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Machine Learning Teaching Jobs in California (NOW HIRING)

About this role We are looking for an experienced Machine Learning Engineer to join our team and help develop cutting-edge speech recognition models that help teach language fluency. In this role you ...

Senior Machine Learning Engineer

Palo Alto, CA · On-site

$123K - $168K/yr

They are seeking a Senior Machine Learning Engineer to work on their Semantic AI Governance Engine ... frontier teacher models. • Training anomaly and action-severity models that catch novel agent ...

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Machine Learning Teaching information

See California salary details

$22.7K

$52.8K

$98.2K

How much do machine learning teaching jobs pay per year?

As of Sep 10, 2026, the average yearly pay for machine learning teaching in California is $52,775.00, according to ZipRecruiter salary data. Most workers in this role earn between $42,400.00 and $59,200.00 per year, depending on experience, location, and employer.

What is machine learning teaching?

A Machine Learning Teaching job involves educating students or professionals about machine learning concepts, algorithms, and applications. Responsibilities may include designing curricula, delivering lectures, conducting hands-on coding sessions, and mentoring learners. These roles exist in universities, online education platforms, and corporate training programs. Strong knowledge of machine learning frameworks, programming (e.g., Python, TensorFlow, PyTorch), and effective teaching skills are essential for success.

What are the typical responsibilities of machine learning teaching?

Machine Learning Teaching professionals are responsible for designing and delivering lessons on core machine learning principles, guiding students through practical projects, and assessing their progress. They may create course materials, conduct lectures and labs, and offer mentorship to students on capstone or research projects. Collaboration with other faculty or industry experts is common for curriculum updates and staying current with advancements in the field. Additionally, they often provide feedback, support diverse learners, and help students connect theory with real-world applications, ensuring a comprehensive educational experience.

What are the key skills and qualifications needed to thrive in machine learning teaching?

To thrive in a Machine Learning Teaching role, you need in-depth knowledge of machine learning concepts, proficiency with programming languages like Python or R, and an advanced degree in computer science or a related field. Experience with tools such as TensorFlow, PyTorch, Jupyter Notebooks, and familiarity with curriculum development and teaching technologies are typically required. Strong communication, patience, and the ability to clearly explain complex topics make educators especially effective. These skills ensure students gain practical expertise and solid theoretical foundations, preparing them for real-world machine learning careers.

What are the most commonly searched types of Machine Learning Teaching jobs in California?

The most popular types of Machine Learning Teaching jobs in California are:

What are popular job titles related to Machine Learning Teaching jobs in California?

For Machine Learning Teaching jobs in California, the most frequently searched job titles are:

What job categories do people searching Machine Learning Teaching jobs in California look for?

The top searched job categories for Machine Learning Teaching jobs in California are:

What cities in California are hiring for Machine Learning Teaching jobs?

Cities in California with the most Machine Learning Teaching job openings:

Infographic showing various Machine Learning Teaching job openings in California as of September 2026, with employment types broken down into 1% Internship, 1% As Needed, 74% Full Time, 22% Part Time, and 2% Contract. Highlights an 84% Physical, 2% Hybrid, and 14% Remote job distribution, with an average salary of $52,775 per year, or $25.4 per hour.

Senior Machine Learning Engineer, AI Safety

Santa Clara, CA • On-site

NVIDIA Gruppe
Computer and Electronic Product Manufacturing • 10K+ employees

$133K - $183K/yr

Other

Posted 6 days ago


Nvidia rating

9.6

Company rating: 9.6 out of 10

Based on 18 frontline employees who took The Breakroom Quiz


Job description

NVIDIA is seeking talented Deep Learning Scientists / AI Researchers / Machine Learning Engineers to join our rapidly growing AI Safety and Responsibility efforts for Enterprise Risk Management. In this role, you will take on innovative problems in machine learning, focusing specifically on scaling safety for multi-modal Large Language Models (LLMs) including advanced agentic safety.

NVIDIA is in a unique position: we develop AI-based products across multiple domains and collaborate with the world’s leading AI companies as partners and customers. This role is directed at measuring improving the security, content safety, and inclusivity of our frontier models. Because we are expanding across multiple pillars of safety, we are looking for specialists with deep expertise in one or more of the following core focus areas:

  • LLM Security: Focus on backdoors, data poisoning, latent malicious behavior, and structural model vulnerabilities.
  • Frontier Risks: Focus on advanced alignment challenges, including model deception, manipulation, and loss-of-control scenarios.
  • Agentic Safety: Focus on LLM-level safety for autonomous systems, including multi-turn tool-calling, orchestration, and execution risks.
  • Multi-turn Safety Evaluation: Focus on robust, scalable automated evaluation methodologies for conversational and iterative multi-turn use cases.
What you'll be doing:
  • Evaluation: Develop datasets and specialized models & algorithms to evaluate/benchmark models & end-to-end systems in our core safety tracks (LLM Security, Agentic Safety, Content Safety, Hallucinations, and ML Fairness).
  • Model Pre-Training, Mid-Training, Post-Training: Develop datasets and recipes for filtering training data, developing training datasets & recipes, including components like RL environments and teacher models, across our core safety tracks (LLM Security, Agentic Safety, Content Safety, Hallucinations, and ML Fairness).
  • Model & system level techniques beyond post-training: Research & deploy new approaches, like Instruction Hierarchy or Risk Detection.
  • Cross-Functional Collaboration: Partner with engineers, data scientists, and research teams across NVIDIA to scale solutions for LLM Security, Agentic Safety, Content Safety, Hallucinations, and ML Fairness.
What we need to see:
  • Master’s or PhD in Computer Science, Electrical Engineering, or a related quantitative field (or equivalent experience).
  • 8+ years of proven experience in systems software engineering or machine learning engineering.
  • Post-Training Experience: 4+ years of hands-on work experience in post-training of LLMs, including Supervised Fine-Tuning (SFT), Reinforcement Learning (RLHF/RLAIF), safety data generation techniques, ablation studies, and deploying models to production.
  • Core Safety Expertise: 1+ years of dedicated experience or research in at least one of the following areas:
    • LLM Security (backdoors, poisoning, latent behaviors).
    • Frontier Risks (deception, manipulation, loss-of-control).
    • Agentic Safety (LLM-level risks for multi-turn tool-calling/agents).
    • Multi-turn Safety Evaluation (dynamic and multi-turn alignment benchmarks).
  • Technical Mastery: In-depth knowledge of machine learning principles and frameworks (PyTorch preferred) with strong Python programming skills.
  • Multimodal Systems: Experience working with large multimodal datasets and multi-modal foundational models.
  • Soft Skills: Outstanding analytical problem-solving abilities paired with excellent collaboration and communication skills.
  • Cultural Alignment: Demonstrates behaviors that build trust: humility, transparency, respect, and intellectual honesty.
Ways to stand out from the crowd:
  • Academic Track Record: Published papers on AI Safety, alignment, or machine learning security as a primary author at top-tier conferences (NeurIPS, ICML, ICLR, ACL, etc.).
  • Community Contributions: Active contributions to open-source AI Safety tools, benchmarks, datasets, and/or models.
  • Advanced Alignment: Proven experience with alignment/fine-tuning of Vision-Language Models (VLMs) or any-to-text foundational models.

With highly competitive salaries and a comprehensive benefits package, Nvidia is widely considered to be one of the technology industry's most desirable employers. We have some of the most forward-thinking and hardworking people in the world working with us and our engineering teams are growing fast in some of the hottest state of the art fields: Deep Learning, Artificial Intelligence, and Large Language Models. If you're a creative engineer with a real passion for robust and enjoyable user experiences, we want to hear from you.

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 184,000 USD - 287,500 USD for Level 4, and 224,000 USD - 356,500 USD for Level 5.

You will also be eligible for equity and benefits.

Applications for this job will be accepted at least until September 4, 2026.

This posting is for an existing vacancy.

NVIDIA uses AI tools in its recruiting processes.

NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.

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About Nvidia

Sourced by ZipRecruiter

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It's a unique legacy of innovation that's fueled by great technology--and amazing people. Today, we're tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what's never been done before takes vision, innovation, and the world's best talent.

Industry

Computer and electronic product manufacturing

Company size

10,000+ Employees

Headquarters location

Santa Clara, CA, US