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Embedded Ai 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 · Hybrid

$145K - $190K/yr

  • Medical

  • PTO

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

  • Medical

  • PTO

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 ...

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 ...

InstantServe LLC

Sr. Level Firmware Engineer

San Jose, CA · On-site

$235K - $260K/yr

Define product requirements, firmware architecture, development milestones, and release plans for embedded AI products from concept through production. * Own firmware project execution, including ...

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$69.1K

$151.4K

$171.7K

How much do embedded ai jobs pay per year?

As of Aug 13, 2026, the average yearly pay for embedded ai 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 are the key skills and qualifications needed to thrive in the embedded AI position, and why are they important?

Success as an Embedded AI professional requires expertise in embedded systems, proficiency in C/C++, Python, and AI algorithms, often backed by a degree in computer engineering or related fields. Familiarity with real-time operating systems (RTOS), development tools like MATLAB, and frameworks such as TensorFlow Lite or ONNX is common, and certifications in embedded or machine learning domains are beneficial. Strong problem-solving skills, attention to detail, and the ability to communicate complex technical concepts clearly are crucial soft skills. These abilities ensure reliable integration of AI models into hardware, fostering innovation and seamless collaboration with multidisciplinary teams.

What is an embedded AI?

An Embedded AI job involves developing and optimizing artificial intelligence models to run efficiently on edge devices with limited computing power, such as IoT devices, autonomous systems, and smart sensors. Professionals in this field work on integrating AI algorithms with embedded systems, ensuring real-time performance, low power consumption, and efficient resource utilization. They collaborate with hardware and software engineers to deploy machine learning models on microcontrollers, FPGAs, or specialized AI accelerators.

What are some common challenges faced by embedded AI professionals in their day-to-day work?

Embedded AI professionals often encounter challenges such as optimizing AI algorithms to run efficiently within the memory and processing constraints of embedded hardware. They must also ensure reliable real-time performance and work to address issues with power consumption and system integration. Collaboration with hardware engineers, data scientists, and software developers is essential to align AI models with platform capabilities. Overcoming these challenges requires continuous learning and adaptability, but the role offers significant opportunities to make impactful contributions to emerging technologies.

What are the most commonly searched types of Embedded Ai jobs in California?

The most popular types of Embedded Ai jobs in California are:

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

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

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

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

What cities in California are hiring for Embedded Ai jobs?

Cities in California with the most Embedded Ai job openings:

Embedded Ai jobs near you
Infographic showing various Embedded Ai job openings in California as of August 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person 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 7 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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