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.