1

Product Manager Machine Learning Jobs in California

Overview We are seeking a highly motivated Machine Learning Engineer to help build next-generation ... Collaborate with Infrastructure, Data Engineering, Product Management, Design, and Quality teams to ...

An understanding of large-scale systems and service-oriented architecture Fraud, Risk, Payments, Identity/Account protection background Machine learning product management experience Ability to lead ...

As a fintech company where data and machine learning (ML) is integral to both our business strategy ... Collaborate with software engineers, data engineers, product managers, and marketing to coordinate ...

The Opportunity Adobe is looking for a Machine Learning Engineer who will apply AI and machine ... Collaborate with product management and engineering groups to develop new products and features.

The Opportunity Adobe is looking for a Machine Learning Engineer who will apply AI and machine ... Collaborate with product management and engineering groups to develop new products and features.

Manage MLOps infrastructure to monitor and optimize models. Qualifications Experience: * 3+ years of professional experience as a Machine Learning Engineer or production-focused Data Scientist.

Manage MLOps infrastructure to monitor and optimize models. Qualifications Experience: * 3+ years of professional experience as a Machine Learning Engineer or production-focused Data Scientist.

... machine learning and natural language processing! The features we create are redefining how ... Our universal search engine powers search features across a variety of Apple products, including ...

next page

Showing results 1-20

Product Manager Machine Learning information

How does a Product Manager specializing in Machine Learning typically collaborate with data scientists and engineering teams?

Product Managers in Machine Learning work closely with both data scientists and engineering teams to translate business objectives into viable AI-driven products. They facilitate communication by defining clear requirements, prioritizing features, and ensuring that the technical roadmap aligns with user needs and company strategy. Regular meetings, progress reviews, and shared documentation are common practices to keep everyone aligned. This cross-functional collaboration is essential for addressing feasibility, optimizing models, and delivering successful products on schedule.

What does a Product Manager for Machine Learning do?

A Product Manager for Machine Learning oversees the development and deployment of machine learning products or features. They work closely with data scientists, engineers, and business stakeholders to identify opportunities where machine learning can deliver value, define product requirements, and guide projects from conception to launch. Their responsibilities include setting the product vision, prioritizing features, ensuring alignment with business goals, and evaluating the impact of machine learning solutions. They also help bridge the gap between technical teams and non-technical stakeholders by translating complex concepts into actionable plans.

What is the difference between Product Manager Machine Learning vs Data Scientist?

AspectProduct Manager Machine LearningData Scientist
Primary FocusOverseeing ML product development, strategy, and deploymentAnalyzing data, building models, and deriving insights
Required SkillsProduct management, ML understanding, cross-functional collaborationStatistics, programming, data analysis
Work EnvironmentProduct teams, engineering, business stakeholdersData analysis teams, research, engineering
Common CertificationsProduct management certifications, ML coursesData science certifications, programming skills

While both roles involve machine learning, Product Manager Machine Learning focuses on guiding ML products from conception to deployment, working closely with engineering and business teams. Data Scientists primarily analyze data and develop models to extract insights. The roles complement each other but differ in their core responsibilities and skill sets.

What are the key skills and qualifications needed to thrive as a Product Manager, Machine Learning, and why are they important?

To thrive as a Product Manager, Machine Learning, you need a solid understanding of product lifecycle management, data analytics, and machine learning concepts—often supported by a technical degree and relevant experience. Familiarity with tools like Python, SQL, JIRA, and machine learning frameworks, as well as certifications such as PMP or Agile, is highly beneficial. Outstanding communication, stakeholder management, and problem-solving skills help you bridge the gap between technical teams and business objectives. These abilities are crucial to successfully guide ML products from ideation to launch, ensuring they deliver real value and align with organizational goals.
What are popular job titles related to Product Manager Machine Learning jobs in California? For Product Manager Machine Learning jobs in California, the most frequently searched job titles are:
What job categories do people searching Product Manager Machine Learning jobs in California look for? The top searched job categories for Product Manager Machine Learning jobs in California are:
What cities in California are hiring for Product Manager Machine Learning jobs? Cities in California with the most Product Manager Machine Learning job openings:
Manager, Machine Learning Infrastructure - SIML

Manager, Machine Learning Infrastructure - SIML

Apple

Cupertino, CA

$198K - $298K/yr

Full-time

Medical, Dental, Retirement

Posted 24 days ago


Key responsibilities

  • Lead a team responsible for building and operating infrastructure for large-scale data processing and machine learning systems.

  • Collaborate with cross-functional partners to define technical strategy, roadmaps, and deliver impactful end-to-end ML solutions.

  • Architect and develop systems for data ingestion, processing, indexing, and access to support ML research and production needs.


Apple rating

8.1

Company rating: 8.1 out of 10

Based on 666 frontline employees who took The Breakroom Quiz

5th of 30 rated technology retailers


Job description

Do you think Computer Vision and Machine Learning can change the world? Do you think it can transform the way millions of people collect, discover and share the most special moments of their lives? We truly believe it can. And we are looking for hardworking engineers who can contribute to building the ecosystem of tooling necessary to create these exciting technologies.
We are the System Intelligent and Machine Learning (SIML) group that provides foundational computer vision and machine learning technologies to Apple's ecosystem. Our work is behind essential features such as Camera, Text & Handwriting recognition, and Apple Intelligence experiences (Image Playground, Writing Tools, Smart Script, Math Notes..). We are seeking an Engineering Manager to lead the development of scalable, high-performance infrastructure that powers product-focused machine learning initiatives.
Description
In this role you will lead a team responsible for building and operating infrastructure that enables large-scale data processing (terabytes and beyond) across domains such as image generation, large language models (LLMs), computer vision, natural language processing, human-computer interaction, and text recognition. This includes designing systems for dataset creation and management, ingesting annotated and inferred data, and delivering seamless access to high-quality data for ML researchers and engineers.
A key part of this role is driving systems that enable deeper understanding of model behavior-such as failure mode analysis, evaluation pipelines, and benchmarking frameworks-to accelerate iteration velocity and improve model quality. You will work across the stack, tackling challenges ranging from low-level distributed systems and compute efficiency to building stable, intuitive interfaces for internal users.
As a leader, you will partner closely with cross-functional teams including ML researchers, product teams, and platform engineering to define roadmaps, align priorities, and deliver impactful solutions. You will play a critical role in shaping how ML systems are developed, evaluated, and scaled from early experimentation to production.","responsibilities":"Lead and grow a team of engineers building ML infrastructure across data, training, and evaluation systems
Define technical strategy and roadmap for scalable ML data and systems infrastructure
Architect and develop systems for large-scale data ingestion, processing, indexing, and access
Drive best practices in system design, distributed computing, and performance optimization
Collaborate with cross-functional partners (ML researchers, product teams, infra teams) to deliver end-to-end ML solutions
Balance hands-on technical contributions with team leadership, mentorship, and execution
Improve developer experience through robust tooling, stable APIs, and self-serve workflows
Ensure systems are reliable, scalable, and efficient to support rapid iteration and production needs
Preferred Qualifications
Experience building infrastructure for ML workflows (data pipelines, training systems, evaluation frameworks, or deployment systems)
Domain experience in areas such as AI/ML, computer vision, NLP, or related fields
Experience working with large-scale datasets and compute-intensive systems
Experience improving developer productivity through tooling and platform abstractions
Ability to operate effectively in cross-functional, fast-paced environments with evolving requirements
Minimum Qualifications
Bachelor’s, Master’s, or Ph.D. in Computer Science, Computer Engineering, or a related field (or equivalent experience)
7+ years of software engineering experience, with 2+ years in a technical leadership or management role
Strong programming skills in one or more of: Python, Java, Go, C/C++
Solid understanding of machine learning fundamentals and ML system workflows
Proven experience in building and scaling distributed systems and backend infrastructure
Strong system design skills with expertise in at least one systems domain (e.g., data infrastructure, distributed systems, ML platforms)
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 $198,300 and $298,100, 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.

What Apple employees say

Pay

Benefits

Hours and flexibility

Workplace

Get the full story on Breakroom


Apple logo

About Apple

Sourced by ZipRecruiter

Imagine what you could do here! At Apple, new ideas have a way of becoming extraordinary products, services, and customer experiences very quickly. Bring passion and dedication to your job and there's no telling what you could accomplish. Dynamic, intelligent people and inspiring, innovative technologies are the norm here. The people who work here have reinvented entire industries with all Apple Hardware products. The same real passion for innovation that goes into our products also applies to our practices strengthening our dedication to leave the world better than we found it.

Industry

Computer and electronic product manufacturing

Company size

10,000+ Employees

Headquarters location

Cupertino, CA, US

Year founded

1976