Software Engineering Manager - Neural Interface ML Infra

Lead a team at the intersection of AI, distributed systems, and hardware architecture. The role drives ML model efficiency and scalability, infrastructure costs, and enables embedded AI capabilities. Directly supporting Meta’s ability to train and serve AI models for wearable products., Software Engineering Manager - Neural Interface ML Infra Responsibilities:

1. People Leadership: Build/retain an engineering team
2. provide coaching, mentorship, and performance management
3. Technical Leadership: Engage in design reviews, architecture decisions, and technical trade-offs
4. define technical vision with Tech Leads
5. Execution: Drive complex hardware-software co-design projects to completion
6. manage roadmaps, timelines, and risk mitigation
7. Cross-Functional Partnership: Collaborate with AI Infra, Hardware Engineering, Product, and Research
8. represent the team to leadership

1. 3+ years managing software engineering teams with a track record of delivering measurable outcomes
2. Technical background in at least two of: AI/ML systems, distributed/HPC systems, software tooling and infrastructure or model optimization with GPUs/accelerators
3. Demonstrated ability to engage deep in technical work and guide decisions
4. Proven track record growing engineers
5. Experience communicating technical strategy and influencing cross-functional stakeholders through written proposals and presentations

Preferred Qualifications:

Preferred Qualifications:

1. M.S. or Ph.D. in CS, EE, or related field
2. Prior Tech Lead, TLM, or IC experience before management
3. Experience building teams in ambiguous, exploratory domains
4. Track record of maintaining team engagement, retention, and consistent delivery outcomes
5. Experience managing AI infrastructure and capacity including GPU/CPU used for training, benchmarking and inference
6. Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
7. Experience with PyTorch, TensorFlow, or equivalent AI frameworks
8. Familiarity with AI compilers, high-performance kernel development, or hardware enablement
9. Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
10. Background in performance optimization/profiling
11. Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)