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Machine Learning Research Engineer Jobs in California

shefsolutionsllc

Machine Learning Engineer

Cupertino, CA · On-site

$143 - $264/hr

Description We are seeking an experienced Machine Learning Research Engineer to design and apply state-of-the-art research in machine learning for data-centric problems! Your responsibilities will ...

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Machine Learning Research Engineer information

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$36.5K

$104.6K

$140.6K

How much do machine learning research engineer jobs pay per year?

As of Aug 22, 2026, the average yearly pay for machine learning research engineer in California is $104,624.00, according to ZipRecruiter salary data. Most workers in this role earn between $102,600.00 and $102,600.00 per year, depending on experience, location, and employer.

What does a machine learning research engineer do?

A Machine Learning Research Engineer develops and improves machine learning models, conducts research to advance AI techniques, and implements scalable algorithms. They work at the intersection of applied research and engineering, leveraging mathematical and statistical methods to optimize performance. Their role involves experimenting with new architectures, analyzing large datasets, and collaborating with data scientists and software engineers to deploy models into production.

What are the key skills and qualifications needed to thrive as a machine learning research engineer?

A Machine Learning Research Engineer typically needs a strong background in computer science, mathematics, and statistics, often with a graduate degree in a related field. Proficiency in programming languages such as Python or C++, experience with machine learning frameworks like TensorFlow or PyTorch, and familiarity with tools for data analysis are crucial, along with relevant certifications being a plus. Strong problem-solving skills, collaboration, and effective communication help drive innovative research and facilitate teamwork. These competencies are essential for developing advanced machine learning models, staying current with evolving technologies, and effectively translating research into real-world applications.

What are some common challenges faced by machine learning research engineers in their daily work?

Machine Learning Research Engineers often encounter challenges such as sourcing and preparing large, high-quality datasets, tuning complex model architectures, and ensuring reproducibility of experimental results. They work closely with cross-functional teams, including data scientists and software engineers, to deploy models in production environments and must frequently adapt to rapidly evolving research. Keeping up with the latest scientific literature and integrating new algorithms into ongoing projects can be demanding but is also rewarding. This collaborative, fast-paced environment provides constant opportunities for learning and professional development.

What are popular job titles related to Machine Learning Research Engineer jobs in California?

For Machine Learning Research Engineer jobs in California, the most frequently searched job titles are:

What job categories do people searching Machine Learning Research Engineer jobs in California look for?

The top searched job categories for Machine Learning Research Engineer jobs in California are:

Machine Learning Research Engineer jobs near you
Infographic showing various Machine Learning Research Engineer job openings in California as of August 2026, with employment types broken down into 1% As Needed, 80% Full Time, 18% Part Time, and 1% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $104,624 per year, or $50.3 per hour.

Research Engineer, Machine Learning (Reinforcement Learning)

Anthropic

San Francisco, CA

$241K/yr

Full-time

Re-posted 17 days ago

Job description

About the teams

Our Reinforcement Learning teams lead Anthropic's reinforcement learning research and development, playing a critical role in advancing our AI systems. We've contributed to all Claude models, with significant impacts on the autonomy and coding capabilities of Claude Sonnet 4.5 and Opus 4.5. Our work spans several key areas:

  • Developing systems that enable models to use computers effectively
  • Advancing code generation through reinforcement learning
  • Pioneering fundamental RL research for large language models
  • Building scalable RL infrastructure and training methodologies
  • Enhancing model reasoning capabilities

We collaborate closely with Anthropic's alignment and frontier red teams to ensure our systems are both capable and safe. We partner with the applied production training team to bring research innovations into deployed models, and are dedicated to implement our research at scale. Our Reinforcement Learning teams sit at the intersection of cutting-edge research and engineering excellence, with a deep commitment to building high-quality, scalable systems that push the boundaries of what AI can accomplish.

About the Role

As a Research Engineer within Reinforcement Learning, you will collaborate with a diverse group of researchers and engineers to advance the capabilities and safety of large language models. This role blends research and engineering responsibilities, requiring you to both implement novel approaches and contribute to the research direction. You'll work on fundamental research in reinforcement learning, creating 'agentic' models via tool use for open-ended tasks such as computer use and autonomous software generation, improving reasoning abilities in areas such as mathematics, and developing prototypes for internal use, productivity, and evaluation.

Representative projects:
  • Architect and optimize core reinforcement learning infrastructure, from clean training abstractions to distributed experiment management across GPU clusters. Help scale our systems to handle increasingly complex research workflows.
  • Design, implement, and test novel training environments, evaluations, and methodologies for reinforcement learning agents which push the state of the art for the next generation of models.
  • Drive performance improvements across our stack through profiling, optimization, and benchmarking. Implement efficient caching solutions and debug distributed systems to accelerate both training and evaluation workflows.
  • Collaborate across research and engineering teams to develop automated testing frameworks, design clean APIs, and build scalable infrastructure that accelerates AI research.
You may be a good fit if you:
  • Are proficient in Python and async/concurrent programming with frameworks like Trio
  • Have experience with machine learning frameworks (PyTorch, TensorFlow, JAX)
  • Have industry experience in machine learning research
  • Can balance research exploration with engineering implementation
  • Enjoy pair programming (we love to pair!)
  • Care about code quality, testing, and performance
  • Have strong systems design and communication skills
  • Are passionate about the potential impact of AI and are committed to developing safe and beneficial systems
Strong candidates may have:
  • Familiarity with LLM architectures and training methodologies
  • Experience with reinforcement learning techniques and environments
  • Experience with virtualization and sandboxed code execution environments
  • Experience with Kubernetes
  • Experience with distributed systems or high-performance computing
  • Experience with Rust and/or C++
Strong candidates need not have:
  • Formal certifications or education credentials
  • Academic research experience or publication history

Deadline to apply: None. Applications will be reviewed on a rolling basis. 

Apply