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Embedded Ai Engineer Jobs in California (NOW HIRING)

Sonatus

Staff AI Engineer, Edge AI

Sunnyvale, CA · On-site

Lead the architecture for the edge ML pipeline and mentor junior engineers on best practices for embedded AI. Requirements: * Bachelor's degree in Computer Science, Electrical Engineering, Software ...

Sonatus

Senior Staff AI Engineer, Edge AI

Mountain View, CA · Hybrid

$123K - $169K/yr

Lead the architecture for the edge ML pipeline and mentor junior engineers on best practices for embedded AI. Requirements: * Bachelor's degree in Computer Science, Electrical Engineering, Software ...

Sonatus

Senior Staff AI Engineer, Edge AI

Sunnyvale, CA · On-site

$122K - $168K/yr

Lead the architecture for the edge ML pipeline and mentor junior engineers on best practices for embedded AI. Requirements: * Bachelor's degree in Computer Science, Electrical Engineering, Software ...

Sonatus

Senior Staff AI Engineer, Edge AI

San Jose, CA · Hybrid

$122K - $168K/yr

Lead the architecture for the edge ML pipeline and mentor junior engineers on best practices for embedded AI. Requirements: * Bachelor's degree in Computer Science, Electrical Engineering, Software ...

Sonatus

Senior Staff AI Engineer, Edge AI

San Francisco, CA · Hybrid

$123K - $169K/yr

Lead the architecture for the edge ML pipeline and mentor junior engineers on best practices for embedded AI. Requirements: * Bachelor's degree in Computer Science, Electrical Engineering, Software ...

Sonatus

Senior Staff AI Engineer, Edge AI

Sunnyvale, CA · On-site

$124K - $170K/yr

Lead the architecture for the edge ML pipeline and mentor junior engineers on best practices for embedded AI. Requirements: * Bachelor's degree in Computer Science, Electrical Engineering, Software ...

Programmers.io

Java Full Stack AI Developer

Sunnyvale, CA · On-site

$61.50 - $79.50/hr

* Drive the adoption of embedded AI, moving beyond simple API calls to integrating local LLMs and vector databases into the application layer. Evangelize usage of AI tools to accelerate developer ...

Socket.dev

Edge Computing AI Engineer

Sunnyvale, CA · On-site

$100 - $150/hr

Edge Computing AI Engineer - Remote Bright Vision Technologies is a technology consulting and ... Solid understanding of mobile and embedded hardware architectures. * Experience deploying ML models ...

New

Nexxa.ai

Applied AI Engineer

Sunnyvale, CA · On-site

They are seeking Applied AI Engineers to deliver high-impact technical solutions by working closely ... embedded technical roles. • Experience with vector databases, embedding pipelines, or retrieval ...

Codvo.ai

$203K/yr

Edge AI & Embedded Systems About the Role: We are looking for a highly motivated and technically proficient Edge AI Engineer with a strong background in Edge AI devices, computer vision algorithms ...

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Showing results 21-40

All JobsEmbedded Ai Engineer JobsEmbedded Ai Engineer Jobs in California

Embedded Ai Engineer information

See California salary details

$69.1K

$151.4K

$171.7K

How much do embedded ai engineer jobs pay per year?

As of Aug 19, 2026, the average yearly pay for embedded ai engineer in California is $151,375.00, according to ZipRecruiter salary data. Most workers in this role earn between $129,800.00 and $170,700.00 per year, depending on experience, location, and employer.

What is an embedded AI engineer?

An Embedded AI Engineer is a professional who designs, develops, and implements artificial intelligence (AI) algorithms and models directly onto embedded systems, such as microcontrollers or edge devices. Their work involves optimizing AI solutions to run efficiently on hardware with limited computing resources, power, and memory. They collaborate with hardware engineers and software developers to integrate machine learning, computer vision, or other AI functionalities into products like smart appliances, autonomous vehicles, or IoT devices. Their expertise helps bring intelligent features directly to devices, enabling real-time decision-making without needing constant cloud connectivity.

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

To thrive as an Embedded AI Engineer, you need expertise in embedded systems, AI/ML algorithms, programming languages like C/C++ and Python, and typically a degree in computer engineering or a related field. Familiarity with development tools such as TensorFlow Lite, ONNX, embedded Linux, and microcontroller platforms is essential, along with experience deploying AI models on resource-constrained devices. Strong problem-solving, collaboration, and communication skills help you work effectively in multidisciplinary teams and address real-world challenges. These skills ensure efficient integration of AI into embedded systems, enabling innovative, high-performance solutions for edge computing.

How does an embedded AI engineer typically collaborate with hardware and software teams during a project?

Embedded AI Engineers work closely with both hardware and software teams to ensure AI models are efficiently integrated into resource-constrained devices. They often collaborate with hardware engineers to optimize model performance based on device limitations like memory and processing power. At the same time, they coordinate with software developers to design efficient firmware and manage data pipelines. Regular cross-functional meetings and code reviews are common to address integration challenges and maintain alignment throughout the project lifecycle.

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

CriteriaEmbedded Ai EngineerMachine Learning Engineer
Required CredentialsBachelor's in Electrical Engineering, Computer Science, or related; knowledge of embedded systemsBachelor's or Master's in Computer Science, Data Science, or related; strong programming skills
Work EnvironmentEmbedded systems, IoT devices, hardware integrationData centers, cloud platforms, software development environments
Employer & Industry UsageConsumer electronics, automotive, IoT companiesTech firms, startups, research institutions
Common Search & ComparisonYesNo

Embedded Ai Engineers focus on integrating AI algorithms into embedded hardware and IoT devices, requiring knowledge of hardware constraints and embedded programming. Machine Learning Engineers develop models primarily for software applications and data analysis. While both roles involve AI, Embedded Ai Engineers specialize in hardware-software integration within embedded systems, whereas Machine Learning Engineers work on developing and deploying AI models in software environments.

What are popular job titles related to Embedded Ai Engineer jobs in California?

For Embedded Ai Engineer jobs in California, the most frequently searched job titles are:

What job categories do people searching Embedded Ai Engineer jobs in California look for?

The top searched job categories for Embedded Ai Engineer jobs in California are:

What cities in California are hiring for Embedded Ai Engineer jobs?

Cities in California with the most Embedded Ai Engineer job openings:

Embedded Ai Engineer jobs near you
Infographic showing various Embedded Ai Engineer job openings in California as of August 2026, with employment types broken down into 81% Full Time, 17% Part Time, and 2% Contract. Highlights an 63% Physical, 4% Hybrid, and 33% Remote job distribution, with an average salary of $151,375 per year, or $72.8 per hour.

Staff AI Engineer, Edge AI

Sonatus

Sunnyvale, CA • On-site

Other

Medical, Dental, Vision, Life, Retirement, PTO

Re-posted 5 days ago

Job description

At Sonatus, we're driving the transformation to AI-enabled software-defined vehicles. Traditional automotive software methods can't keep pace with consumer expectations shaped by the mobile industry-where features evolve rapidly, update seamlessly, and improve continuously. That's why leading OEMs trust Sonatus to accelerate this shift. Our technology is already in production across more than 8 million vehicles on the road today and rapidly expanding.
Headquartered in Sunnyvale, CA, with 250+ employees worldwide, Sonatus combines the agility of a fast-growing company with the scale and impact of an established partner. Backed by strong funding and proven by global deployment, we're solving some of the most interesting and complex challenges in the industry. Join us and help redefine what's possible as we shape the future of mobility.
Role Summary:
Sonatus is a global leader in the automotive industry, providing key technologies that enable intelligent AI-defined vehicles. Our solutions are already on the road with millions of vehicles, and we are quickly expanding our offerings for production-grade AI on the Edge. We are looking for a great Staff AI Engineer to join our seasoned AI team and lead the development of Edge AI for in-vehicle self-aware health monitoring and prediction. In this role, you will build and deploy AI models that analyze continuous data generated in the vehicle during the day-to-day operation, including system logs, traces, and vehicle internal signals (Ethernet and CAN) to detect and predict the health of different sub-systems and anticipate failures in real-time. You will own the end-to-end ML pipeline-from data ingestion and model training to deployment on resource-constrained edge devices and model optimization. You will work in a fast-paced startup environment where your code will directly impact fleet reliability and build the next generation of the self-aware vehicle. You will be expected to collaborate with other leading developers who have a deep understanding and expertise of vehicle software and systems, and other AI developers working on MLOps and integration of AI models on vehicles expected to be on the road today. Expect to experiment with cutting-edge model architectures and best-in-class development tools.
This is a hybrid role out of our Sunnyvale, CA, where you will be expected to work in our office 3 days a week.
Responsibilities:
  • Build and train AI Edge models (e.g., Transformers, LLM, CNN, LSTM, Trees) to process unstructured application logs, kernel traces, and multi-modalities.
  • Integrating ML flows, including cloud-based LLM APIs (Gemini, OpenAI, Claude), with emphasis on synthetic data creation.
  • Develop algorithms to automatically cluster log patterns and detect software regressions, race conditions, or crash precursors.
  • Design unsupervised and supervised learning models (e.g., Autoencoders, Isolation Forests) to monitor time-series data from CAN bus and on-board sensors.
  • Implement logic to correlate signal anomalies (e.g., ADAS drifts, sensor spikes, latency jitters) across different modalities with system events to identify root causes.
  • Port and optimize PyTorch/TensorFlow models into production-grade models for execution on CPU/GPU-bound targets or embedded NPUs.
  • Apply quantization, pruning, distillation, and memory optimization to ensure models run within strict RAM/Flash budgets.
  • Define the data strategy for on-device filtering: pre-processing on device and decide which data is processed locally versus processed in the cloud.
  • Lead the architecture for the edge ML pipeline and mentor junior engineers on best practices for embedded AI.
Requirements:
  • Bachelor's degree in Computer Science, Electrical Engineering, Software Engineering, or a related field.
  • 7+ years in Machine Learning Engineering, with 3+ years focused on Edge AI or Embedded Systems.
  • Proven experience mentoring junior engineers in software development.
  • Expert Python (for training) and decent working knowledge of modern C++ (C++14/17 for inference).
  • Deep proficiency with PyTorch or TensorFlow, and experience with inference engines like ONNX, TFLite, or TVM.
  • Experience with NLP techniques for textual data parsing, sequence modeling (RNN/GRU), vector store, or lightweight LLMs/SLMs.
  • Experience with libraries like scikit-learn, tslearn, or statsmodels for anomaly detection on sensor data.
  • Proven ability to lead technical projects from concept to production in an ambiguous, fast-paced environment. Ability to communicate with stakeholders and articulate trade-offs.
  • Experience deploying to Edge environments (e.g., ARM-based), managing memory manually, and working with limited compute resources.
  • Candidates with a strong Computer Vision (CV) / ADAS track record are highly encouraged to apply!
Desired Skills:
  • MS/PhD in Computer Science, Engineering, or related fields.
  • Familiarity with Edge systems and preferably automotive formats (CAN, DBC, UDS, SOME/IP, or MQTT.
  • Understanding of Linux/QNX kernel logs (dmesg), process states, and OS-level debugging.
  • Experience with NVIDIA TensorRT, Qualcomm SNPE.

Sunnyvale HQ Benefits & Perks Offered:
  • Health care plan (Medical, Dental & Vision)
  • Flexible and Dependent Care Expense program
  • Retirement plan (401k)
  • Life Insurance (Basic, Voluntary & AD&D)
  • Unlimited paid time off per year, 14+ paid holidays
  • Hybrid office work arrangement
  • Complimentary lunches, snacks, and beverages during on-site working days
  • Wellness benefit allowance
  • Phone & Internet reimbursement
  • Computer Accessory Allowance

The posted salary range is a general guideline and represents a good faith estimate of what Sonatus ("Company") could reasonably expect to pay for a base salary for this position. The pay offered to a selected candidate will be determined based on factors such as (but not limited to) the scope and responsibilities of the position, the qualifications of the selected candidate, departmental budget availability, geographic location and external market pay for comparable jobs. The Company reserves the right to modify this range in the future, as needed, as market conditions change.
Base Salary Pay Range
$197,500-$272,000 USD

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