Machine Learning Engineer - Gen AI

Cupertino, California, United States Software and Services Add to Favorites Machine Learning Engineer - Gen AI Description

We are looking for a Machine Learning Scientist/Engineer to join our team and help shape the future of Apple Maps. In this role, you’ll work alongside world-class engineers and scientists to develop innovative AI/ML solutions that enhance user experiences. Your primary responsibility will be transforming high-level business objectives into measurable, technical requirements, including designing, implementing, and deploying cutting-edge models that solve real-world challenges. You’ll collaborate closely with cross-functional teams, including engineering and design, to architect scalable systems while continuously refining solutions through data-driven iteration.The ideal candidate takes initiative, demonstrates ownership from concept to production, is not afraid to dive deep into data, and is hands-on in building robust, real-world solutions. They thrive in a fast-paced environment and bring resilience and curiosity to continually improve in pursuit of excellence. Minimum Qualifications

5+ years of experience in building large scale machine learning systems 3+ years of experience in one or more of the following ML areas: generative AI models (e.g. Transformers, LLMs, VLMs, etc), NLP and text data, Computer Vision Intuitive understanding of machine learning algorithms, supervised and unsupervised modeling techniques and their performance characteristics Experience working with large scale and real world datasets Metrics focused and passionate about delivering models that render high quality results Strong programming skills and hands-on experience with machine learning tools and libraries such as PyTorch, TensorFlow, Scikit-learn; programming skills in Python, Scala, Java, or C++ Knowledge of Spark, Apache Hadoop, Solr/Lucene, Cassandra, and related big data technologies Preferred Qualifications

Masters or PhD degree in Machine Learning, Computer Science, Electrical/Computer Engineering, or related fields. Hands on experience with multi-modal foundation models and understanding of state of the art approaches in the field Experience shipping a complex AI system, including research and leveraging generative AI models Excellent independent problem-solving skills Excellent written and oral communication skills 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 $143,100 and $264,200, 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. Apple is an equal opportunity employer that is committed to inclusion and diversity. We seek to promote equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics. Learn more about your EEO rights as an applicant . Add to Favorites Machine Learning Engineer - Gen AI

#J-18808-Ljbffr