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Edge Ai Machine Learning Jobs in California (NOW HIRING)

Nace AI

Machine Learning Engineer

Palo Alto, CA · On-site

Nace AI is a company focused on machine learning solutions, and they are seeking a Machine Learning Engineer to translate cutting-edge research into scalable, production-ready solutions. The role ...

Vectra

Director of AI

San Jose, CA · On-site

Advanced degree in Computer Science, AI/Machine Learning, IT development/ Data Science, or a related field preferred. Vectra is at the forefront of cybersecurity AI innovation - applying cutting-edge ...

Nace AI

Machine Learning Engineer

Palo Alto, CA · On-site

As a Machine Learning Engineer, you will play a central role in translating cutting-edge machine ... AI's strategic objectives. Key Responsibilities: * Design, build, and maintain end-to-end ML ...

Vectra

Director of AI

San Jose, CA · On-site

Advanced degree in Computer Science, AI/Machine Learning, IT development/ Data Science, or a related field preferred. Vectra is at the forefront of cybersecurity AI innovation - applying cutting-edge ...

Altera

Machine Learning Engineer

San Jose, CA · On-site

About the Role We are seeking a Machine Learning Engineer to help drive the development ... Familiarity with edge AI inference on FPGAs and neuro-symbolic AI techniques. * Strong ...

Otter.ai

Machine Learning Engineer

Mountain View, CA · On-site +1

$196K - $221K/yr

The Opportunity Do you want to lead projects to build and deploy cutting-edge AI technology to help ... As a Machine Learning Engineer, you'll bring your strong software engineering mindset to machine ...

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Edge Ai Machine Learning information

What is an Edge AI Machine Learning?

An Edge AI Machine Learning job involves developing and deploying machine learning models directly on edge devices, such as IoT sensors, mobile devices, and embedded systems. This role requires expertise in optimizing AI models for low-power, low-latency environments while ensuring real-time processing. Professionals in this field work with frameworks like TensorFlow Lite, ONNX, and OpenVINO to implement AI solutions efficiently. They must also handle challenges like model compression, hardware acceleration, and data privacy.

What are some typical challenges faced in an Edge AI Machine Learning role, and how can I prepare for them?

One of the most common challenges in Edge AI Machine Learning is optimizing models to run efficiently on hardware with limited resources, while maintaining acceptable accuracy and speed. You may encounter constraints related to memory, processing power, and connectivity, which require creative engineering and a deep understanding of both machine learning and embedded systems. Collaborating closely with hardware engineers, data scientists, and software developers is typical, as solutions often span multiple technical disciplines. To prepare, staying current with advancements in model compression, quantization, and edge deployment technologies will help you tackle these challenges with confidence.

What are the key skills and qualifications needed to thrive in the Edge AI Machine Learning position?

To thrive as an Edge AI Machine Learning professional, you need a strong background in machine learning algorithms, embedded systems, and proficiency with programming languages such as Python or C++. Familiarity with edge computing platforms (like NVIDIA Jetson, Google Coral), frameworks (TensorFlow Lite, ONNX), and certifications in AI or ML can greatly enhance your qualifications. Strong problem-solving abilities, collaboration, and effective communication skills are important for adapting solutions to diverse environments and working cross-functionally. These abilities enable the successful deployment of efficient and robust AI models directly on devices, meeting the unique challenges of real-time, resource-constrained settings.

How to become an edge AI machine learning engineer?

To become an edge AI machine learning engineer, develop strong skills in machine learning, embedded systems, and programming languages like Python and C++. Gain experience with hardware platforms such as NVIDIA Jetson or Raspberry Pi, and learn to optimize models for low-power, resource-constrained environments. Earning certifications in AI, embedded systems, or IoT can also enhance your qualifications.

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

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

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

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

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

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

What cities in California are hiring for Edge Ai Machine Learning jobs?

Cities in California with the most Edge Ai Machine Learning job openings:

Edge Ai Machine Learning jobs near you
Infographic showing various Edge Ai Machine Learning job openings in California as of August 2026, with employment types broken down into 21% Internship, and 79% Full Time. Highlights an 100% In-person job distribution.

Staff Machine Learning Engineer - AI Foundation

XPENG

Santa Clara, CA • On-site

Full-time

Re-posted 3 days ago

Job description

XPENG is a leading smart technology company at the forefront of innovation, integrating advanced AI and autonomous driving technologies into its vehicles, including electric vehicles (EVs), electric vertical take-off and landing (eVTOL) aircraft, and robotics. With a strong focus on intelligent mobility, XPENG is dedicated to reshaping the future of transportation through cutting-edge R&D in AI, machine learning, and smart connectivity.
We are looking for a full-time Machine Learning Engineer - AI Foundation, with deep knowledge and strong enthusiasm towards establishing a state-of-art ML infrastructure for training very large foundation model and accelerating model training/inference.
Our mission is to solve the autonomous driving problem. You will work with a team of talented software engineers, machine learning engineers and research scientists to push the boundary of state-of-art machine learning models which will enable the next-generation E2E solution of autonomous driving.
Job Responsibilities:
  • Optimize transformer-based LLMs for low-latency and high-throughput inference.
  • Optimize kernels and model graphs using tools like CUDA, Triton, and custom fused operators.
  • Implement and benchmark (Quantization, Knowledge distillation, structured and unstructured pruning, KV-cache optimization, etc.).
  • Deploy optimized models across GPUs, CPUs, and edge acceleators.
  • Contribute to internal tooling and documentation for model optimization flows.

Minimum Skill Requirements:
  • Master in CS/CE/EE, or equivalent, with 5-8 years of industry experience.
  • Good knowledge of PyTorch.
  • Knowledge of transformer architecture and ways to accelerate the training and inference of transformer models.

Preferred Skill Requirements:
  • Previous experience in the autonomous driving industry.
  • Knowledge of Torchscript and Nvidia TensorRT.
  • Strong programming skills in Python and C++
  • Familiarity with GPU CPU, NPU, DSP architecture.
  • Deep understanding of memory bandwidth, compute bottlenecks, and hardware-aware model optimization
  • Being efficiently in solving complex problems collaboratively on larger teams

What do we provide:
  • A fun, supportive and engaging environment.
  • Infrastructures and computational resources to support your work.
  • Opportunity to work on cutting edge technologies with the top talents in the field.
  • Opportunity to make significant impact on the transportation revolution by the means of advancing autonomous driving.
  • Competitive compensation package.
  • Snacks, lunches, dinners, and fun activities.

The base salary range for this full-time position is $215,280 - $364,320, in addition to bonus, equity and benefits. Our salary ranges are determined by role, level, and location. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position across all US locations. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training.
We are an Equal Opportunity Employer. It is our policy to provide equal employment opportunities to all qualified persons without regard to race, age, color, sex, sexual orientation, religion, national origin, disability, veteran status or marital status or any other prescribed category set forth in federal or state regulations.

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