Lead Machine Learning Engineer, Human Sensing

Apple’s Human and Object Understanding (HOUr) team seeks a Lead Machine Learning Engineer (Technical Lead) to set technical direction for a multimodal Human Sensing group in Seattle.

Responsibilities

• Serve as the primary technical lead with engineering management to define project scope, technical milestones, and roadmap execution
• Set KPI targets and quality benchmarks across demographics, environmental conditions, and device use cases
• Define dataset collection, annotation, and curation strategy in collaboration with the Data team to reduce blind spots
• Architect and lead the team’s evaluation framework and benchmarking pipelines, including custom metrics, evaluation scripts, and automated stress-testing tools
• Lead failure mode analysis, root-cause investigation, and edge-case discovery to drive targeted model and data iterations
• Coordinate day-to-day technical execution across multiple teams, including Evaluation, Integration, and Data Operations
• Drive model optimization in partnership with integration and other partner teams
• Train, fine-tune, and run experiments with state-of-the-art vision architectures when needed to unblock research or validate hypotheses
• Maintain the team’s core codebase: author and review PRs, uphold engineering hygiene, and support rapid iteration velocity
• Communicate technical strategy, performance trade-offs, and progress to stakeholders and senior leadership; mentor junior and mid-level engineers
• Stay current with machine learning and multimodal foundation model trends, plus best practices in computer vision and natural language understanding

Requirements

• Master’s or Ph.D. in Computer Science, Computer Engineering, or related field (or equivalent practical experience)
• 6+ years of industry experience in Computer Vision and Machine Learning
• Proven Technical Lead or Staff-level experience: scoping projects, setting KPIs, and leading technical initiatives across cross-functional teams
• Expertise in evaluating complex ML systems, defining benchmarking methodologies, and performing deep failure analysis
• Experience coordinating engineering teams, mentoring peers, and partnering with management on roadmap execution
• Strong attention to detail, ownership mindset, and agility in fast-evolving research environments
• Deep proficiency in Python, PyTorch, and hands-on experience producing clean, maintainable code in shared repositories

Technologies

• Python, PyTorch
• Core ML
• Quantization-aware training, knowledge distillation
• Latency profiling, quantization, pruning
• Multimodal foundation models
• Computer vision, natural language understanding
• Face recognition
• Identity re-identification (ReID)
• Foundation vision models
• Large-scale Vision-Language Models (VLMs), large language models (LLMs), multimodal large language models (LLMs)
• Large-scale vision-language models (VLMs)

Benefits

• Comprehensive medical and dental coverage
• Retirement benefits
• Discounted products and free services
• Reimbursement for certain educational expenses, including tuition
• Discretionary bonuses or commission payments (may be eligible)
• Relocation (may be eligible)
• Opportunity to become an Apple shareholder through participation in Apple’s discretionary employee stock programs
• Discretionary restricted stock unit awards (eligible)
• Discounted Apple stock purchase if voluntarily participating in the Employee Stock Purchase Plan

Preferred Qualifications

• Deep domain knowledge in face recognition, identity re-identification (ReID), biometrics, or visual human sensing (pose, expression, human-object interaction)
• Hands-on collaboration with Data Collection & Annotation teams to design robust collection protocols and active learning datasets
• Experience with on-device model optimization: quantization-aware training, knowledge distillation, Core ML conversion, latency profiling
• Experience with foundation vision models or large-scale Vision-Language Models (VLMs)
• Hands-on experience training and scaling multi-modal LLMs or large-scale VLMs
• Experience with on-device ML, model optimization (knowledge distillation, quantization, pruning), or production-grade ML pipelines
• Research and innovation background demonstrated through publications in top-tier journals or conferences, patents, or impactful software developments

Pay & Location

• Location: Seattle, WA (onsite)
• Base pay range: USD 175,000 - 308,500 per year
• Base pay depends on skills, qualifications, experience, and location
• Apple benefit, compensation, and employee stock program eligibility and terms apply