Lead Machine Learning Engineer, Human Sensing

Apple • Seattle, WA

5d

About The Position

The Human and Object Understanding team (HOUr) in the Intelligent System Experience (ISE) organization is looking for an exceptional Machine Learning (ML) Technical Lead with deep expertise in Computer Vision and Machine Learning to anchor the technical direction of our multimodal Human Sensing team. In this pivotal leadership role, you will act as the principal technical driver and trusted partner to engineering leadership by driving project scoping, setting evaluation and KPI standards, shaping dataset collection strategies, and ensuring cross functional alignment. You will be part of a dynamic, high impact Applied Research organization building foundation models for facial and full body perception. You will work on cutting edge machine learning that sits at the heart of the most loved features across Apple platforms, including Apple Intelligence, Camera, Photos, Visual Intelligence, and next generation device experiences.

Requirements

• Master’s or Ph.D. in Computer Science, Computer Engineering, or related field (or equivalent practical experience) with 6+ years of industry experience in Computer Vision and Machine Learning.
• Proven experience in a Technical Lead or Staff-level role driving project scoping, setting KPIs, and leading technical initiatives across cross-functional teams.
• Strong expertise in evaluating complex ML systems, defining benchmarking methodologies, and conducting deep-dive failure analysis.
• Demonstrated ability to coordinate engineering teams, mentor peers, and partner closely with management on roadmap execution.
• High attention to detail, strong ownership mindset, and agility in dynamic, fast-evolving research environments.
• Deep proficiency in Python, PyTorch, and hands-on experience authoring clean, maintainable code and managing shared repositories.
• Deep domain knowledge in face recognition, identity re-identification (ReID), biometrics, or visual human sensing (e.g., pose, expression, human-object interaction).

Nice To Haves

• Hands-on experience collaborating 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 large language models (LLMs) or large-scale vision-language models (VLMs)
• Experience with on-device ML, model optimization (knowledge distillation, quantization, pruning), or production-grade ML pipelines.
• Background in research and innovation, demonstrated through publications in top-tier journals or conferences, patents, or impactful software developments.

Responsibilities

• Partner with engineering management as the primary technical lead to define project scope, technical milestones, and roadmap execution, including rigorous KPI targets and quality benchmarks across demographics, environmental conditions, and device use cases.
• Define dataset collection, annotation, and curation strategy in close collaboration with the Data team to systematically eliminate model blind spots.
• Architect and lead the team's core evaluation framework and benchmarking pipelines, including custom metrics, evaluation scripts, and automated tooling to stress-test models against production scenarios.
• Lead in-depth failure mode analysis, root-cause investigation, and edge-case discovery to drive targeted model and data iterations.
• Drive multi-team alignment across Evaluation, Integration and Data Operations teams.
• Coordinate day-to-day technical execution.
• Drive model optimization in close partnership with integration and other partner teams.
• Train, fine-tune, and experiment with state-of-the-art vision architectures when needed to unblock research or validate hypotheses.
• Act as the primary maintainer of the team’s core codebase, authoring and reviewing PRs, maintaining high engineering hygiene, and ensuring 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 the latest trends, technologies, and best practices in machine learning, multimodal foundation models, computer vision, and natural language understanding.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior