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Machine Learning Research Jobs in California (NOW HIRING)

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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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 Engineer

Cupertino, CA • On-site

Etched
See more jobs at Etchedexternal

$2.0K/mo

Full-time

Medical, Dental, Vision

Re-posted 16 days ago

Key responsibilities

  • Propose and conduct novel research to improve Sohu's performance on transformer models.

  • Translate mathematical operations from transformer models into efficient instruction sequences for Sohu hardware.

  • Guide and contribute to the Sohu software stack, performance characterization tools, and runtime abstractions by implementing models using Python and Rust.

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