Machine Learning Engineer

Apple's Health Sensing team is seeking a versatile Machine Learning Engineer to develop next-generation health algorithms that deliver meaningful insights to users by combining classical ML, signal processing, and emerging generative AI techniques. Our team has delivered impactful features including heart rate notifications, ECG, blood oxygen, sleep apnea notifications, and overnight vitals to millions of Apple Watch users.

Description

This role is ideal for an engineer who enjoys moving quickly from idea to prototype to product, creatively overcoming data limitations, and applying new tools to multi-modal sensor fusion problems in health and wellness. You will work across the full algorithm lifecycle including data strategy, modeling, evaluation, optimization, and deployment.

Responsibilities:

Develop and validate ML and GenAI-driven algorithms for health sensing applications from concept through productization

Prototype and compare multiple approaches using real and synthetic data to accelerate algorithm development

Design experiments and evaluation methodologies to quantify performance and guide algorithm improvements

Optimize algorithms for robustness, efficiency, and on-device deployment constraints

Work cross-functionally with user studies, hardware, software, and product teams to bring algorithms into product

Analyze failure modes, quantify tradeoffs, and drive data-driven algorithm improvements

Preferred Qualifications

Experience developing algorithms for physiological sensing using multi-modal data

Familiarity with on-device ML frameworks or resource-constrained optimization

Experience working with incomplete, noisy, or limited datasets

Background in experimental design and statistical validation

Experience with distributed or cloud-based ML workflows

Experience accelerating development through simulation, synthetic data, or creative data augmentation approaches

Self-driven, curious engineer comfortable taking ambiguous sensing problems from concept to working solutions

Minimum Qualifications

Bachelors degree in Computer Science, Electrical Engineering, Biomedical Engineering, Statistics, Applied Mathematics, or related field, or equivalent industry experience.

Strong foundation in machine learning, statistics, signal processing, or applied mathematics for real-world sensing problems

Experience applying modern AI techniques, including generative AI and agentic AI, to accelerate algorithm development, data generation, and performance evaluation

Proficiency in Python for algorithm development and optimization

Demonstrated ability to rapidly prototype, evaluate multiple approaches, and iterate based on experimental results

Experience owning algorithm development from early exploration through validation and integration

Pay & Benefits

At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $147,400 and $272,100, and your base pay will depend on your skills, qualifications, experience, and location.

Apple employees also have the opportunity to become an Apple shareholder through participation in Apple's discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple's Employee Stock Purchase Plan. You'll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses - including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation. Learn more about Apple Benefits

Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.