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Embedded Machine Learning Engineer Jobs in Santa Clara, CA

Role Summary We are seeking a highly motivated Machine Learning Engineer with a strong background in model architecture design and algorithm development, ideally with experience in scientific domains ...

Machine Learning Engineer

Sunnyvale, CA · On-site

$150K - $277K/yr

Description Apple's Video Computer Vision (VCV) Face and Body technologies team is looking for a skilled Machine Learning Engineer with experience developing ML models for computer vision and ...

Description Apple's Video Computer Vision (VCV) Face and Body technologies team is looking for a skilled Machine Learning Engineer with experience developing ML models for computer vision and ...

... bound targets or embedded NPUs. * Apply quantization, pruning, distillation, and memory ... Machine Learning Engineering, with 3+ years focused on Edge AI or Embedded Systems. * Proven ...

Bee Genius is building the future of work and is seeking an AI/Machine Learning Engineer to join their team. The role involves developing and implementing machine learning models and algorithms to ...

Machine Learning Engineer

Mountain View, CA · On-site

$175K - $275K/yr

About the Role We're hiring our first Machine Learning Engineer in the United States, a foundational role that will shape how Abaka builds, trains, and optimizes multimodal AI systems. You will own ...

Machine Learning Engineer

Mountain View, CA · On-site

$175K - $275K/yr

About the Role We're hiring our first Machine Learning Engineer in the United States, a foundational role that will shape how Abaka builds, trains, and optimizes multimodal AI systems. You will own ...

Machine Learning Engineer

Mountain View, CA · On-site

$175K - $275K/yr

About the Role We're hiring our first Machine Learning Engineer in the United States, a foundational role that will shape how Abaka builds, trains, and optimizes multimodal AI systems. You will own ...

Machine Learning Engineer

Dublin, CA · On-site

$90 - $130/hr

We are seeking machine learning engineers to join our team full-time. As part of your role, you will help us build pipelines of data collection, data extraction, data filtering/synthetic data ...

They are seeking a Machine Learning Engineer to translate research into scalable solutions, collaborating with teams to architect robust systems and integrate AI-driven features into applications.

Machine Learning Engineer

Mountain View, CA · On-site

$175K - $275K/yr

About the Role We're hiring our first Machine Learning Engineer in the United States, a foundational role that will shape how Abaka builds, trains, and optimizes multimodal AI systems. You will own ...

We are seeking machine learning engineers to join our team full-time. As part of your role, you will help us build pipelines of data collection, data extraction, data filtering/synthetic data ...

Lead Machine Learning Engineer

San Jose, CA

$120K - $158K/yr

Lead Machine Learning Engineer As a Capital One Machine Learning Engineer (MLE), you'll be part of an Agile team dedicated to productionizing machine learning applications and systems at scale. You ...

... bound targets or embedded NPUs. * Apply quantization, pruning, distillation, and memory ... Machine Learning Engineering, with 3+ years focused on Edge AI or Embedded Systems. * Proven ...

Showing results 41-60

Embedded Machine Learning Engineer information

See Santa Clara, CA salary details

$82.2K

$180.1K

$204.4K

How much do embedded machine learning engineer jobs pay per year?

As of Sep 6, 2026, the average yearly pay for embedded machine learning engineer in Santa Clara, CA is $180,139.00, according to ZipRecruiter salary data. Most workers in this role earn between $154,400.00 and $203,200.00 per year, depending on experience, location, and employer.

What does an embedded machine learning engineer do?

An Embedded Machine Learning Engineer designs and implements machine learning models that can run efficiently on embedded systems, such as microcontrollers and edge devices. Their work involves optimizing algorithms to fit within the resource constraints of these devices, integrating ML models into hardware, and ensuring real-time performance. They collaborate closely with hardware engineers and software developers to deploy intelligent features in products like smart sensors, IoT devices, and autonomous systems.

What are the key skills and qualifications needed to thrive as an embedded machine learning engineer?

To thrive as an Embedded Machine Learning Engineer, you need expertise in machine learning algorithms, embedded systems programming (C/C++ or Python), and a solid understanding of hardware constraints, usually supported by a degree in computer science, electrical engineering, or related fields. Familiarity with tools like TensorFlow Lite, ONNX, microcontroller SDKs, and experience with real-time operating systems (RTOS) are typically required. Strong problem-solving, communication skills, and the ability to collaborate across multidisciplinary teams help you stand out in this role. These skills are crucial for efficiently deploying intelligent models on resource-constrained devices, ensuring optimal performance and seamless integration in real-world applications.

What are some common challenges faced by embedded machine learning engineers when deploying models to hardware devices?

One of the main challenges for Embedded Machine Learning Engineers is optimizing machine learning models to run efficiently on devices with limited memory, processing power, and energy capacity. Ensuring real-time performance while maintaining accuracy often requires model quantization, pruning, or using lightweight architectures. Additionally, engineers must carefully manage hardware-software integration and address issues like compatibility with various microcontrollers and ensuring secure, reliable updates for deployed models. Close collaboration with hardware engineers and software developers is essential to overcome these challenges and deliver robust embedded AI solutions.

What is the difference between Embedded Machine Learning Engineer vs Firmware Engineer?

AspectEmbedded Machine Learning EngineerFirmware Engineer
Required CredentialsBachelor's/Master's in Computer Science, Electrical Engineering, or related; knowledge of ML frameworksBachelor's in Electrical Engineering, Computer Engineering, or related; embedded systems experience
Work EnvironmentDevelops ML models for embedded devices, often in IoT or smart devicesDesigns and implements low-level firmware for hardware devices
Industry UsageTech companies, IoT, consumer electronics, automotiveConsumer electronics, automotive, industrial equipment

The Embedded Machine Learning Engineer focuses on integrating machine learning models into embedded systems, while the Firmware Engineer specializes in developing low-level software for hardware devices. Both roles require embedded systems knowledge but differ in their core focus and skill sets.

What cities near Santa Clara, CA are hiring for Embedded Machine Learning Engineer jobs?

Cities near Santa Clara, CA with the most Embedded Machine Learning Engineer job openings:

Infographic showing various Embedded Machine Learning Engineer job openings in Santa Clara, CA as of July 2026, with employment types broken down into 1% As Needed, 72% Full Time, 23% Part Time, 1% Temporary, and 3% Contract. Highlights an 88% Physical, 2% Hybrid, and 10% Remote job distribution, with an average salary of $180,139 per year, or $86.6 per hour.

Machine Learning Engineer

Gotion, Inc.

Fremont, CA

Full-time

Re-posted 28 days ago


Key responsibilities

  • Design and implement machine learning and deep learning models tailored to research needs

  • Prototype, evaluate, and benchmark algorithms such as Transformers, LLMs, and hybrid architectures

  • Collaborate with scientists and domain experts to incorporate physical constraints or scientific priors into models


Job description

About The Team
The Product Development Team at Gotion Illinois New Energy Inc. focuses on the design and development of advanced battery products for next-generation energy storage system (ESS) and electric vehicle (EV) applications. We lead the full product development cycle, integrating mechanical, electrical, thermal, and control system to create high-performance battery solutions, along with comprehensive system integration, validation, and certification activities.

Role Summary

We are seeking a highly motivated Machine Learning Engineer with a strong background in model architecture design and algorithm development, ideally with experience in scientific domains such as battery technology, energy systems, or related physical sciences. This is a fully on-site role based in Manteno, IL focused on building innovative ML models from the ground up.

You will collaborate closely with cross-disciplinary R&D teams to develop and deploy machine learning solutions that address real-world challenges in advanced materials, electrochemical systems, and high-throughput data environments.

Essential Duties and Responsibilities:

  • Design and implement novel machine learning and deep learning models tailored to internal research needs
  • Prototype and evaluate state-of-the-art algorithms, including Transformers, LLMs, and hybrid model architectures
  • Conduct rigorous experimentation, benchmarking, and ablation studies
  • Collaborate with battery scientists and domain experts to incorporate physical constraints or scientific priors into modeling
  • Contribute to internal documentation and present research outcomes to technical and leadership teams
  • Track and integrate advances from the ML research community to ensure technical excellence

Required Qualifications:

  • Ph.D. (preferred) or M.S. in Machine Learning, Computer Science, Electrical Engineering, Applied Mathematics, or a closely related field
  • Demonstrated expertise in model development, optimization, and algorithmic innovation
  • Proficiency in Python and ML libraries/frameworks such as PyTorch, TensorFlow, etc.
  • Solid understanding of learning theory concepts such as regularization, generalization, loss functions, and evaluation metrics
  • Experience working with scientific or time-series datasets, especially in battery, materials, or energy domains, is highly desirable
  • A publication record in top-tier ML conferences (e.g., NeurIPS, ICML, ICLR, CVPR) is a strong plus
  • Excellent communication, collaboration, and problem-solving skills in interdisciplinary environments

The US base salary range for this full-time position is $80,000.00 - $90,000.00 + 15% bonus + benefits. Our salary ranges are determined by role, level, and location. The range displayed on each job posting reflects the minimum and maximum target 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. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.  Â