Senior ML Engineer Jobs in Cupertino, CA
Senior ML Engineer jobs in Cupertino are in strong demand, concentrated in the Main Street corridor, the Vallco area, and the broader De Anza Boulevard tech strip, across consumer electronics, on-device AI, and cloud infrastructure. Employers actively hiring include Amazon Web Services, Apple, and Amazon. Scan the live roles below and apply to whichever ones fit.
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Join Us in Shaping the Future of Generative AI at Apple!
Are you passionate about building and leading teams that make AI systems safer, more inclusive, and globally representative? Apple is seeking an experienced Engineering Manager to lead a team shaping the future of responsible AI evaluation for the next generation of generative features.
In this role, you will lead a team of machine learning engineers and data scientists working to assess, quantify, and improve the safety and inclusivity of Apple’s Generative-AI powered features and products. You and your team's work will tackle innovative problems in machine learning, particularly focused on large language models for text generation, diffusion models for image generation, and mixed model systems for multimodal applications. You will directly influence how we evaluate, align, and monitor the safety of large language and multimodal models, while upholding Apple's values, privacy, and quality standards.
As part of Apple’s Responsible AI group within the Software Engineering organization, you’ll collaborate with cross-functional partners to minimize unintended consequences across people, systems, and society while elevating feature capabilities and the overall user experience. You’ll set technical direction, anticipate safety risks, and develop a team to deliver trusted, high-quality AI experiences to users around the globe.
Description
We are seeking a visionary Machine Learning Engineering Leader to build, mentor, and develop a high-performing team, driving Responsible AI Evaluation efforts for global generative AI products. In this role, you will provide critical technical direction for operationalizing policy and risk assessments into measurable criteria; designing datasets; developing LLM- and human-in-the-loop methods for data analysis; and overseeing the implementation of data pipelines to produce reliable, scalable insights to inform ship/no-ship product decisions. Pre and Post-launch, you will remain hands-on-analyzing evaluation results, conducting failure investigations, and leveraging human and automated feedback to continuously improve feature safety and build user trust. As a champion for your team, you will foster an inclusive culture of technical excellence, empower senior leadership with safety recommendations, and systematically remove roadblocks to ensure team success. This role heavily draws on applied data science, scientific investigation and interpretation, strategic cross-functional stakeholder management, including negotiating requirements and defending safety metrics to leadership, and metrics reporting and presentation to stakeholders & decision-makers.","responsibilities":"Develop metrics for evaluation of safety and fairness risks inherent to generative models and Gen-AI features
Design datasets, identify data needs, and work on creative solutions, scaling and expanding data coverage through human and synthetic generation methods
Develop sampling strategies and combine human annotation with auto-grading to deliver high-quality, high-confidence insights at a fast pace and large scale
Use and implement data pipelines, and collaborate cross-functionally to execute end-to-end safety evaluations
Drive operational excellence by automating manual workflows, optimizing evaluation velocity, and scaling infrastructure to meet rapid product release cycles.
Distill project findings into insights for product engineering teams and safety policy development
Work with highly-sensitive content with exposure to offensive and controversial content
Preferred Qualifications
Experience working in the Responsible AI space
Passion for building innovative, high-impact products and teams that draw upon interdisciplinary skills
Curiosity about fairness and bias in generative AI systems, and a strong desire to help make the technology more equitable
A Social Science or Humanities background, or other demonstrable interest in socio-technical issues
Prior scientific research and publication experience
Proven track record of contributing to diverse teams in a collaborative environment
Experience working with multimodal models, agentic systems, and/or diverse languages and locales
Experience with compliance and regulatory needs in the AI space, specifically regarding child safety, clinical/health applications, or companionship chatbots
Experience conducting large scale benchmarking across models and capabilities
Minimum Qualifications
MS or PhD in Computer Science, Linguistics, Cognitive Science, HCI, Psychology, Mathematics, Physics, or a similar science or technology field with a strong basis in scientific data collection and analysis + at least 4 years of relevant work experience, or BA/BS with 8+ years of relevant work experience, with 2+ years directly managing engineering teams
Experience gathering and analyzing varied language, image, voice, video, and/or multi-modal data, including human- and LLM-generated data
Experience with LLM-Judges as well as human-in-the-loop techniques, including designing human annotation projects, writing guidelines, and dealing with highly multi-labeled, nuanced, and often conflicting data
Proficiency in data science, machine learning, analytics, programming with Python & Pandas, and AI-assisted coding tools; experience with one or more plotting & visualization libraries
Cross-functional collaboration skills to prioritize competing projects needs, set and maintain schedules for project milestones, and communicate with all levels of team members, as well as internal and external stakeholders
A rigorous approach to metric development; interpretation of experiments and evaluations; and presentation to executives
Comfortable leading a team that works with sensitive or potentially offensive content.
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 $184,700 and $324,800, 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.
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Who's Hiring
- Amazon Web Services439

- Apple279

- Amazon20

- HiringCafe20
Top Industries Hiring
- Distribution & Wholesale
- Electronics & Hardware
Senior ML Engineer Jobs in Cupertino: Frequently Asked Questions
How do I get a senior ml engineer job in Cupertino?
Focus on the consumer electronics and on-device AI companies that anchor Cupertino's tech corridor along De Anza Boulevard and the Vallco district, as these employers drive the bulk of local senior ml engineer hiring. Candidates with hands-on experience in model optimization, edge inference, or large-scale recommendation systems stand out most. Building a portfolio of shipped production models and contributing to open-source ML projects read well to Cupertino hiring managers who evaluate applied depth over academic credentials alone.
Which companies hire senior ml engineers in Cupertino?
Companies currently hiring senior ml engineers in Cupertino include Amazon Web Services, Apple, and Amazon, per current listings on Migrate Mate as of November 2026. Cupertino's employer mix skews heavily toward large consumer technology companies and their direct hardware and software partners, so most openings come from mature product organizations rather than early-stage startups.
Are there remote senior ml engineer jobs in Cupertino?
Yes, though opportunities vary by specialization: senior ml engineer work tied to on-device or hardware-integrated AI tends to require on-site collaboration, while roles focused on data pipelines, model evaluation, or research are more commonly remote or hybrid. About 0% of senior ml engineer openings tied to Cupertino are remote or hybrid as of November 2026, with fully remote positions appearing most often in platform and infrastructure-side roles rather than product-embedded ones.
How can I get a senior ml engineer job in Cupertino with little or no experience?
The most realistic path into Cupertino's senior ml engineer market with limited experience is through associate or mid-level ML roles at the technology companies and hardware vendors clustered along the De Anza Boulevard corridor, then building toward a senior title internally. Cupertino employers also hire from internship pipelines tied to nearby universities, and lateral moves from data science or software engineering roles with strong ML project exposure are a common entry point at companies that value demonstrated applied work over years of formal seniority.
Which industries hire the most senior ml engineers in Cupertino?
Cupertino senior ml engineer roles concentrate in Distribution & Wholesale and Electronics & Hardware, based on current listings on Migrate Mate as of November 2026. These sectors dominate local hiring because Cupertino's economy is built around consumer hardware and the software ecosystems that run on it, creating sustained demand for engineers who can develop and optimize models at the intersection of device performance and user experience.
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