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Machine Learning Research Jobs in California (NOW HIRING)

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Machine Learning Researcher

San Francisco, CA · On-site

$140K - $250K/yr

We apply deep learning research to large scale neural datasets to decode internal thought directly ... You will design and implement advanced machine learning models for EEG-based neural decoding ...

Etched

Machine Learning Research Engineer

Cupertino, CA · On-site

$252K/yr

The Machine Learning Research Engineer will propose and conduct research to optimize performance on Sohu, collaborating with hardware architects to develop software solutions that leverage the unique ...

Etched

Machine Learning Research Engineer

Cupertino, CA · On-site

$252K/yr

The Machine Learning Research Engineer will propose and conduct novel research to achieve results on Sohu, translating core mathematical operations into performant instruction sequences and ...

Apple

Machine Learning Research Engineer

Cupertino, CA · On-site

... research practical ML algorithms and build with us the next generation of video technology. We are ... frontier of machine learning - including computer vision, image and video generation ...

Apple

Machine Learning Research Engineer

Cupertino, CA · On-site

$150K - $277K/yr

... research practical ML algorithms and build with us the next generation of video technology. We are ... frontier of machine learning - including computer vision, image and video generation ...

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Machine Learning Research information

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How much do machine learning research jobs pay per hour?

As of Sep 9, 2026, the average hourly pay for machine learning research in California is $21.93, according to ZipRecruiter salary data. Most workers in this role earn between $17.07 and $23.51 per hour, depending on experience, location, and employer.

What is machine learning research?

Machine learning research is the scientific study and development of algorithms and statistical models that enable computers to perform tasks without explicit instructions, instead relying on patterns and inference. Researchers in this field work on advancing the theory, design, and application of machine learning systems, exploring areas such as deep learning, reinforcement learning, and unsupervised learning. They often publish their findings, develop new techniques, and collaborate with industry to solve real-world problems. This work is foundational to progress in artificial intelligence and has wide-ranging impacts across technology, healthcare, finance, and more.

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

To thrive as a Machine Learning Researcher, you need a strong background in mathematics, statistics, and computer science, often supported by an advanced degree (Master's or PhD) in a related field. Proficiency in programming languages like Python or R, experience with machine learning frameworks (such as TensorFlow or PyTorch), and familiarity with cloud computing platforms are typically required. Strong analytical thinking, creativity, and effective communication skills help researchers devise novel solutions and collaborate within multidisciplinary teams. These skills are essential for driving innovation, solving complex problems, and advancing the field of machine learning.

What are some common challenges faced by professionals in machine learning research and how can they be overcome?

One of the main challenges in Machine Learning Research is dealing with insufficient or poor-quality data, which can hinder model performance and generalizability. Additionally, keeping up with the rapid pace of advancements in the field requires continuous learning and adaptation. Collaborating effectively with multidisciplinary teams, such as data engineers and domain experts, is also crucial but can present communication challenges. Overcoming these obstacles typically involves building strong data pipelines, dedicating time for ongoing education, and honing collaboration and communication skills to bridge gaps between technical and non-technical stakeholders.

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

AspectMachine Learning ResearchData Scientist
Required CredentialsAdvanced degrees (Master's/PhD) in CS, ML, or related fieldsBachelor's or Master's in CS, Statistics, or related fields
Work EnvironmentResearch labs, academia, R&D departmentsBusiness environments, analytics teams, product development
Employer & Industry UsageTech companies, research institutions, universitiesTech, finance, healthcare, e-commerce, and more
Common Search & ComparisonYesYes

Machine Learning Research focuses on developing new algorithms and advancing theoretical understanding, often in academic or R&D settings. Data Scientists apply existing ML techniques to analyze data, build models, and generate insights for business decisions. While both roles require strong technical skills, Machine Learning Research emphasizes innovation and theory, whereas Data Scientists focus on practical application and data analysis.

How to become a machine learning researcher?

To become a machine learning researcher, typically a strong foundation in mathematics, statistics, and programming is required, along with advanced degrees such as a master's or Ph.D. in computer science, data science, or related fields. Gaining experience with machine learning frameworks like TensorFlow or PyTorch, publishing research, and staying current with academic literature are also important steps.

Is machine learning research a high paying job?

Machine learning research positions are generally well-paid due to the high demand for specialized skills in algorithms, data analysis, and programming languages like Python and TensorFlow. Salaries vary based on experience, education, and location, but they tend to be higher than average for many tech roles, especially in industry or academia with strong research funding.

What does a machine learning researcher do?

A machine learning researcher develops algorithms and models that enable computers to learn from data and improve their performance over time. They analyze large datasets, experiment with different techniques, and publish findings to advance the field, often using tools like Python, TensorFlow, or PyTorch. Their work typically involves both theoretical understanding and practical implementation to solve complex problems across various industries.
Machine Learning Research jobs near you
Infographic showing various Machine Learning Research 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 $45,616 per year, or $21.9 per hour.

Machine Learning Research Scientist

San Mateo, CA • On-site

Voiceflow
Software Development • 11 - 50 employees
Learn more about Voiceflowexternal

$125 - $150/hr

Other

PTO

Posted 21 days ago

Job description

Company Description

At Autoscience Institute, we create AI systems that autonomously conduct AI research. Recently, we announced the first AI agent to autonomously create peer-reviewed literature (ICLR 2025 Workshops). We are passionate about pushing the boundaries of artificial intelligence and contributing to groundbreaking advancements in the field.

Role Description

This is a full-time on-site role for a Machine Learning Research Scientist located in the San Francisco Bay Area.

  • Work directly with the founder to develop autonomous research systems that ideate, experiment, and improve customer models.
  • Collaborate with the engineering team to build and deploy production-ready research systems.
  • RL post-train and fine-tune reasoning models to automate components of the machine learning research process.
  • Stay current with the latest developments in AI research and automation.
Qualifications
  • Education: PhD or equivalent research experience in Computer Science, Machine Learning, Artificial Intelligence, or a related field. Exceptional candidates with strong research contributions are encouraged to apply regardless of formal degree.
  • Research: Publishing in top-tier AI/ML conferences (e.g., NeurIPS, ICML, ICLR, etc.) or equivalent industry experience at corporate AI research labs (Microsoft, Google, Nvidia, TRI, etc.).
  • Technical: Expertise in training machine learning models, including deep learning, reinforcement learning or genetic algorithms. This does not include building multi-agent systems using LLM APIs or building RAG-based agents.
  • Curiosity: Passion for accelerating scientific discovery through AI and willingness to explore uncharted directions with minimal supervision.
Recommended Qualifications
  • Systems: Experience building scalable and production-ready machine learning pipelines or large-scale model training (distributed model training over >64 GPUs).
  • Science: Any background or proven interested in Automated Scientific Research is a plus.
Benefits & Perks
  • Our comp is competitive against major LLM frontier lab packages
  • Unlimited PTO and flexible working arrangements
  • Conference attendance and publication support
Our Culture

We're a team of passionate researchers and engineers working to automate scientific discovery. We believe in the power of AI to accelerate scientific discovery and are committed to responsible AI development. We are an e-verify employer.

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