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Senior Machine Learning Jobs in California (NOW HIRING)

Hum AI

Senior Machine Learning Engineer

San Francisco, CA · On-site

$123K - $169K/yr

Senior Machine Learning Engineer Location: San Francisco About Hum.ai Hum.ai is building planetary superintelligence. Backed by top funds, we've raised $10M+ and are now heads down building. Join us ...

Yum

Sr Machine Learning Engineer

Irvine, CA · On-site

$112K - $154K/yr

We are seeking a hands-on Senior Machine Learning Engineer to support and enhance machine learning platforms used for media measurement and customer analytics. This role partners closely with Data ...

Yum Brands

Sr Machine Learning Engineer

Irvine, CA

$112K - $154K/yr

We are seeking a hands-on Senior Machine Learning Engineer to support and enhance machine learning platforms used for media measurement and customer analytics. This role partners closely with Data ...

Yum! Brands

Sr Machine Learning Engineer

Irvine, CA

$112K - $154K/yr

We are seeking a hands-on Senior Machine Learning Engineer to support and enhance machine learning platforms used for media measurement and customer analytics. This role partners closely with Data ...

MrBeast

Senior Machine Learning Engineer

San Francisco, CA · Hybrid

$123K - $169K/yr

Senior Machine Learning Engineer Primary: Bay Area (San Francisco / Peninsula) | Secondary: NYC The Opportunity We're doing an AI-first engineering rebuild for a company that already has an audience ...

Hive

Senior Machine Learning Engineer

San Francisco, CA · On-site

$160K - $250K/yr

Senior Machine Learning Engineer In order to execute our vision, we need to grow our team of best-in-class machine learning engineers. We are looking for developers who are excited about staying at ...

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Senior Machine Learning information

See California salary details

$24.7K

$79.2K

$161.4K

How much do senior machine learning jobs pay per year?

As of Jul 14, 2026, the average yearly pay for senior machine learning in California is $79,236.00, according to ZipRecruiter salary data. Most workers in this role earn between $41,000.00 and $101,700.00 per year, depending on experience, location, and employer.

What are some common challenges Senior Machine Learning Engineers face when deploying models to production, and how can they address them?

Senior Machine Learning Engineers often encounter challenges such as ensuring model scalability, maintaining model performance over time, and integrating models seamlessly with existing systems. Addressing these requires robust monitoring frameworks, collaboration with data engineering and DevOps teams, and implementing strategies like continuous integration/continuous deployment (CI/CD) for ML. Proactive communication with stakeholders and staying updated on the latest MLOps tools can also help ensure smooth deployment and ongoing reliability.

What are the key skills and qualifications needed to thrive as a Senior Machine Learning Engineer, and why are they important?

To thrive as a Senior Machine Learning Engineer, you need a strong background in mathematics, statistics, computer science, and experience with designing and deploying machine learning models, typically supported by an advanced degree in a related field. Proficiency with programming languages such as Python or R, ML frameworks like TensorFlow or PyTorch, and experience with cloud platforms and version control systems are commonly required. Excellent problem-solving skills, communication abilities, and leadership in collaborating with cross-functional teams make candidates stand out. These skills ensure the effective development, scaling, and integration of ML solutions to drive business value and innovation.

What are Senior Machine Learning engineers?

Senior Machine Learning engineers are experienced professionals who design, develop, and deploy advanced machine learning models and systems. They typically lead projects, mentor junior team members, and collaborate with data scientists, software engineers, and stakeholders to solve complex problems using AI and data-driven techniques. Their responsibilities include researching new algorithms, optimizing model performance, and ensuring the scalability and reliability of machine learning solutions in production environments.
What are the most commonly searched types of Machine Learning jobs in California? The most popular types of Machine Learning jobs in California are:
What are popular job titles related to Senior Machine Learning jobs in California? For Senior Machine Learning jobs in California, the most frequently searched job titles are:
What job categories do people searching Senior Machine Learning jobs in California look for? The top searched job categories for Senior Machine Learning jobs in California are:
What cities in California are hiring for Senior Machine Learning jobs? Cities in California with the most Senior Machine Learning job openings:
Senior Machine Learning jobs near you
Infographic showing various Senior Machine Learning job openings in California as of July 2026, with employment types broken down into 1% As Needed, 75% Full Time, 21% Part Time, 2% Temporary, and 1% Contract. Highlights an 89% Physical, 1% Hybrid, and 10% Remote job distribution, with an average salary of $79,236 per year, or $38.1 per hour.
Senior Machine Learning Engineer

Senior Machine Learning Engineer

TetraMem - Accelerate The World

San Jose, CA • On-site

$122K - $168K/yr

Full-time

Re-posted 3 days ago

Job description

Job Summary:
TetraMem is a company focused on accelerating the world through innovative technology. They are seeking a Senior Machine Learning Engineer to develop, optimize, and deploy lightweight machine learning models for edge AI applications, particularly in audio processing, while providing technical leadership and mentoring to junior engineers.
Responsibilities:
• Develop, optimize, and deploy lightweight machine learning models for edge AI applications, particularly for audio processing.
• Implement and optimize ML models on embedded platforms, including FPGA and custom ASIC solutions.
• Work closely with hardware and software teams to integrate ML models into production systems.
• Research and implement state-of-the-art ML techniques to enhance model efficiency, latency, and power consumption for embedded AI applications.
• Improve inference efficiency and model compression techniques, including quantization, pruning, and knowledge distillation.
• Collaborate with cross-functional teams to drive innovation and contribute to the overall system architecture.
• Provide technical leadership and mentorship to junior engineers.
• Publish research findings, present at conferences, and contribute to open-source projects when applicable.
Qualifications:
Required:
• 5+ years of relevant industry experience (or a PhD) in Computer Science, Electrical Engineering, Machine Learning, or related fields.
• Must have prior experience managing a team, serving in a Team Lead role, or demonstrating strong technical leadership and cross-functional coordination capabilities.
• Strong hands-on experience in machine learning, with a focus on edge AI, on-device inference, and deploying lightweight models on resource-constrained devices.
• Expertise in modern ML frameworks such as PyTorch, TensorFlow (including TensorFlow Lite), and JAX.
• Proficiency in Python and C/C++, with practical experience in ML model optimization and production deployment.
• Deep experience with model quantization (PTQ/QAT), pruning, knowledge distillation, sparsity, and other compression techniques for efficient edge inference.
• Hands-on experience developing for or integrating with AI chip SDKs, neural accelerators (NPUs/DSPs), or hardware-specific toolchains (e.g., NVIDIA TensorRT, Qualcomm Neural Processing SDK, ARM Ethos, or similar).
• Familiarity with edge inference runtimes (ONNX Runtime, ExecuTorch, TVM) and optimizing models for hardware constraints (latency, memory footprint, power consumption).
Preferred:
• Understanding of ML compiler and runtime design.
• Experience working with tools such as Optimum, ONNX, TensorRT, TFLite/LiteRT, ncnn, or CoreML.
• Familiarity with hardware acceleration techniques.
• Experience in embedded system development.
Company:
TetraMem is developing cutting-edge analog computing solutions for AI applications, offering exceptional performance with ultra-low power consumption. Founded in 2018, the company is headquartered in Newark, USA, with a team of 51-200 employees. The company is currently Growth Stage.

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