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Entry Level Embedded Ai Jobs (NOW HIRING)

XPENG

AI Intern - VLA Deployment

Santa Clara, CA · On-site

We are looking for an entry-level engineer or intern to support the optimization and deployment of ... Interest in computer architecture, performance optimization, and edge or embedded systems. * Strong ...

XPENG

AI Intern - VLA Deployment

Santa Clara, CA · On-site

We are looking for an entry-level engineer or intern to support the optimization and deployment of ... Interest in computer architecture, performance optimization, and edge or embedded systems. * Strong ...

Order.co

Vendor Operations Associate

New York, NY · On-site

Order.co leverages embedded AI agents and embedded financial products to reinvent the way ... This is an entry-level role ideal for someone who enjoys problem solving, investigating issues, and ...

Shield AI

Electrical Engineer I (SD)

San Diego, CA

Founded in 2015, Shield AI is a venture-backed defense-tech company with the mission of protecting ... We are looking for an entry level Electrical Engineer that is ready to help us bring X-BAT to first ...

Shield AI

Electrical Engineer I (SD)

San Diego, CA · On-site

$81K - $122K/yr

Founded in 2015, Shield AI is a venture-backed defense-tech company with the mission of protecting ... We are looking for an entry level Electrical Engineer that is ready to help us bring X-BAT to first ...

Shield AI

Electrical Engineer I (TX)

Dallas, TX

Founded in 2015, Shield AI is a venture-backed defense-tech company with the mission of protecting ... We are looking for an entry level Electrical Engineer that is ready to help us bring X-BAT to first ...

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Entry Level Embedded Ai information

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$70K

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$174K

How much do entry level embedded ai jobs pay per year?

As of Jun 11, 2026, the average yearly pay for entry level embedded ai in the United States is $153,383.00, according to ZipRecruiter salary data. Most workers in this role earn between $131,500.00 and $173,000.00 per year, depending on experience, location, and employer.

What is an entry level embedded AI engineer?

An entry level embedded AI engineer is a professional who helps design, develop, and implement artificial intelligence (AI) solutions on embedded systems, such as microcontrollers and other hardware devices with limited processing power. Their role often involves integrating machine learning models into devices, optimizing code for hardware constraints, and testing AI functionalities in real-world scenarios. Entry level engineers typically work under the guidance of more experienced engineers and may participate in tasks like data preprocessing, algorithm selection, firmware development, and debugging. A background in computer engineering, embedded systems, or related fields is usually required for this position.

What are some common challenges faced by entry-level embedded AI engineers, and how can they overcome them?

Entry-level embedded AI engineers often face challenges such as optimizing AI models to run efficiently on resource-constrained hardware, integrating machine learning algorithms with embedded systems, and debugging complex interactions between hardware and software. Overcoming these challenges typically involves developing strong programming skills (especially in C/C++ and Python), gaining familiarity with embedded development environments, and learning to use profiling tools to identify performance bottlenecks. Collaborating closely with senior engineers and participating in code reviews can also accelerate learning and help resolve technical hurdles more effectively.

What are the key skills and qualifications needed to thrive as an Entry Level Embedded AI Engineer, and why are they important?

To thrive as an Entry Level Embedded AI Engineer, you need a solid understanding of embedded systems, programming languages like C/C++ or Python, and basic knowledge of AI/ML concepts, typically backed by a degree in computer engineering, electrical engineering, or a related field. Familiarity with microcontrollers, real-time operating systems (RTOS), and tools such as TensorFlow Lite or PyTorch Mobile is often necessary. Strong problem-solving abilities, attention to detail, and effective teamwork set candidates apart in this role. These skills and qualities are crucial for developing efficient, reliable AI solutions that operate seamlessly on embedded hardware in real-world applications.

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

AspectEntry Level Embedded AiEntry Level Machine Learning Engineer
Required CredentialsBachelor's in Electrical Engineering, Computer Science, or related field; knowledge of embedded systemsBachelor's in Computer Science, Data Science, or related; understanding of ML algorithms
Work EnvironmentEmbedded device development, hardware-software integrationSoftware development, data modeling, algorithm implementation
Industry UsageConsumer electronics, IoT devices, automotive systems

Entry Level Embedded Ai focuses on developing AI solutions within embedded systems, often requiring hardware knowledge. In contrast, Entry Level Machine Learning Engineer emphasizes designing and implementing ML models primarily in software. Both roles typically require a related bachelor's degree and are common in tech and electronics industries, but they differ in their focus on hardware versus software development.

What engineer makes $500,000 a year?

Highly experienced engineers in specialized fields such as software engineering, data science, or embedded AI can earn salaries approaching or exceeding $500,000 annually, especially in senior or executive roles at large tech companies. These positions often require advanced skills, certifications, and significant industry experience.

Can I get an AI job with no experience?

Entry level embedded AI positions often require some foundational knowledge of programming, electronics, and machine learning concepts, but many employers are willing to consider candidates with relevant coursework, personal projects, or certifications. Gaining skills in programming languages like Python or C++, and familiarity with AI frameworks such as TensorFlow or PyTorch, can improve your chances. Internships, online courses, and hands-on projects can help build the experience needed to qualify for entry-level roles.

Is embedded AI a good career?

Embedded AI is a growing field that involves developing artificial intelligence systems integrated into hardware devices, often requiring skills in programming, hardware design, and machine learning. It offers opportunities in industries like robotics, IoT, and consumer electronics, with demand for professionals who can optimize AI algorithms for resource-constrained environments. The career typically involves continuous learning and working with tools such as embedded systems, microcontrollers, and AI frameworks.

What is the most entry-level AI job?

An entry-level AI job often involves roles such as AI intern, junior machine learning engineer, or AI research assistant. These positions typically require foundational knowledge of programming, data analysis, and basic understanding of AI concepts, with some roles offering on-the-job training or requiring certifications in relevant tools like Python or TensorFlow.
More about Entry Level Embedded Ai jobs
What cities are hiring for Entry Level Embedded Ai jobs? Cities with the most Entry Level Embedded Ai job openings:
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What job categories do people searching Entry Level Embedded Ai jobs look for? The top searched job categories for Entry Level Embedded Ai jobs are:
Entry Level Embedded Ai jobs near you
Infographic showing various Entry Level Embedded Ai job openings in the United States as of June 2026, with employment types broken down into 95% Full Time, 4% Part Time, and 1% Contract. Highlights an 66% Physical, 4% Hybrid, and 30% Remote job distribution, with an average salary of $153,383 per year, or $73.7 per hour.

AI Intern - VLA Deployment

XPENG

Santa Clara, CA • On-site

Other

Posted 7 days ago

Job description

XPENG is a leading smart technology company at the forefront of innovation, integrating advanced AI and autonomous driving technologies into its vehicles, including electric vehicles (EVs), electric vertical take-off and landing (eVTOL) aircraft, and robotics. With a strong focus on intelligent mobility, XPENG is dedicated to reshaping the future of transportation through cutting-edge R&D in AI, machine learning, and smart connectivity.
 
The Mission: Vision-Language-Action (VLA) models and foundation models are becoming increasingly important in autonomous driving, but turning research models into real-time, production-ready systems on vehicle hardware remains a major challenge. We are looking for an entry-level engineer or intern to support the optimization and deployment of multimodal models onto vehicle-grade compute platforms. This role is a strong fit for candidates who are excited about deep learning systems, model deployment, and edge inference for real-world autonomous driving applications.
Key Responsibilities
  • Support model quantization and deployment efforts for large-scale multimodal models, including Transformers and vision-language models.
  • Assist with applying model optimization techniques such as post-training quantization, quantization-aware training, pruning, and related compression methods under guidance from senior engineers.
  • Work with research and platform teams to help improve model deployability and understand hardware and runtime constraints.
  • Contribute to deployment tools, test pipelines, and runtime modules in C++ and Python for autonomous driving systems.
  • Help analyze model performance, memory usage, latency, and numerical accuracy across different deployment targets.
  • Participate in debugging and performance tuning across the model, runtime, and system stack.
  • Support validation and testing workflows to ensure stable and reliable deployment in vehicle and simulation environments.
Basic Qualifications
  • BS, MS, or PhD in Computer Science, Electrical Engineering, Robotics, or a related field.
  • Strong programming skills in C++ and/or Python.
  • Familiarity with deep learning frameworks such as PyTorch.
  • Basic understanding of model inference, deployment, or optimization workflows using tools such as ONNX, TensorRT, or similar frameworks.
  • Exposure to model compression or quantization concepts such as INT8, FP16, or related approaches.
  • Interest in computer architecture, performance optimization, and edge or embedded systems.
  • Strong problem-solving skills and the ability to learn quickly in a fast-paced engineering environment.
  • Good communication skills and the ability to collaborate with cross-functional teams.
Preferred Qualifications
  • Internship, research, or project experience in deep learning model deployment, inference acceleration, or embedded AI.
  • Familiarity with Transformers, multimodal models, or foundation models.
  • Experience with CUDA or GPU programming.
  • Exposure to autonomous driving, robotics, or real-time systems.
  • Contributions to research projects, open-source repositories, or relevant course projects.
What do we provide:
  • A fun, supportive and engaging environment.
  • Infrastructures and computational resources to support your work.
  • Opportunity to work on cutting edge technologies with the top talents in the field.
  • Opportunity to make significant impact on the transportation revolution by the means of advancing autonomous driving.
  • Competitive compensation package.
  • Snacks, lunches, dinners, and fun activities.
 
We are an Equal Opportunity Employer. It is our policy to provide equal employment opportunities to all qualified persons without regard to race, age, color, sex, sexual orientation, religion, national origin, disability, veteran status or marital status or any other prescribed category set forth in federal or state regulations.

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