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Embedded Ai Engineer Jobs in California (NOW HIRING)

E-Space

AI / Embedded ML Engineer

Saratoga, CA · On-site

$145K - $190K/yr

As an AI / Embedded ML Engineer, you will be responsible for the full lifecycle of AI/machine learning on resource-constrained hardware, including data ingestion, model development, optimization, and ...

E-Space

AI / Embedded ML Engineer

Saratoga, CA · On-site

$145K - $190K/yr

The AI / Embedded ML Engineer will be responsible for the full lifecycle of AI/machine learning on resource-constrained hardware, including data ingestion, model development, optimization, and ...

E-Space

AI / Embedded ML Engineer

Saratoga, CA · On-site

$145K - $190K/yr

E-Space is focused on making connectivity from space universally accessible and is seeking an AI / Embedded ML Engineer to work on the full lifecycle of AI/machine learning on resource-constrained ...

E-Space

AI / Embedded ML Engineer

Saratoga, CA · On-site

$145K - $190K/yr

As an AI / Embedded Engineer, you will be responsible for the full lifecycle of AI/ machine learning on resource-constrained hardware. This includes data ingestion, model development, optimization ...

E-Space

AI / Embedded ML Engineer

Saratoga, CA · Hybrid

$145K - $190K/yr

As an AI / Embedded Engineer, you will be responsible for the full lifecycle of AI/ machine learning on resource-constrained hardware. This includes data ingestion, model development, optimization ...

E-Space

AI / Embedded ML Engineer

Saratoga, CA · On-site

$145K - $190K/yr

As an AI / Embedded Engineer, you will be responsible for the full lifecycle of AI/machine learning on resource-constrained hardware, including data ingestion, model development, optimization, and ...

E-Space

AI / Embedded ML Engineer

Saratoga, CA · On-site

$145K - $190K/yr

As an AI / Embedded Engineer, you will be responsible for the full lifecycle of AI/ machine learning on resource-constrained hardware. This includes data ingestion, model development, optimization ...

E-Space

AI / Embedded ML Engineer

Saratoga, CA · Hybrid

$150K - $225K/yr

As an AI / Embedded Engineer, you will be responsible for the full lifecycle of AI/ machine learning on resource-constrained hardware. This includes data ingestion, model development, optimization ...

Sonatus

Staff AI Engineer, Edge AI

Sunnyvale, CA · Hybrid

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 · 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

Staff AI Engineer, Edge AI

San Jose, CA · Hybrid

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

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

Staff AI Engineer, Edge AI

Mountain View, CA · Hybrid

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

Staff AI Engineer, Edge AI

San Francisco, CA · Hybrid

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 ...

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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.

Embedded AI Engineer - Android Automotive (On-Device Intelligence)

Applied Intuition

Sunnyvale, CA • On-site

$153K - $201K/yr

Full-time

Re-posted 12 days ago

Job description

Job Summary:
Applied Intuition is a Silicon Valley company focused on powering the future of physical AI. The Embedded AI Engineer will be responsible for the end-to-end lifecycle of embedded ML systems on the Android Automotive platform, ensuring models operate safely and predictably in real-world conditions.
Responsibilities:
• Deploy and run production-grade ML inference and learning systems on Android Automotive (AAOS)
• Implement on-device multimodal LLMs, including schema design and safe dispatch to local vehicle APIs
• Integrate models using TensorFlow Lite, ONNX Runtime, or specialized vendor SDKs
• Profile and optimize models for strict latency, memory, power, and thermal budgets
• Instrument runtime performance across CPU, GPU, and NPU acceleration layers
• Design safety boundaries and guardrails for model outputs, including tool-call allowlists and fallback logic
• Interface directly with vehicle signals, sensors, and system services using C++ and JNI
Qualifications:
Required:
• BS, MS, or PhD in Computer Science, Electrical Engineering, or a related technical field
• 3+ years of experience shipping ML inference on embedded, mobile, or automotive platforms
• Strong proficiency in C++ and experience with native Android integration (JNI)
• Expertise in model optimization techniques such as quantization, pruning, and compilation
• Experience integrating LLM function calling or tool execution with structured outputs
• Hands-on experience with Android system services or Android Automotive OS (AAOS)
• Deep understanding of edge constraints including real-time behavior and memory pressure
Preferred:
• Experience with Snapdragon Automotive, ARM Ethos, or specialized NPU pipelines
• Background in running quantized LLMs on-device using llama.cpp or TFLite transformers
• Familiarity with functional safety concepts (ISO 26262), sandboxing, or policy enforcement
• Experience bridging cloud-trained models to resource-constrained embedded runtimes
Company:
Applied Intuition provides software infrastructure to safely develop, test, and deploy autonomous vehicles
 at scale. Founded in 2017, the company is headquartered in Mountain View, USA, with a team of 1001-5000 employees. The company is currently Late Stage.

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