Lead Machine Learning Engineer, Human Sensing

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

• Partner with engineering management as the primary technical lead 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 with the Data team to reduce model blind spots
• Architect and lead the team’s evaluation framework and benchmarking pipelines (custom metrics, evaluation scripts, automated tooling)
• Stress-test models against production scenarios through evaluation and benchmarking automation
• Lead failure mode analysis: root-cause investigation and edge-case discovery to drive model and data iterations
• Coordinate day-to-day technical execution across Evaluation, Integration, and Data Operations teams
• 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 for research and hypothesis validation
• Maintain the team’s core codebase: author and review PRs, uphold engineering hygiene, and enable rapid iteration velocity
• Communicate technical strategy, performance trade-offs, and progress to stakeholders and senior leadership
• Guide and mentor junior and mid-level engineers
• Stay current with machine learning trends and best practices across multimodal foundation models, 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 driving project scoping, KPI setting, and technical initiatives across cross-functional teams
• Expertise in evaluating complex ML systems, defining benchmarking methodologies, and conducting deep-dive failure analysis
• Ability to coordinate engineering teams and mentor peers while partnering closely with management on roadmap execution
• Strong ownership mindset, attention to detail, and agility in fast-evolving research environments
• Deep proficiency in Python and PyTorch, including hands-on experience writing clean, maintainable code and managing 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
• Range of 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
• Eligible for discretionary restricted stock unit awards
• Can purchase Apple stock at a discount with voluntary participation in the Employee Stock Purchase Plan

Preferred Qualifications

• Domain knowledge in face recognition, identity re-identification (ReID), biometrics, or visual human sensing (pose, expression, human-object interaction)
• Experience collaborating with Data Collection & Annotation teams on 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 multimodal 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 shown through publications, patents, or impactful software developments

Pay & Benefits

• Base pay range: $175,000 to $308,500 per year
• Base pay depends on skills, qualifications, experience, and location
• Apple also offers eligibility for discretionary restricted stock unit awards and an Employee Stock Purchase Plan discount (if voluntarily participating)
• Note: Benefits, compensation, and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program