Machine Learning Engineer

Cupertino, CA, US • Posted 9 hours ago • Updated 9 hours ago
Full Time
On-site
Fitment

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Job Details

Skills

  • ECG
  • IDEA
  • Sensors
  • Fusion
  • Collaboration
  • Modeling
  • Evaluation
  • Computer Science
  • Electrical Engineering
  • Biomedical Engineering
  • Statistics
  • Signal Processing
  • Applied Mathematics
  • Generative Artificial Intelligence (AI)
  • Artificial Intelligence
  • Performance Appraisal
  • Python
  • Algorithms
  • Optimization
  • Design Of Experiments
  • Cloud Computing
  • Machine Learning (ML)
  • Workflow

Summary

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.

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.

Bachelors degree in Computer Science, Electrical Engineering, Biomedical Engineering, Statistics, Applied Mathematics, or related field, or equivalent industry experience.\nStrong foundation in machine learning, statistics, signal processing, or applied mathematics for real-world sensing problems\nExperience applying modern AI techniques, including generative AI and agentic AI, to accelerate algorithm development, data generation, and performance evaluation\nProficiency in Python for algorithm development and optimization\nDemonstrated ability to rapidly prototype, evaluate multiple approaches, and iterate based on experimental results\nExperience owning algorithm development from early exploration through validation and integration

Experience developing algorithms for physiological sensing using multi-modal data\nFamiliarity with on-device ML frameworks or resource-constrained optimization\nExperience working with incomplete, noisy, or limited datasets\nBackground in experimental design and statistical validation\nExperience with distributed or cloud-based ML workflows\nExperience accelerating development through simulation, synthetic data, or creative data augmentation approaches\nSelf-driven, curious engineer comfortable taking ambiguous sensing problems from concept to working solutions
Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.
  • Dice Id: 90733111
  • Position Id: 1c45194055e44943a73871c5216ca6f
  • Posted 9 hours ago
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