1

Embedded Machine Learning Internship Jobs in Berkeley, CA

Through custom underwater cameras, computer vision, and machine learning we are able to quantify ... Improve our embedded Linux build and deployment process * Develop software to automate hardware ...

You'll collaborate directly with machine learning engineers and researchers - your job is to make ... You'll be deeply embedded with ML teams, understanding their workflows and building infrastructure ...

Showing results 41-60

Embedded Machine Learning Internship information

See Berkeley, CA salary details

$31.2K

$52.1K

$107.8K

How much do embedded machine learning internship jobs pay per year?

As of Sep 7, 2026, the average yearly pay for embedded machine learning internship in Berkeley, CA is $52,141.00, according to ZipRecruiter salary data. Most workers in this role earn between $39,800.00 and $56,300.00 per year, depending on experience, location, and employer.

What is an embedded machine learning internship?

An Embedded Machine Learning Internship is a temporary position designed for students or recent graduates to gain hands-on experience in developing and deploying machine learning algorithms on embedded systems. These internships typically involve working with hardware such as microcontrollers, sensors, or edge devices, and using specialized tools to optimize machine learning models for low-power and resource-constrained environments. Interns collaborate with engineers and data scientists to create efficient, real-world AI solutions that run directly on devices rather than relying on cloud computing. This role helps bridge the gap between theoretical machine learning concepts and practical implementation on embedded platforms.

What are some typical projects or tasks I might work on during an embedded machine learning internship?

During an Embedded Machine Learning Internship, you can expect to work on projects such as optimizing machine learning models to run efficiently on hardware with limited resources, integrating AI algorithms into embedded systems (like microcontrollers or IoT devices), and performing real-time data processing. You'll likely collaborate closely with software engineers and hardware designers to test models on physical devices, debug performance issues, and contribute to documentation. These experiences provide practical exposure to the challenges of deploying AI in real-world, resource-constrained environments and help build skills valuable for a future career in embedded AI.

What are the key skills and qualifications needed to thrive as an embedded machine learning intern, and why are they important?

To thrive as an Embedded Machine Learning Intern, you need a background in computer science, electrical engineering, or a related field with strong programming skills in C/C++ and Python, as well as foundational knowledge of machine learning algorithms. Experience with embedded systems development tools (such as ARM Cortex, Raspberry Pi, or Arduino), version control systems, and familiarity with ML frameworks like TensorFlow Lite or Edge Impulse is often required. Analytical thinking, problem-solving ability, and effective teamwork are vital soft skills for success in this role. These skills and qualities are crucial for efficiently developing, optimizing, and deploying machine learning solutions on resource-constrained embedded platforms.

What are popular job titles related to Embedded Machine Learning Internship jobs in Berkeley, CA?

For Embedded Machine Learning Internship jobs in Berkeley, CA, the most frequently searched job titles are:

What cities near Berkeley, CA are hiring for Embedded Machine Learning Internship jobs?

Cities near Berkeley, CA with the most Embedded Machine Learning Internship job openings:

Embedded Software Engineer

Aquabyte

San Francisco, CA • On-site

$130K - $190K/yr

Full-time

Medical, Dental, Vision, Retirement, PTO

Re-posted 15 days ago


Key responsibilities

  • Interface with sensors, cameras, networks, and hardware to create reliable data collection and processing systems

  • Develop software on ARM-based embedded platforms using C, C++, Python, Golang, or Rust

  • Develop software to automate hardware testing procedures and build diagnostic and configuration tools for field teams


Job description

Our mission
Aquabyte is on a mission to revolutionize the sustainability and efficiency of aquaculture. By making fish farming cheaper and more viable than livestock production, we aim to mitigate one of the biggest causes of climate change and help prepare our planet for impending population growth. Aquaculture is the single fastest growing food-production sector in the world, and now is the time to define how technology is used to harvest the sea and preserve it for generations to come.
We are a diverse, mission-driven team that is eager to work alongside kindred spirits. If this vision makes you smile, gives you goosebumps, or otherwise inspires you please get in touch.
Our product
We are currently focused on helping salmon farmers better understand their fish populations and make environmentally-sound decisions. Through custom underwater cameras, computer vision, and machine learning we are able to quantify fish weights, detect the health status, and generate optimal feeding plans in real time. Our product operates at three levels: on-site hardware for image capture, cloud pipelines for data processing, and a user-facing web application. As a result, there are hundreds of moving pieces and no shortage of fascinating challenges across all levels of the stack.
About The Edge Systems Team:
Edge engineering is responsible for the hardware and software orchestrating the hardware installed at fish farms around the world. Our goals are to create autonomous, reliable, bandwidth-light, long-lasting, robust, remote-debuggable, fail-safe, and easily deployable underwater cameras and sensors.
We work with world-class mechanical engineering firms and optical consultants to spec the underwater equipment we deploy. The edge engineering team writes software and procedures to make quality testing of these cameras as easy as possible for the field team in Norway. The types of tests we orchestrate are hardware burn-in, optical quality testing in-air and in-water, sensor calibration and verification, and stereo camera calibration.
The edge team also writes software to make it easy for the field team to successfully deploy and configure our hardware at the farm. As it's often rainy in Norway and the Internet may not yet be set up, our debugging tools need to operate wirelessly and allow a field technician to interface with the hardware from their phones.
The edge team is responsible for designing the network, cellular backup system, and mesh network of devices at a farm. We plan for failure, and build in redundancies where possible. Internet can go out for hours and there's only so much data we can uplink. Boats may park between our antennas.
As Aquabyte evolves, more products will be built on-top of the pixel and sensor data we collect. In order to scale, these algorithms need to live on the edge. We work closely with the machine learning team to help move their algorithms safely from the cloud to the edge.
We are responsible for our own Linux build process and the process of safely deploying software to the devices in the field.
This role is flexible and is based out of our Bay Area office and involves occasional travel to Norway and Chile.
Job Responsibilities
  • Interface with sensors; cameras; mesh, wireless, and cellular networks to create robust, reliable, and remote data collection and processing systems
  • Develop on ARM-based embedded platforms using C, C++, python, golang or rust
  • Improve our embedded Linux build and deployment process
  • Develop software to automate hardware testing procedures
  • Build diagnostic and configuration tooling to enable our field team to interface with our hardware wirelessly from their phones.
  • Enable our research team to try new machine learning models on real hardware
  • Participate in hardware specifications for our next generation equipment
  • Participate in on-call for diagnosing and fixing device issues remotely and implementing procedures and tooling to help enable the field team to self-diagnose and fix issues themselves

Qualifications
  • Engineering or CS degree.
  • Software development on an embedded device
  • Experience writing and building software.
  • Professional experience with C, C++, Golang, Python or Rust.

Desired but Not Required
  • Solid understanding of TCP/IP
  • Real-Time Operating Systems (RTOS)
  • Buildroot, Yocto Project, toolchains, uBoot, UART, SPI, I2C interfaces
  • Experience with WiFi, BLE, LoRaWAN, Mesh Networking, Cellular Networks
  • Selecting hardware targeted for harsh environmental conditions
  • Ability to read a schematic
  • Experience with cloud environments such as AWS.
  • Experience deploying to off-site hardware.
  • Professional experience working with cameras.
  • Build and maintain fleet operations tools for monitoring, notifications, trending, and analysis.
  • Experience at a small & quickly growing startup

Benefits
  • Competitive salary and equity
  • Unlimited vacation policy
  • Flexible working hours + hybrid work policy
  • Medical, vision, & dental insurance
  • Retirement matching plan
  • Potential travel to Norway
  • Evolve in a fast-paced environment
  • Be able to shape a business in its early days
  • Get ideas, feedback, and suggestions from other best-in-their-field colleagues
  • Mentorship opportunities, we'll be dedicated to investing in you and supporting you as you grow

$130,000 - $190,000 a year
Aquabyte takes a market-based approach to compensation. The pay varies on a variety of factors including: job-related qualification, years of experience and competence level, interview performance, and work location.
At Aquabyte, we admire interesting people with a unique background. We strongly encourage you to apply even if you don't satisfy all the requirements, and we will get back to you as soon as possible!
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.