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Machine Learning Jobs in Santa Rosa, CA (NOW HIRING)

DD-Scientific Data

Scientific Data Engineer

Bodega Bay, CA

$135K - $163K/yr

Support machine learning and AI use of biological data by making it well-structured, documented, and efficiently accessible * Work closely with the community of developers of the Neurodata Without ...

KenkoTech Futures

AI Scientist

Sonoma, CA · On-site

This is a rare opportunity to join an exceptionally strong research organisation working at the intersection of large-scale machine learning and modern biology. You'll collaborate with world-class AI ...

KenkoTech Futures

AI Scientist

Santa Rosa, CA · On-site

This is a rare opportunity to join an exceptionally strong research organisation working at the intersection of large-scale machine learning and modern biology. You'll collaborate with world-class AI ...

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Showing results 21-40

All JobsMachine Learning JobsMachine Learning Jobs in Santa Rosa, CA

Machine Learning information

See Santa Rosa, CA salary details

$27.9K

$46.6K

$96.2K

How much do machine learning jobs pay per year?

As of Sep 4, 2026, the average yearly pay for machine learning in Santa Rosa, CA is $46,558.00, according to ZipRecruiter salary data. Most workers in this role earn between $35,500.00 and $50,300.00 per year, depending on experience, location, and employer.

What is a machine learning?

A Machine Learning job involves developing algorithms and models that enable computers to learn from data and make predictions or decisions without explicit programming. Professionals in this field work with large datasets, design and train machine learning models, and optimize them for performance and accuracy. Roles often require knowledge of programming languages like Python or R, experience with frameworks like TensorFlow or PyTorch, and an understanding of statistics and data science principles. Machine learning engineers and data scientists collaborate with software developers and domain experts to build AI-driven solutions for various industries.

What are the typical day-to-day responsibilities in a machine learning role?

As a machine learning professional, your daily tasks may include data preprocessing, developing and training models, evaluating performance metrics, and experimenting with algorithms to optimize results. You’ll often collaborate closely with data scientists, software engineers, and business stakeholders to align technical solutions with organizational goals. Regular activities can also involve deploying models to production, monitoring performance, and troubleshooting any issues that arise post-deployment. Staying up to date with recent ML research and participating in team discussions or code reviews are also common parts of the job.

What are the key skills and qualifications needed to thrive in a machine learning position?

To thrive in Machine Learning, you need a solid background in mathematics, statistics, programming (especially Python or R), and a formal degree in computer science, data science, or a related field. Experience with popular ML frameworks (such as TensorFlow, PyTorch, or Scikit-learn), version control, and relevant certifications like AWS Certified Machine Learning are highly valued. Strong problem-solving skills, curiosity, clear communication, and the ability to work both independently and within multidisciplinary teams make candidates stand out. These skills and qualities are essential for developing robust models, staying updated with technology advancements, and collaborating effectively on complex projects.

Is machine learning a high paying job?

Machine learning engineers and specialists are generally among the higher-paid roles in the tech industry due to their advanced skills in algorithms, programming, and data analysis. Salaries vary based on experience, location, and industry, but the field is known for competitive compensation compared to many other tech roles.

What jobs can I get with machine learning?

With a background in machine learning, you can pursue roles such as machine learning engineer, data scientist, AI researcher, or data analyst. These positions typically require skills in programming languages like Python or R, knowledge of algorithms, and experience with tools like TensorFlow or PyTorch.

What are the most commonly searched types of Machine Learning jobs in Santa Rosa, CA?

The most popular types of Machine Learning jobs in Santa Rosa, CA are:

What job categories do people searching Machine Learning jobs in Santa Rosa, CA look for?

The top searched job categories for Machine Learning jobs in Santa Rosa, CA are:

What cities near Santa Rosa, CA are hiring for Machine Learning jobs?

Cities near Santa Rosa, CA with the most Machine Learning job openings:

Machine Learning jobs near you
Infographic showing various Machine Learning job openings in Santa Rosa, CA as of August 2026, with employment types broken down into 1% As Needed, 74% Full Time, 22% Part Time, and 3% Contract. Highlights an 89% Physical, 2% Hybrid, and 9% Remote job distribution, with an average salary of $46,558 per year, or $22.4 per hour.

Machine Learning Engineer, Frontier AI Evaluation (Contract)

Cobalt

Santa Rosa, CA • On-site

Other

Posted 5 days ago

Key responsibilities

  • Produce written reasoning traces on real ML engineering tasks, diagnosing failures and explaining the reasoning clearly.

  • Author ML engineering problems and task environments with automated success checks, including multi-file and multi-step tasks.

  • Evaluate model-generated ML code and configurations, ranking solutions, and identifying points of failure.

Job description

About the role:

Cobalt is seeking machine learning engineers to produce the expert reasoning, task environments, and evaluation data used to train and assess frontier AI models on real ML engineering work.

This opportunity is suited to practitioners rather than only researchers: ML engineers, applied scientists, MLOps and platform engineers, and data engineers who have trained, deployed, and maintained models in production. A PhD is welcome but not required, and hands-on delivery experience counts for more here than publication record.

You do not need prior experience in data annotation or AI research. What matters is that you can diagnose why a pipeline or a training run is failing, decide what the right fix is, and explain both clearly enough for another engineer to follow.


What you'll do:

Depending on the project, you may:

  • Produce written reasoning traces on real ML engineering tasks, capturing how you diagnose a failing training run, a data pipeline defect, or a serving regression, including what you rule out and why
  • Author non-trivial ML engineering problems and task environments with checks that verify success automatically, including multi-file and multi-step tasks
  • Evaluate model-generated ML code and configurations, ranking solutions, explaining what makes the stronger one stronger, and identifying the point at which the approach goes wrong
  • Assess whether a proposed solution actually addresses the failure, and identify fixes that pass the immediate check but mask the underlying problem, degrade performance, or would not survive review
  • Design rubrics and partial-credit criteria for scoring multistep engineering tasks, and classify observed failures into a consistent taxonomy

Projects follow their own guidelines, formatting conventions, and quality standards, and you will work with feedback from reviewers and lab research teams.


Required qualifcations:

  • Several years of hands-on experience building, training, and deploying machine learning systems in production, with a track record you can point to
  • Strong coding ability in Python, plus working command of at least one deep learning framework such as PyTorch or JAX, and comfort reading unfamiliar codebases
  • Depth in at least one area, for example large-scale training and distributed compute, data pipelines and feature infrastructure, model serving and inference optimization, evaluation and monitoring, or fine-tuning and post-training workflows
  • Solid debugging discipline, including the ability to isolate a failure across data, model, and infrastructure rather than guessing at it
  • Ability to explain each step of your reasoning clearly in writing, and to produce work another engineer could reproduce and review


Why join Cobalt AI:

  • Advance frontier AI where it counts. Apply your expertise to data that frontier labs cannot obtain any other way, where your reasoning directly shapes how the next generation of models works through technical problems.
  • Grow professionally. Expand your influence through evaluation projects, advisory roles, and research collaborations, while developing a working understanding of how frontier models are trained and assessed.
  • Work with a top-tier network. Collaborate with researchers and engineers from leading institutions and labs on high-impact, flexible work.
  • Set your own schedule. Flexible 10 to 40 hour weeks that fit around your existing work and your life.
  • Competitive pay. Rates vary by project and are determined by a number of factors, including scope, skillset, and experience.

Apply