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 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.

Description

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 models.

You 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 iteration.

Beyond 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 velocity.

To 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.

Minimum Qualifications

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.

Preferred Qualifications

Deep domain knowledge in face recognition, identity re-identification (ReID), biometrics, or visual human sensing (e.g., pose, expression, human-object interaction). 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.