Lead Machine Learning Engineer, Human Sensing

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 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 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.

As the Technical Lead for Human Sensing you will play a pivotal role on the team translating high level product ambitions into crisp well scoped ML programs. You will take ownership of defining project milestones establishing quantitative KPI targets and directing the data strategy by identifying exact dataset requirements edge cases and collection protocols needed to train robust identity recognition and human perception will spearhead our comprehensive Evaluation Framework obsessing over metric fidelity deep failure analysis and benchmark integrity. You will be directly responsible for designing and executing thorough evaluation protocols as well as building diagnostic tools and automation scripts that empower the team to rapidly isolate failure modes uncover root causes and accelerate model technical strategy you will actively drive cross functional engagements across Data Evaluation and Platform Integration teams coordinating engineering efforts removing technical roadblocks and mentoring engineers to ensure alignment and rapid execution ensure continuous team agility and engineering excellence you will also serve as a hands on technical anchor by diving deep into failure analysis driving model optimizations for on device performance and maintaining high codebase health through rigorous PR reviews and core repository stewardship.

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 dataset collection annotation and curation strategy in close collaboration with the Data team to systematically eliminate model blind and lead the teams core evaluation framework and benchmarking pipelines including custom metrics evaluation scripts and automated tooling to stress-test models against production in-depth failure mode analysis root-cause investigation and edge-case discovery to drive targeted model and data multi-team alignment across Evaluation Integration and Data Operations teams. Coordinate day-to-day technical model optimization in close partnership with integration and other partner fine-tune and experiment with state-of-the-art vision architectures when needed to unblock research or validate as the primary maintainer of the teams core codebase authoring and reviewing PRs maintaining high engineering hygiene and ensuring rapid iteration technical strategy performance trade-offs and progress to stakeholders and senior leadership. Guide and mentor junior and mid-level current with the latest trends technologies and best practices in machine learning multimodal foundation models computer vision and natural language understanding.

Masters 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 experience in a Technical Lead or Staff-level role driving project scoping setting KPIs and leading technical initiatives across cross-functional expertise in evaluating complex ML systems defining benchmarking methodologies and conducting deep-dive failure ability to coordinate engineering teams mentor peers and partner closely with management on roadmap attention to detail strong ownership mindset and agility in dynamic fast-evolving research 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).nHands-on experience collaborating with Data Collection u0026 Annotation teams to design robust collection protocols and active learning with on-device model optimization (quantization-aware training knowledge distillation Core ML conversion latency profiling).nExperience with foundation vision models or large-scale Vision-Language Models (VLMs).nHands-on experience training and scaling multi-modal large language models (LLMs) or large-scale vision-language models (VLMs)nExperience with on-device ML model optimization (knowledge distillation quantization pruning) or production-grade ML in research and innovation demonstrated through publications in top-tier journals or conferences patents or impactful software developments.