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Hourly Embedded Machine Learning Jobs in Boston, MA

Axon

Senior Machine Learning Scientist

Boston, MA · On-site

$99K - $135K/yr

Your Impact We are seeking highly skilled and innovative Machine Learning Scientists to join our AI ... IoT devices, or embedded systems is highly desirable. * Excellent problem-solving skills ...

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 ...

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$76K

$166.6K

$189K

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

As of Sep 5, 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 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.

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 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.

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

Hourly Embedded Machine Learning jobs near you

Senior Machine Learning Engineer ml/python/Wilmington ma

Motion Recruitment

Boston, MA • On-site

$113K - $156K/yr

Full-time

Medical, Dental, Vision, Retirement, PTO

Re-posted 23 days ago

Job description

Job Description
A full-service product development consultancy specializing in medical devices, robotic systems and automation is hiring a Senior Machine Learning Engineer. As a Machine Learning Engineer you will be expected to own the full ML lifecycle, from raw sensor data to a model running on constrained hardware.
In this role you will design and utilize data pipelines for sensor data. Your responsibilities include training, optimizing, and deploying machine learning models for signal processing and anomaly detection on edge devices, collaborating with embedded engineers to integrate and validate inference within device software. Additionally, you will build MLOps infrastructure, participate in sensor selection and validation, and document model development to support both regulatory submissions and internal quality processes.
Required Skills & Experience
  • Strong proficiency in Python
  • Hands on experience in PyTorch or TensorFlow
  • Experience deploying models to edge using TFLite, ONNX, CoreML, TensorRT, or equivalent
  • Experience building sensor data pipelines
  • Proficiency with MLOps
  • Solid Software engineering fundamentals
  • Proficiency in C or C++
Desired Skills & Experience
  • 5 years of machine learning engineering or applied ML
  • Experience with physiological signal processing for medical or wearable applications
  • Background in robotics, or autonomous systems
  • Experience in a startup or small team
  • Degree in a relevant field
What You Will Be Doing
Daily Responsibilities
  • 100% Hands On
  • Develop and troubleshoot workflows for collecting, cleaning, and organizing sensor data.
  • Build and refine ML models for real-time device applications and performance improvements
  • Work closely with firmware teams to embed and test AI features on hardware platforms
  • Set up and oversee tools for tracking experiments, automating evaluations and managing deployments
  • Analyze model behavior, ensure reliability and resolve issues to maintain highquality outputs
The Offer
  • Bonus OR Commission eligible
You will receive the following benefits
  • Medical Insurance
  • Dental Benefits
  • Vision Benefits
  • Paid Time Off (PTO)
  • 401(k) {including match - if applicable}

Applicants must be currently authorized to work in the US on a full-time basis now and in the future.

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