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Hourly Embedded Machine Learning Jobs in Boston, MA

Xforia, Inc.

Principal AI/ML Engineer

Boston, MA · On-site

... on embedded systems. • Working closely with hardware engineers to optimize machine learning models for specific hardware architectures and assisting in system integration. • Conducting ...

National Geographic

Senior Data Science Engineer

Boston, MA · On-site

$120.80 - $151/hr

You'll work closely with machine learning engineers to develop new product capabilities and uncover ... solutions embedded within customer-facing systems. * Mentor junior data scientists and share ...

NxT Level

Lead Engineer, MLOps

Boston, MA · On-site

$111K - $146K/yr

... and embedded data science engineering. The team partners closely with data scientists to ensure forecasting, network orchestration, pricing, routing, and other machine learning systems are well ...

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All JobsHourly Embedded Machine Learning JobsHourly Embedded Machine Learning Jobs in Boston, MA

Hourly Embedded Machine Learning information

See Boston, MA salary details

$76K

$166.6K

$189K

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

As of Aug 16, 2026, the average yearly pay for hourly 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 are the key skills and qualifications needed to thrive as an hourly embedded machine learning engineer, and why are they important?

To thrive as an Hourly Embedded Machine Learning Engineer, you need a solid background in embedded systems, machine learning algorithms, and programming languages like C/C++ and Python, often supported by a degree in computer engineering or a related field. Familiarity with tools such as TensorFlow Lite, embedded Linux, microcontroller development environments, and model optimization frameworks is typically required. Strong problem-solving skills, adaptability, and effective communication help you address complex technical challenges and collaborate with cross-functional teams. These skills are crucial for designing efficient, real-time ML solutions that operate reliably on resource-constrained embedded devices.

How does an hourly embedded machine learning professional typically collaborate with hardware and software teams during a project?

As an Hourly Embedded Machine Learning professional, you will often work closely with both hardware and software engineering teams to ensure that machine learning models are efficiently integrated into embedded systems. This typically involves frequent communication to align on hardware constraints, such as memory and processing power, and to optimize algorithms for real-time performance. You may also participate in joint debugging sessions and code reviews to address integration issues and streamline deployment. Collaboration is key, as successful projects depend on the seamless interaction between machine learning solutions and the embedded hardware platform.

What is an hourly embedded machine learning engineer?

An Hourly Embedded Machine Learning engineer is a professional who specializes in developing and deploying machine learning models on embedded systems, such as microcontrollers, IoT devices, or edge devices, and is compensated on an hourly basis rather than a salaried or project-based arrangement. These engineers work to optimize algorithms so they can run efficiently on devices with limited computing power, memory, and energy resources. Their responsibilities often include model selection, quantization, optimization, and integration of machine learning pipelines into hardware. Hiring on an hourly basis allows for flexibility in project scope and duration, making it ideal for companies with specific, time-limited needs. They often collaborate with hardware engineers, data scientists, and software developers to create intelligent embedded solutions.

What is the difference between Hourly Embedded Machine Learning vs Hourly Data Scientist?

AspectHourly Embedded Machine LearningHourly Data Scientist
CredentialsKnowledge of embedded systems, programming, ML algorithmsDegree in Data Science, Statistics, or related field
Work EnvironmentEmbedded hardware, IoT devices, real-time systemsData analysis, modeling, visualization in office or cloud
Industry UsageConsumer electronics, automotive, IoT devicesFinance, healthcare, marketing, research

Hourly Embedded Machine Learning specialists focus on integrating ML models into embedded systems and hardware, often working with IoT devices and real-time constraints. In contrast, Hourly Data Scientists analyze large datasets to develop predictive models primarily in cloud or office environments. While both roles require programming skills, embedded ML emphasizes hardware integration, whereas data science centers on data analysis and visualization.

What are the most commonly searched types of Embedded Machine Learning jobs in Boston, MA?

The most popular types of Embedded Machine Learning jobs in Boston, MA are:

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For Hourly Embedded Machine Learning jobs in Boston, MA, the most frequently searched job titles are:

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What cities near Boston, MA are hiring for Hourly Embedded Machine Learning jobs?

Cities near Boston, MA with the most Hourly Embedded Machine Learning job openings:

Hourly Embedded Machine Learning jobs near you

Software Engineer, Embedded Systems

MatrixSpace

Burlington, MA • Remote

$150K - $185K/yr

Full-time

Re-posted 24 days ago

Job description

Help bring AI and machine learning capabilities to embedded edge platforms by building high-performance software that runs close to the hardware.

MatrixSpace develops AI-enabled radar and sensing systems that help people understand what's happening in the world around them. By combining advanced radar, edge computing, and AI, we deliver situational awareness in environments where traditional sensing solutions struggle.

We're looking for a hands-on Embedded Software Engineer to build high-performance software that runs close to the hardware. You'll develop production embedded applications in C/C++, optimize software for resource-constrained edge platforms, and work across Linux, networking, and system-level software.

If you're the kind of engineer who can read complex C/C++ code like a book, enjoys understanding entire systems rather than isolated components, and loves solving practical engineering problems, we'd love to talk.

What You'll Do

  • Port, optimize, and enhance platform software for embedded and resource-constrained compute environments.
  • Deploy, validate, profile, and optimize AI/ML-enabled applications on edge hardware.
  • Develop production-quality software using C/C++, Python, Golang, and Linux-based technologies.
  • Collaborate with Data Science teams to integrate AI/ML models into production software pipelines.
  • Work across Linux kernel, device interfaces, networking, and system-level software components.
  • Participate in architecture reviews, code reviews, testing, troubleshooting, and technical planning.

What We're Looking For

This position requires working directly or indirectly with the US Government in restricted environments. Candidates must be legally authorized to work in the United States without employer sponsorship and may be required to obtain and maintain a U.S. government security clearance in the future.

THIS IS NOT A FULLY REMOTE POSITION.

Required

  • Bachelor’s degree in Computer Science, Electrical Engineering, Computer Engineering, Robotics, or a related technical field, or equivalent practical experience.
  • 4+ years of professional software engineering experience or equivalent demonstrated expertise
  • Professional experience building, deploying, and maintaining production embedded software systems on edge devices with constrained CPU, GPU, memory, storage, and power resources.
  • Expert-level proficiency in C/C++ with the ability to quickly understand, debug, and extend large existing codebases. This role is not a fit for candidates without deep C/C++ experience. Working knowledge of Golang and Python3.8+ preferred.
  • Strong experience with Yocto-based embedded Linux distributions, including image customization, package management, board support packages, kernel configuration and tuning, and production deployment workflows.
  • Strong debugging, profiling, and performance optimization skills on constrained compute platforms.
  • Ability to collaborate effectively across software, firmware, DevOps, data science, and hardware teams.

Someone Who Will Thrive in This Role

  • Enjoys understanding complete systems—not just individual components.
  • Takes ownership of complex technical problems and follows them through to production.
  • Is comfortable diving into large existing codebases and becoming productive quickly.
  • Values practical, reliable engineering over unnecessary complexity.
  • Collaborates effectively across software, firmware, hardware, and AI teams.
  • Has experience at smaller or fast-growing companies where engineers own broad portions of the product rather than a single isolated component.
  • Has experience developing connected devices, IoT platforms, fleet management systems, robotics, or other distributed edge computing products.

Bonus Points

  • Experience deploying AI/ML models usingTensorRT, ONNX Runtime,PyTorch, TensorFlow Lite, or similar frameworks.
  • Experience with NVIDIA Jetson, CUDA, GPUs, NPUs, or other edge accelerators.
  • Background in radar, RF sensing, robotics, autonomy, perception systems, signal processing, or sensor fusion.
  • Experience with hardware-in-the-loop testing, board bring-up, and embedded platform validation.

At MatrixSpace, software engineering is where advanced sensing technology meets real-world deployment. You'll help bring AI-powered capabilities to edge platforms so our customers can gain actionable insights from complex environments. If that sounds exciting, we'd love to hear from you.