Software Engineer, Core Machine Learning

Meta is seeking a Staff Software Engineer to join the Core Machine Learning team, focused on building and scaling the foundational ML infrastructure and systems that power Meta's family of products. In this role, you will architect and deliver high-impact ML platform capabilities — spanning training infrastructure, model serving, feature engineering pipelines, and AI-accelerated developer tooling — that enable thousands of engineers and researchers across Meta to build and ship state-of-the-art machine learning models at scale.

Responsibilities
Architect and own large-scale ML infrastructure systems, including distributed training frameworks, model serving platforms, and feature computation pipelines that support production workloads across Meta's product surface
* Lead the technical design and implementation of foundational ML platform components, evaluating trade-offs across performance, reliability, and developer experience
* Drive end-to-end delivery of major ML infrastructure initiatives, coordinating across teams and disciplines to align on priorities, manage dependencies, and execute phased rollouts
* Identify and resolve performance bottlenecks in ML training and inference systems through instrumentation, profiling, and targeted optimization
* Define and enforce service level objectives for core ML platform services, building dashboards, alerting systems, and runbooks to reduce mean time to mitigation during incidents
* Establish and advocate for engineering best practices in ML systems development, including testing strategies, safe rollout patterns, and AI-accelerated development workflows
* Collaborate with research, product engineering, and infrastructure teams as a credible technical co-owner, independently driving design reviews, data analyses, and architectural decisions
* Mentor other engineers on ML systems design, debugging complex distributed system issues, and applying AI tools to accelerate development velocity
* Contribute to the team's technical roadmap by identifying opportunities to improve ML platform capabilities and obtaining buy-in from key stakeholders across the organization

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 machine learning systems, ML infrastructure, or large-scale distributed systems
* Experience designing and implementing production ML systems such as distributed training frameworks, model serving infrastructure, or large-scale feature engineering pipelines
* Experience leading major technical initiatives from design through production, including cross-team coordination and phased rollout management
* Experience with performance analysis and optimization of ML training or inference workloads, including profiling, instrumentation, and bottleneck resolution
* Experience communicating technical decisions and trade-offs in writing to both engineering and non-engineering stakeholders through design documents, architectural proposals, or postmortems Experience defining and operating ML platform reliability programs, including resiliency testing, SLO frameworks, and incident retrospective processes
* Experience building or contributing to open-source ML frameworks or platform tooling such as PyTorch, TensorFlow, Ray, or similar systems
* Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
* Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
* Experience applying AI-assisted development tools to accelerate engineering workflows, including code generation, automated testing, or intelligent debugging
* Experience with hardware-software co-design for ML workloads, including quantization, model compression, or resource-efficient AI techniques
* Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)