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Embedded Machine Learning Jobs in Boston, MA (NOW HIRING)

Motional

Senior Software Engineer, Next Gen Compute

Boston, MA · Hybrid

$133.10K - $175.50K/yr

... machine learning, sensors, and hardware compute platforms to evolve Motional's on-board vehicle architecture. If you are a software engineer and love the idea of working on embedded AI hardware and ...

MORSE Corp

Data Scientist

Cambridge, MA · On-site

$90K - $210K/yr

You will be part of teams performing Test and Evaluation (T&E) of AI and machine learning models ... embedded systems. * Support the transition of the developed algorithms to software using one or ...

Boston Dynamics

Sr. Staff, ML Engineer R&D

Waltham, MA · On-site

$173.73K - $225K/yr

As a Sr. Staff ML Research Engineer on the Machine Learning Safety R&D Team, you will join a small ... You will help integrate your algorithms into embedded systems intended to make our robots safe and ...

MORSE Corp

Lead Data Scientist (TS/SCI)

Cambridge, MA · On-site

$90K - $210K/yr

You will lead teams and strategy for Algorithm Test and Evaluation (T&E) of AI and machine learning ... time embedded systems. Requirements: * US CITIZENSHIP REQUIRED and an Active Top Secret U.S.

STR

Principal Signal Processing Researcher

Woburn, MA · On-site

$206K - $284K/yr

... machine learning algorithms, systems analysis, and real-time embedded processor implementation. The Algorithms, Processing and Experimentation (APEX) Group specializes in development of RF/radar ...

Onto Innovation

Software Engineer 3

Wilmington, MA · On-site

$62.75 - $84.50/hr

... Apply machine learning, image processing, computer vision, mathematics, and optics to develop ... • Familiarity with embedded systems, hardware communication protocols, and/or real-time ...

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How much do embedded machine learning jobs pay per year?

As of May 30, 2026, the average yearly pay for embedded machine learning in Boston, MA is $166,636.00, according to ZipRecruiter salary data. Most workers in this role earn between $142,900.00 and $187,900.00 per year, depending on experience, location, and employer.

What is an Embedded Machine Learning job?

An Embedded Machine Learning job involves developing and optimizing machine learning models to run efficiently on resource-constrained devices like microcontrollers, edge devices, and IoT hardware. Professionals in this role work on model compression, low-power inference, and real-time processing, ensuring AI capabilities can function without relying on cloud computing. Responsibilities often include data preprocessing, feature extraction, model training, and deployment on embedded systems using frameworks like TensorFlow Lite or Edge Impulse.

What are the key skills and qualifications needed to thrive in the Embedded Machine Learning position, and why are they important?

To thrive in Embedded Machine Learning, you should have expertise in machine learning algorithms, embedded systems programming (e.g., C/C++, Python), and a solid understanding of hardware-software integration, typically backed by a degree in computer engineering, electrical engineering, or a related field. Familiarity with edge AI tools (such as TensorFlow Lite, ONNX, or Edge Impulse), microcontrollers, and real-time operating systems is highly valued, alongside relevant certifications such as Embedded Systems or AI certificates. Strong problem-solving skills, effective communication, and the ability to work cross-functionally are crucial soft skills in this field. These qualifications and qualities are vital for creating efficient, reliable AI solutions that operate seamlessly within resource-constrained environments and interdisciplinary project teams.

What are some common challenges faced by professionals working in embedded machine learning roles?

Professionals in embedded machine learning roles often face the challenge of optimizing machine learning models to run efficiently on resource-constrained hardware, such as microcontrollers or edge devices with limited memory and processing power. Balancing model accuracy, inference speed, and energy consumption can require creative problem-solving and deep knowledge of both hardware and software. Additionally, collaboration with hardware engineers, data scientists, and software developers is key, as projects typically require cross-functional teamwork to meet performance and deployment goals. Staying current with rapidly evolving tools and best practices is also important in this dynamic field.
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Embedded Machine Learning jobs near you
Senior Software Engineer, Next Gen Compute

Senior Software Engineer, Next Gen Compute

Motional

Boston, MA • Hybrid

$133.10K - $175.50K/yr

Other

Posted 26 days ago

Job description

Motional's global headquarters are located at 100 Northern Avenue in Boston, MA. Nestled in the bustling Seaport district with sweeping views of Boston Harbor and downtown Boston, the office is located close to major transit lines and a quick walk to various restaurants and popular attractions.

Mission Summary:

Motional's CORE team is responsible for our vehicle's Compute and Onboard Runtime Environment. We are creating a world leading AI compute platform for autonomous vehicles. This is the system that executes the software and neural networks that make our vehicles autonomous. 

The Next-Gen Technologies team is part of CORE. We work at the intersection of software engineering, machine learning, sensors, and hardware compute platforms to evolve Motional's on-board vehicle architecture. If you are a software engineer and love the idea of working on embedded AI hardware and software systems to create the next generation of autonomous vehicles, we would love to talk with you. 

What You'll Be Doing:

As a senior engineer in the Next-Gen Technologies team, you will help us improve the compute performance of our current generation and next-generation autonomous driving systems through participating in full lifecycle development from idea to proofs-of-concept to production. 

Specifically, you will:

  • Focus deeply on ML model deployment, integration of multiple ML models, and ML model optimization on embedded compute platforms.
  • Dive deep into the full ML software stack. Analyze ML workload performance on a variety of hardware processors, optimize ML models, improve ML software, and help us continually improve our stack through the application of efficient and effective ML approaches. 
  • Design, develop, test, integrate, and optimize software and tools on a variety of ML compute architectures.
  • Collaborate with deep learning experts in perception, prediction, and other autonomous driving application areas to enable algorithms on GPU, NPU, and other ML accelerator architectures.
  • Optimize the utilization of GPU/NPU resources and sharing of GPU/NPU access across multiple programs running on the same system.
  • Lead designs to determine the needs of the system and how to best meet those needs through continually improving our ML software stack.
  • Advise peers and management on technical matters.

What We're Looking For:

  • Experience with machine learning accelerators, including GPUs, NPUs, TPUs, and their programming environments, including CUDA, TensorRT, or similar technologies.
  • Strong experience with modern C++ development in a Linux environment.
  • Experience with parallel and high-performance computing.
  • Comfortable with experimentation and evaluating different options as we work towards finding solutions that work. 
  • A degree in Software Engineering, Computer Science, Electrical or Electronic Engineering, or similar technical field of study, or you have equivalent knowledge gained through your practical experience.

Preferred, but not required:

  • Experience with PyTorch, TensorFlow, ONNX, and/or other ML frameworks. 
  • Experience with embedded systems development for ARM-based system-on-chip architectures.
  • Experience working in a MLOps or DevOps environment.
  • Passion for self-driving technology and its potential for positive impact on the world.

This role is hybrid from our Boston office. It requires two in-office days each week, ideally Tuesday and Thursday.

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