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Nvidia Machine Learning Jobs in California (NOW HIRING)

Senior Software Engineer, AI Networking

Santa Clara, CA · On-site

$143K - $189K/yr

NVIDIA seeks a senior software engineer to join the AI Networking co-design and benchmark R&D team. In this pivotal role, the candidate is responsible for building and productizing machine learning ...

What You'll Do * Design and implement scalable machine learning pipelines for large-scale 3D ... We bring deep experience from organizations such as DeepMind, NASA JPL, Boston Dynamics, NVIDIA ...

What You'll Do * Design and implement scalable machine learning pipelines for large-scale 3D ... We bring deep experience from organizations such as DeepMind, NASA JPL, Boston Dynamics, NVIDIA ...

What You'll Do * Design and implement scalable machine learning pipelines for large-scale 3D ... We bring deep experience from organizations such as DeepMind, NASA JPL, Boston Dynamics, NVIDIA ...

... in machine learning and data science. A Solutions Architect is the first line of technical expertise between NVIDIA and our customers so you will engage directly with developers, researchers, and ...

Solutions Architect, AI and ML

Santa Clara, CA · On-site

$74 - $97.50/hr

... in machine learning and data science. A Solutions Architect is the first line of technical expertise between NVIDIA and our customers so you will engage directly with developers, researchers, and ...

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Nvidia Machine Learning information

How much do NVIDIA machine learning engineers make?

NVIDIA machine learning engineers typically earn between $100,000 and $160,000 annually, depending on experience, location, and skill level. Senior roles or those with specialized expertise in deep learning and GPU programming can earn higher salaries, often exceeding $180,000. Compensation may also include bonuses and stock options in competitive tech environments.

What is a Nvidia Machine Learning job?

A Nvidia Machine Learning job involves developing and optimizing AI models, deep learning frameworks, and GPU-accelerated applications. Engineers in this role work on cutting-edge research, building scalable ML solutions, and improving performance on Nvidia hardware like GPUs and AI accelerators. They collaborate with software and hardware teams to enhance AI capabilities across industries such as gaming, healthcare, and autonomous systems. Strong coding skills in Python, C++, and experience with ML frameworks like TensorFlow or PyTorch are often required.

What are the key skills and qualifications needed to thrive in the Nvidia Machine Learning position, and why are they important?

To thrive in an Nvidia Machine Learning role, a deep understanding of machine learning algorithms, proficiency in programming languages like Python or C++, and a solid background in mathematics or computer science are essential. Experience with Nvidia's CUDA, TensorRT, cuDNN, and familiarity with modern deep learning frameworks such as TensorFlow or PyTorch are highly valued, as are relevant certifications in AI or data science. Strong problem-solving skills, teamwork, and effective communication distinguish top candidates in collaborative, fast-paced environments. These skills are crucial for developing and optimizing AI solutions that leverage Nvidia’s advanced hardware and software platforms.

Does NVIDIA do machine learning?

Nvidia offers extensive tools and platforms for machine learning, including GPUs optimized for training and deploying models. Many machine learning engineers and researchers use Nvidia hardware and software frameworks like CUDA and cuDNN to accelerate AI development. The company also provides AI-focused products and solutions for various industries.

Is it hard to get hired at NVIDIA?

Getting hired for a machine learning role at NVIDIA can be competitive due to the company's focus on advanced technology and innovation. Candidates typically need strong technical skills in deep learning, programming, and relevant experience, along with a solid educational background. The hiring process often involves multiple interviews and technical assessments to evaluate expertise and problem-solving abilities.

What are some common challenges faced by professionals in Nvidia Machine Learning roles?

One common challenge in Nvidia Machine Learning roles is optimizing models to fully leverage GPU architectures for both performance and efficiency, which requires continuous learning as the technology rapidly evolves. Team members often work on complex, large-scale projects that demand close collaboration across software, hardware, and research divisions. Navigating the fast pace of innovation and contributing effectively to cross-functional teams is essential for success. However, these challenges also make the role exciting and offer excellent opportunities for professional growth and hands-on experience with state-of-the-art AI solutions.

Is ML a high paying job?

Machine Learning roles, including positions like Nvidia Machine Learning engineers, are generally well-paid due to high demand for specialized skills in AI, data analysis, and programming. Salaries vary based on experience, location, and company, but these jobs tend to offer above-average compensation compared to many other tech roles.
What are the most commonly searched types of Nvidia Machine Learning jobs in California? The most popular types of Nvidia Machine Learning jobs in California are:
What cities in California are hiring for Nvidia Machine Learning jobs? Cities in California with the most Nvidia Machine Learning job openings:
Infographic showing various Nvidia Machine Learning job openings in California as of June 2026, with employment types broken down into 2% As Needed, 51% Full Time, 45% Part Time, and 2% Temporary. Highlights an 88% Physical, 6% Hybrid, and 6% Remote job distribution.
Machine Learning Engineer, AI Safety & Security - Supporting NVIDIA - Santa Clara (Hybrid)

Machine Learning Engineer, AI Safety & Security - Supporting NVIDIA - Santa Clara (Hybrid)

Sustainable Talent

Santa Clara, CA • On-site, Remote

$90 - $130/hr

Other

PTO

Posted 14 days ago


Job description

Sustainable Talent is partnering with Nvidia a global leader who's been transforming computer graphics, PC gaming, and accelerated computing for over 25 years. We are looking for a ML Engineer, LLM Safety & Security to support our client's team based out of in Santa Clara, CA with remote/ hybrid work options. 

This is a full-time (W-2) contract role. We offer competitive pay $90/hr - $130/hr based on factors like experience, education, location, etc. and provide full benefits, PTO, and amazing company culture!

As a Machine Learning Engineer, you'll work alongside NVIDIA's research and engineering teams, focused on AI Safety for LLMs, including multi-lingual, multi-modal, and reasoning models.  We value expertise in data science paired with a robust data engineering foundation.   This role is directed at assessing, and improving the safety and inclusivity of our LLM models in a scalable fashion.  We seek someone proficient in programming and scripting for comprehensive data manipulation, analysis, and model fine-tuning.  We believe in proactive problem-solving, minimal supervision, and being exceptional teammates who collaborate, think, and learn as one unit. Let's make a difference together!

What you'll be doing:

  • Develop datasets and moderator models for evaluating LLM models and end-to-end systems for Content Safety, ML Fairness. These LLM models can be txt-to-txt or multimodal-to-txt.
  • Develop datasets for training LLM models with SFT and RL techniques, for Content Safety, ML Fairness, Security and more.
  • Research and implement cutting-edge techniques for bias detection and mitigation in LLMs and systems.
  • Define and track key metrics for responsible LLM behavior and usage.
  • Follow the best practices of automation, monitoring, scale, safety.
  • Contribute to our repositories and develop safety tools to help ML teams be more effective.
  • Data pre-processing and analysis: Collaborate with data scientists and data engineers to collect, clean, pre-process, and transform large and wide datasets.
  • Conduct exploratory data analysis (EDA) to uncover insights and identify patterns that boost the model performance.
  • Collaborate with multidisciplinary teams: Collaborate with product engineers, data scientists, and analysts to understand business requirements and translate them into machine learning solutions.

What we need to see:

  • Bachelor's or Master's Degree in Computer Science or related field or equivalent experience.
  • 2+ years of work experience as a Machine Learning Engineer or Deep Learning Scientist or a similar role, with a consistent record of successfully delivering ML solutions.
  • Strong programming skills in languages such as Python. Experience with frameworks like TensorFlow, PyTorch, or scikit-learn.
  • Proficiency in data manipulation, analysis, and visualization using tools like NumPy and pandas.
  • Deep understanding of machine learning algorithms, statistical models, and data structures.
  • Familiarity with software development practices and version control systems (e.g., Git).
  • Good at problem solving and analytical ability.
  • Excellent collaboration and communication skills.

Ways to stand out from the crowd:

  • Experience with GenAI Security including Prompt Injection Stability, Model Extraction, Confidentiality/Data Extraction, Integrity, Availability and Adversarial Robustness.
  • Experience with one or more of the following areas within Content Safety: Hate/Harassment, Sexualized, Harmful/Violent, or other specific areas from your application.
  • Experience with alignment/fine-tuning of LLMs - including regular LLMs as well as VLMs  (Vision Language Model) or any-to-text
  • Experience with multimodal and/or multilingual Content Safety, legal and regulatory compliance.
  • Passion for AI and a demonstrated commitment to advancing the field through innovative research, prior scientific research and publication experience.

Sustainable Talent is a M/F+, disabled, and veteran equal employment opportunity and affirmative action employer.