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Machine Learning Research Engineer Jobs in California

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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 Sep 13, 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

Cupertino, CA • On-site

Etched
See more jobs at Etchedexternal

$2.0K/mo

Full-time

Medical, Dental, Vision

Re-posted 20 days ago

Job description

About Etched

Etched is building AI chips that are hard-coded for individual model architectures. Our first product (Sohu) only supports transformers, but has an order of magnitude more throughput and lower latency than a B200. With Etched ASICs, you can build products that would be impossible with GPUs, like real-time video generation models and extremely deep & parallel chain-of-thought reasoning agents. Etched Labs is the organization within Etched whose mission is to democratize generative AI, pushing the boundaries of what will be possible in a post-Sohu world. 

Key responsibilities

  • 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 performant instruction sequences for Sohu
  • Develop deep architectural knowledge informing best-in-the-world software performance on Sohu HW, collaborating with HW architects and designers.
  • Co-design and finetune emerging model architectures for highest efficiency on Sohu
  • Guide and contribute to the Sohu software stack, performance characterization tools, and runtime abstractions by implementing frontier models using Python and Rust.

Representative projects

  • Propose and implement a novel test time compute algorithm that leverages Sohu's unique capabilities to unlock a product could never be achieved on a typical GPU
  • Implement diffusion models on Sohu to achieve GPU-impossible latencies that allow for real-time image generation
  • Optimize model instructions and scheduling algorithms to optimize for utilization, latency, throughput, and/or a mix of these metrics. 
  • Implement model-specific inference-time acceleration techniques such as speculative decoding, tree search, KV cache sharing, priority scheduling, etc by interacting with the rest of the inference serving stack.

You may be a good fit if you have

  • An ML Research background with interests in HW co-design
  • Experience with Python, Pytorch, and / or JAX
  • Familiarity with transformer model architectures and/or inference serving stacks (vLLM, SGLang, etc.) and/or experience working in distributed inference/training environments
  • Experience working cross-functionally in diverse software and hardware organizations

Strong candidates may also have

  • ML Systems Research and HW Co-design backgrounds
  • Published inference-time compute research and/or efficient ML research
  • Experience with Rust
  • Familiarity with GPU kernels, the CUDA compilation stack and related tools, or other hardware accelerators

Benefits

  • Full medical, dental, and vision packages, with 100% of premium covered
  • Housing subsidy of $2,000/month for those living within walking distance of the office
  • Daily lunch and dinner in our office
  • Relocation support for those moving to Cupertino

How we're different

Etched believes in the Bitter Lesson. We think most of the progress in the AI field has come from using more FLOPs to train and run models, and the best way to get more FLOPs is to build model-specific hardware. Larger and larger training runs encourage companies to consolidate around fewer model architectures, which creates a market for single-model ASICs.

We are a fully in-person team in Cupertino, and greatly value engineering skills. We do not have boundaries between engineering and research, and we expect all of our technical staff to contribute to both as needed.

Apply