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Machine Learning Engineer Jobs in Santa Rosa, CA

Develop solution recommendations across machine learning, NLP, generative AI, and automation. Manage project delivery in collaboration with internal engineers and data scientists. Present technical ...

As a DataAnnotation's coder, you'll be part of a growing community of over 100,000 professionals -- including front-end, back-end, full-stack, machine learning, and other engineers -- who are driving ...

As a DataAnnotation's coder, you'll be part of a growing community of over 100,000 professionals -- including front-end, back-end, full-stack, machine learning, and other engineers -- who are driving ...

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Showing results 1-20

Machine Learning Engineer information

See Santa Rosa, CA salary details

$34.4K

$140.8K

$211.6K

How much do machine learning engineer jobs pay per year?

As of Jun 26, 2026, the average yearly pay for machine learning engineer in Santa Rosa, CA is $140,787.00, according to ZipRecruiter salary data. Most workers in this role earn between $111,000.00 and $169,500.00 per year, depending on experience, location, and employer.

Is ML full of coding?

Machine Learning Engineers typically do a significant amount of coding, especially in languages like Python or R, to develop algorithms, preprocess data, and build models. Strong programming skills are essential, along with knowledge of frameworks such as TensorFlow or PyTorch, but the role also involves data analysis, model evaluation, and collaboration with teams. Coding is a core component of the job, though some tasks may involve model deployment and optimization that require different skills.

What engineers make $500,000?

Senior machine learning engineers with extensive experience, advanced skills in deep learning and data science, and often working in high-paying industries such as finance or technology can earn $500,000 or more annually. Compensation typically includes base salary, bonuses, and stock options, especially at large tech companies or startups with significant funding.

What do machine learning engineers do?

Machine learning engineers develop algorithms and models that enable computers to learn from data and make predictions or decisions. They often work with large datasets, use programming languages like Python or Java, and utilize tools such as TensorFlow or PyTorch to build, test, and deploy machine learning systems in production environments.

What are Machine Learning Engineers?

Machine Learning Engineers are specialized software engineers who design, build, and deploy machine learning models and systems. They work at the intersection of software engineering and data science, transforming data-driven prototypes into scalable, production-ready solutions. Their responsibilities include data preprocessing, model selection, algorithm implementation, and optimizing models for performance and efficiency. Machine Learning Engineers often collaborate with data scientists, software developers, and other stakeholders to integrate AI technologies into products and services.

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

To thrive as a Machine Learning Engineer, you need strong programming skills (particularly in Python), a solid background in mathematics and statistics, and a degree in computer science or a related field. Experience with machine learning frameworks (such as TensorFlow or PyTorch), data processing tools, and cloud platforms is typically required. Problem-solving ability, effective communication, and adaptability are crucial soft skills for collaborating with teams and translating complex models into practical solutions. These competencies ensure the development, deployment, and continual improvement of machine learning systems that drive business value.

Which 5 jobs will survive AI?

Machine Learning Engineers are likely to continue to be in demand as they develop, implement, and maintain AI systems, requiring specialized skills in programming, data analysis, and model optimization. Roles that involve complex problem-solving, creativity, and human interaction—such as healthcare professionals, educators, skilled tradespeople, and certain managerial positions—are also expected to persist despite AI advancements. These jobs typically require emotional intelligence, adaptability, and domain expertise that AI cannot easily replicate.

What Does a Machine Learning Engineer Do?

A machine learning engineer maintains production systems and often works with other engineers. In this career, you work with software development methodology, use modern software development tools, and use agile practices. You also play a role in software design and architecture, so you may occasionally work with a programmer. An engineer may help to predict how a model should perform or seek out regression issues by using different test types and algorithms. To fulfill your duties and responsibilities, you work on a computer and use an array of skills and programs to carry out these tests.

What are some common challenges faced by Machine Learning Engineers when deploying models to production?

Machine Learning Engineers often encounter challenges such as ensuring model scalability, maintaining data consistency between training and production environments, and monitoring model performance over time. Integrating models into existing software infrastructure may require collaboration with DevOps and software engineering teams to address issues like latency, version control, and resource allocation. Additionally, ongoing model maintenance is crucial to prevent model drift and ensure that predictions remain accurate as new data becomes available.

What is the difference between Machine Learning Engineer vs Data Scientist?

AspectMachine Learning EngineerData Scientist
CredentialsBachelor's or Master's in CS, Data Science, or related; experience with ML frameworksBachelor's or Master's in Statistics, Data Science, or related; strong analytical skills
Work EnvironmentDevelops scalable ML models, deploys algorithms into productionAnalyzes data, builds models, interprets data insights
Industry UsageTech companies, startups, AI-focused firmsFinance, healthcare, marketing, research organizations

While both roles work with data and machine learning, Machine Learning Engineers focus on building and deploying scalable ML models in production environments. Data Scientists primarily analyze data, create models, and generate insights. The roles often overlap but differ in their core responsibilities and focus areas.

What are the most commonly searched types of Machine Learning Engineer jobs in Santa Rosa, CA? The most popular types of Machine Learning Engineer jobs in Santa Rosa, CA are:
What are popular job titles related to Machine Learning Engineer jobs in Santa Rosa, CA? For Machine Learning Engineer jobs in Santa Rosa, CA, the most frequently searched job titles are:
What job categories do people searching Machine Learning Engineer jobs in Santa Rosa, CA look for? The top searched job categories for Machine Learning Engineer jobs in Santa Rosa, CA are:
What cities near Santa Rosa, CA are hiring for Machine Learning Engineer jobs? Cities near Santa Rosa, CA with the most Machine Learning Engineer job openings:
Infographic showing various Machine Learning Engineer job openings in Santa Rosa, CA as of June 2026, with employment types broken down into 1% As Needed, 89% Full Time, 8% Part Time, 1% Temporary, and 1% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $140,787 per year, or $67.7 per hour.
Senior Machine Learning iOS Platform Engineer - Responsible AI and Safety

Senior Machine Learning iOS Platform Engineer - Responsible AI and Safety

Apple

Bodega Bay, CA

$171K - $302K/yr

Full-time

Medical, Dental, Retirement

Posted 17 days ago


Apple rating

8.1

Company rating: 8.1 out of 10

Based on 662 frontline employees who took The Breakroom Quiz

6th of 30 rated technology retailers


Job description

Join Us in Shaping the Future of Generative AI at Apple!
Are you passionate about making AI systems safer, more inclusive, and globally representative? Apple is seeking an expert Client Engineer to own the integration of our Responsible AI mitigation assets across the full deployment surface, from on-device foundation models running on Apple Silicon to server-side inference on Private Cloud Compute (PCC).
In this role, you will be a pivotal technical leader, bridging Swift client engineering and ML deployment, driving the architectural vision, design, and implementation of how safety classifiers, guardrail models, and mitigation policies are shipped, invoked, and streamed alongside our generative features. You will take end-to-end ownership, from initial concept and rapid prototyping to delivering robust, high-performance, and maintainable solutions that minimize unintended consequences across people, systems, and society while elevating feature capabilities and the overall user experience. Together, we’ll anticipate challenges, measure real-world impact, and deliver trusted, high-quality AI experiences to users around the globe.
Description
Our team leads Responsible AI initiatives for global generative AI products, operating at the intersection of policy, product, and GenAI. We build the safety classifiers, content filters, and policy enforcement layers that protect users from unintended model behavior. This role is about getting those assets into users' hands reliably, on the device or in the cloud, at the latency and quality bar Apple expects. We are seeking candidates who will work closely with multiple stakeholders, ranging from design, engineering, legal and regulatory to ensure our safeguards advance both user protection and product innovation. You will work on defining mitigation architectures, owning the implementation and overseeing the integration in production. Additionally, you will contribute to modeling, tooling and frameworks, as well as dataset, and evaluation methods to monitor, diagnose failures, and improve the safety of generative models throughout the deployment lifecycle.","responsibilities":"Architectural Leadership: Defining and evolving the technical architecture for complex multimodal workflows, ensuring scalability, performance, and future extensibility.
Feature Innovation: Leading the design and implementation of cutting-edge features that leverage Apple's unique hardware and software capabilities.
Technical Mentorship & Growth: Guiding and mentoring a team of talented engineers, fostering best development practices, and contributing to the growth and culture of the team.
Cross-Functional Collaboration: Partnering closely with other engineering teams to translate Safety needs into same and performant software experiences.
Performance Optimization: Ensure our features set the benchmark for speed and efficiency, by expertly optimizing for large-scale image processing and real-time interactions, resulting in an unparalleled user experience.
Contribute to deployment pipeline and tooling efforts as needed
Preferred Qualifications
Working knowledge of on-device ML runtimes (Core ML, MLX, or equivalent) and the model-export lifecycle: converting trained models into shippable assets, and loading them efficiently at runtime
Working knowledge of frontier/LLM models including token-streaming inference, tokenization, and buffering strategies
Experience building applications that utilize modern ML/AI technology
Minimum Qualifications
12+ years of professional experience, with at least 5+ years in iOS / macOS application development in both Objective C and Swift
Expertise in Apple's Core iOS and Foundation frameworks
BS in Computer Science, Mathematics, Statistics, or a related field, or equivalent industry experience
Experience in shipping impactful mobile frameworks used by others outside your direct team
Experience leading the architecture and development of complex, high-performance production systems
Demonstrated ability to technically lead projects, mentor engineers, and drive cross-functional initiatives from concept to delivery
Excellent analytical, problem solving and communication skills
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 $171,600 and $302,200, 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.

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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