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Machine Learning Research Analyst Jobs in California

Etched

Machine Learning Research Engineer

Cupertino, CA · On-site

$2.0K/mo

Propose and conduct novel research to achieve results on Sohu that are unviable on GPUs * Translate core mathematical operations from the most popular Transformer-based models into maximally ...

Etched

Machine Learning Research Engineer

Cupertino, CA · On-site

$2.0K/mo

Propose and conduct novel research to achieve results on Sohu that are unviable on GPUs * Translate core mathematical operations from the most popular Transformer-based models into maximally ...

Etched

Machine Learning Research Engineer

Cupertino, CA · On-site

$2.0K/mo

Propose and conduct novel research to achieve results on Sohu that are unviable on GPUs * Translate core mathematical operations from the most popular Transformer-based models into maximally ...

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Machine Learning Research Analyst information

What does a machine learning research analyst do?

A Machine Learning Research Analyst studies and develops algorithms that enable computers to learn from data. They analyze large datasets, experiment with different machine learning models, and evaluate their performance to solve complex problems. Their work often involves staying updated with the latest research in artificial intelligence and applying these advancements to real-world applications. The role typically requires strong programming, statistical, and problem-solving skills.

How does a machine learning research analyst typically collaborate with data scientists and engineers during a project?

As a Machine Learning Research Analyst, you’ll often work closely with data scientists to interpret complex data sets, develop hypotheses, and validate models. Collaboration with engineers is essential to ensure that research findings are correctly implemented into production systems. Regular meetings, code reviews, and joint problem-solving sessions are common, allowing you to provide analytical insights while engineers focus on system scalability and deployment. This teamwork helps bridge the gap between theoretical research and practical application, leading to impactful solutions.

What are the key skills and qualifications needed to thrive as a machine learning research analyst, and why are they important?

To thrive as a Machine Learning Research Analyst, you need a solid background in mathematics, statistics, and computer science, often demonstrated by a relevant degree or research experience. Proficiency with programming languages like Python or R, familiarity with machine learning libraries (such as TensorFlow or PyTorch), and experience with data analysis tools are typically required. Strong analytical thinking, creativity, and effective communication skills help distinguish top performers in this role. These capabilities enable analysts to develop innovative models, interpret complex data, and clearly present actionable insights to stakeholders.

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

AspectMachine Learning Research AnalystData Scientist
Required CredentialsBachelor's or Master's in Computer Science, Data Science, or related fields; knowledge of ML algorithmsBachelor's or Master's in Data Science, Statistics, or related fields; strong programming skills
Work EnvironmentResearch labs, academic institutions, tech companies focusing on ML innovationsBusiness environments, analytics teams, tech companies applying data insights
Employer & Industry UsageResearch institutions, AI startups, tech giants focusing on ML advancementsCorporate, finance, healthcare, and e-commerce sectors leveraging data for decision-making

While both roles require strong analytical skills and knowledge of machine learning, Machine Learning Research Analysts focus more on developing and testing new algorithms in research settings. Data Scientists apply these techniques to solve practical business problems, often working directly with large datasets to generate insights and support decision-making.

What cities in California are hiring for Machine Learning Research Analyst jobs?

Cities in California with the most Machine Learning Research Analyst job openings:

Infographic showing various Machine Learning Research Analyst job openings in California as of August 2026, with employment types broken down into 1% As Needed, 72% Full Time, 23% Part Time, 1% Temporary, and 3% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution.

Machine Learning Research Scientist (1 Year Fixed Term)

Stanford, CA • On-site

Stanford University
Colleges, Universities, and Professional Schools • 11 - 50 employees
Learn more about Stanford Universityexternal

Full-time

Re-posted 15 days ago

Stanford University rating

7.9

Company rating: 7.9 out of 10

Based on 25 frontline employees who took The Breakroom Quiz

Job description

Job Summary:
Stanford University is a leading research institution dedicated to understanding natural intelligence through artificial intelligence. The Machine Learning Research Scientist will design and implement large-scale multimodal deep learning architectures and guide teams in establishing frameworks for brain foundation models, contributing to the project’s goal of aligning AI models with human-like neural representations.
Responsibilities:
• Design and implement large-scale multimodal deep learning architectures that relate sensory inputs to neuronal correlates of perception, action, and cognition
• Develop novel computational approaches for training and optimizing frontier models on unprecedented amounts of neural data
• Provide technical leadership in distributed training systems and model optimization techniques
• Guide cross-functional teams in establishing technical frameworks and evaluation metrics for brain foundation models
• Communicate research findings through publications, presentations, workshops and research blogs
• Stay ahead of the latest developments in machine learning and neuroscience, and propose innovative solutions to advance the project's goals
Qualifications:
Required:
• Ph.D. in Computer Science, Machine Learning, Computational Neuroscience, or related field plus 2+ years post-Ph.D. research experience
• At least 2+ years of practical experience in training, fine-tuning, and using multi-modal deep learning models
• Strong publication record in top-tier machine learning conferences and journals, particularly in areas related to multi-modal modeling
• Strong programming skills in Python and deep learning frameworks
• Demonstrated ability to lead research projects and mentor others
• Ability to work effectively in a collaborative, multidisciplinary environment
• Bachelor's degree and five years of relevant experience, or combination of education and relevant experience
• Expert knowledge of the principles of engineering and related natural sciences
• Demonstrated project leadership experience
• Demonstrated experience leading and/or managing technical professionals
Preferred:
• Background in theoretical neuroscience or computational neuroscience
• Experience in processing and analyzing large-scale, high-dimensional data of different sources
• Experience with cloud computing platforms (e.g., AWS, GCP, Azure) and their machine learning services
• Familiarity with big data and MLOps platforms (e.g. MLflow, Weights & Biases)
• Familiarity with training, fine tuning, and quantization of LLMs or multimodal models using common techniques and frameworks (LoRA, PEFT, AWQ, GPTQ, or similar)
• Experience with large-scale distributed model training frameworks (e.g. Ray, DeepSpeed, HF Accelerate, FSDP)
Company:
Stanford University is a teaching and research university that focuses on graduate programs in law, medicine, education, and business. Founded in 1885, the company is headquartered in Stanford, USA, with a team of 10001+ employees. The company is currently Late Stage.

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