Software Engineer, Core Machine Learning

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

• 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, Tensor Flow, 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)