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Embedded Ai Jobs in California (NOW HIRING)

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

$150 - $225/hr

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 · 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 ...

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 ...

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 ...

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All JobsEmbedded Ai JobsEmbedded Ai Jobs in California

Embedded Ai information

See California salary details

$69.1K

$151.4K

$171.7K

How much do embedded ai jobs pay per year?

As of Sep 3, 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 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 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 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 72% Full Time, 23% Part Time, and 5% 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.

Senior/Staff Software Engineer - Embedded AI Runtime

Talentlab

San Diego, CA • On-site

$150 - $230/hr

Other

Posted 12 days ago

Job description

Senior/Staff Software Engineer – Embedded AI Runtime Senior/Staff Software Engineer – Embedded AI Runtime

Location: San Diego, California
Focus: C/C++, Embedded AI, AI Runtimes, IoT

TalentLab is recruiting a Senior/Staff Software Engineer to join a team developing high-performance AI runtime software for IoT platforms.

This is a hands-on software engineering role focused on the infrastructure that enables modern AI models to execute efficiently on-device. Rather than building or training models, you'll work deeper in the AI software stack — developing runtime and SDK capabilities, integrating with edge and IoT platforms, and solving performance and compatibility challenges across hardware and software.

You'll work with advanced neural network architectures including DNNs and LLMs while collaborating closely with hardware, operating systems, compiler, driver and AI software teams.

What You'll Work On

Develop and enhance AI runtime and SDK capabilities for embedded and IoT platforms.

Lead significant features from technical design through implementation, debugging and integration.

Develop high-performance systems software primarily in C/C++.

Enable efficient execution of DNNs, LLMs and other modern neural network architectures.

Integrate AI software across multiple embedded product platforms.

Debug complex issues spanning AI runtime software, operating systems, compilers, drivers and hardware.

Work closely with platform, hardware and software engineering teams.

Evaluate new developments in AI and systems software and determine how they can improve the runtime platform.

Provide technical guidance to other engineers and contribute to broader architecture and technical planning.

What We're Looking For

Strong professional software development experience, ideally 6+ years, with deeper experience expected at the Staff level.

Advanced C/C++ development skills.

Experience developing systems software in Linux or Unix environments.

Strong software engineering fundamentals including data structures, algorithms, object-oriented design, debugging and testing.

Experience building embedded software or working close to hardware.

Ability to independently own complex technical problems and drive them through to completion.

Experience providing technical leadership or guidance within a software engineering team.

Bachelor's, Master's or PhD in Computer Science, Computer Engineering, Electrical Engineering or a related discipline.

Particularly Relevant Experience

We're especially interested in candidates with experience in one or more of the following:

AI runtimes, inference engines or AI SDK development.

Embedded or on-device AI.

DNN, CNN, RNN/LSTM, LLM or other neural network architectures.

Hardware-accelerated AI inference.

Low-level interactions between operating systems and hardware.

Linux, Android, QNX or similar embedded operating systems.

Debugging across hardware, OS, compiler and driver layers.

AI accelerator, DSP, GPU or NPU software.

Agile development and Git-based source control.

This is a strong fit for an experienced systems software engineer who wants to work underneath the models, solving the runtime, performance and platform challenges required to bring advanced AI capabilities onto real-world embedded devices.

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