate with ML researchers and product engineers to translate model requirements into reliable, high-throughput system designs
Own technical design for features and components within ML infrastructure, evaluating trade-offs between throughput, latency, cost, and engineering maintainability
Participate in staged rollouts of ML system changes using feature flagging and A/B testing frameworks, monitoring key metrics and responding to regressions
Contribute to code quality through code reviews, clear technical documentation, and consolidation of duplicative implementations across the ML systems codebase
Support on-call rotations for owned ML infrastructure, investigating production incidents and contributing detailed retrospectives to prevent recurrence