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Machine Learning Research Engineer Jobs in California

Advatix Inc.

Machine Learning Engineer

San Mateo, CA · On-site

$110K - $165K/yr

Machine Learning Engineer / Research Engineer Pay: $$110,000 - $165,000 Base Salary + Equity Shift: N/A Location: San Mateo, CA (Peninsula) - Onsite Preferred Schedule: Full time, Permanent Role Visa ...

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 ...

Profluent

Machine Learning Research Engineer

Emeryville, CA · On-site +1

$237K/yr

We're looking for an experienced Machine Learning Engineer to build and improve the models and ML ... Partner with ML and protein design scientists to prototype research ideas and bring them into ...

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All JobsMachine Learning Research Engineer JobsMachine Learning Research Engineer Jobs in California

Machine Learning Research Engineer information

See California salary details

$36.5K

$104.6K

$140.6K

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

As of Sep 12, 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.

Machine Learning Research Engineer

Menlo Park, CA • On-site

Voltai
See more jobs at Voltaiexternal

Full-time

Re-posted 19 days ago

Job description

Job Summary:
Voltai is the leading AI company building agentic systems and frontier foundation models for semiconductor and electronics design. The Machine Learning Research Engineer will be responsible for training foundation models, fine-tuning large language models, and designing innovative algorithms for various tasks in AI and machine learning.
Responsibilities:
• Train foundation models from scratch that integrate multiple modalities.
• Train and fine-tune large language models (LLMs) and multi-modal models (MMMs).
• Design and implement models capable of solving discrete and continuous constraint reasoning tasks and graph-related challenges.
• Structure and optimize data for model training, ensuring high performance and efficiency.
• Create data synthetically and collect it from human interactions for diverse tasks.
• Build and optimize both open-source and proprietary models, considering various constraints.
• Design and execute innovative search and retrieval algorithms.
Qualifications:
Required:
• Strong software engineering skills, particularly in Pytorch, Python, CUDA, and Triton.
• Deep expertise in machine learning, including sequence modeling, generative models, and model architecture.
• Experience in pre-training and fine-tuning large multi-modal models.
• Proven track record of publications in top AI conferences and competitions.
• Experience implementing research papers into production code.
• Familiarity with the latest state-of-the-art techniques, including prompting and inference-time search methods.
• Experience in developing and managing large-scale machine learning systems.
• Proficiency in configuring and optimizing hardware and operating systems for maximum performance.
• Experience building distributed training systems for AI models in high-performance computing (HPC) clusters.
Company:
AI models for electronics Founded in , the company is headquartered in , , with a team of 11-50 employees. The company is currently Early Stage.

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