Lead a team at the intersection of AI, distributed systems, and hardware architecture. The role drives ML model efficiency and scale, infrastructure costs, and enables next-generation AI capabilities. Directly supporting Meta's ability to train and serve AI models at scale.
Responsibilities
People Leadership: Build/retain an engineering team; provide coaching, mentorship, and performance management
• Technical Leadership: Engage in design reviews, architecture decisions, and technical trade-offs; define technical vision with Tech Leads
• Execution: Drive complex hardware-software co-design projects to completion; manage roadmaps, timelines, and risk mitigation
• Cross-Functional Partnership: Collaborate with AI Infra, Hardware Engineering, Product, and Research; represent the team to leadership
Minimum Qualifications
• 3+ years managing software engineering teams with high-performing team track record
• Technical background in at least two of: AI/ML systems, distributed/HPC systems, or model optimization with GPUs/accelerators
• Demonstrated ability to engage deep in technical work and guide decisions
• Experience supporting engineer development through mentorship, technical guidance, and career growth planning
• Experience communicating technical strategy and influencing cross-functional stakeholders through written proposals and presentations
Preferred Qualifications
• Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
• 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)
• M.S. or Ph.D. in CS, EE, or related field
• Familiarity with AI compilers, high-performance kernel development, or hardware enablement
• Experience building teams in ambiguous, exploratory domains
• Background in performance optimization/profiling
• Prior Tech Lead, TLM, or IC experience before management
• Experience with PyTorch, TensorFlow, or equivalent AI frameworks
• Track record of maintaining team engagement through regular feedback, clear goal-setting, and retention-focused practices, with consistent delivery outcomes
