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Embedded Machine Learning Engineer Jobs in Massachusetts

Chewy

Staff Machine Learning Engineer

Boston, MA

$186.50K - $297.50K/yr

At Chewy, our Sponsored Ads Technology team based out of Bellevue, WA is looking for a Staff Machine Learning Engineer to help launch various innovative ads-offerings for Chewy onsite and offsite ...

Chewy

Senior Machine Learning Engineer

Boston, MA · On-site +1

$149K - $245K/yr

At Chewy, our Sponsored Ads Technology team based out of Bellevue, WA is looking for a Senior Machine Learning Engineer to help launch various innovative ads-offerings for Chewy onsite and offsite ...

OneStudyTeam

Senior Machine Learning Engineer

Boston, MA · On-site +1

$140K - $190K/yr

By joining our team as a Senior Machine Learning Engineer , you will play a pivotal role in building cutting-edge AI products that directly impact how new therapies reach patients. We're looking for ...

Redhat

Principal Machine Learning Engineer

Boston, MA · On-site +1

$189.60K - $312.73K/yr

As a Machine Learning Engineer focused on model optimization algorithms, you will work closely with our product and research teams to develop SOTA deep learning software. You will collaborate with ...

Manulife

Associate Machine Learning Engineer

Boston, MA · On-site

We are seeking a skilled Machine Learning Engineer with a strong focus on building data pipelines, AI solution development, deployment, and monitoring. The ideal candidate will have experience in ...

New

GN Group

Machine Learning Engineer - Cloud

Lowell, MA · On-site +1

$86K - $135K/yr

Machine Learning Engineer - Cloud *Please consider before applying: This is a hybrid role, and candidates must reside within a commutable distance of one of our offices in either Dover, NH, or Lowell ...

CVS Health

Senior Machine Learning Engineer

Wellesley, MA · On-site

$111.24K - $222.48K/yr

We're looking for a Senior Machine Learning Engineer to help build and scale the next generation of data science and AI products in the journey. In this role, you'll leverage your engineering ...

Redhat

Senior Machine Learning Engineer

Boston, MA · On-site +1

$174.19K - $287.41K/yr

As a Senior Principal Machine Learning Engineer focused on model optimization algorithms, you will work closely with our product and research teams to develop SOTA deep learning software. You will ...

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Embedded Machine Learning Engineer information

See Massachusetts salary details

$76.4K

$167.5K

$190K

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

As of May 29, 2026, the average yearly pay for embedded machine learning engineer in Massachusetts is $167,514.00, according to ZipRecruiter salary data. Most workers in this role earn between $143,600.00 and $188,900.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as an Embedded Machine Learning Engineer, and why are they important?

To thrive as an Embedded Machine Learning Engineer, you need expertise in machine learning algorithms, embedded systems programming (C/C++ or Python), and a solid understanding of hardware constraints, usually supported by a degree in computer science, electrical engineering, or related fields. Familiarity with tools like TensorFlow Lite, ONNX, microcontroller SDKs, and experience with real-time operating systems (RTOS) are typically required. Strong problem-solving, communication skills, and the ability to collaborate across multidisciplinary teams help you stand out in this role. These skills are crucial for efficiently deploying intelligent models on resource-constrained devices, ensuring optimal performance and seamless integration in real-world applications.

What are some common challenges faced by Embedded Machine Learning Engineers when deploying models to hardware devices?

One of the main challenges for Embedded Machine Learning Engineers is optimizing machine learning models to run efficiently on devices with limited memory, processing power, and energy capacity. Ensuring real-time performance while maintaining accuracy often requires model quantization, pruning, or using lightweight architectures. Additionally, engineers must carefully manage hardware-software integration and address issues like compatibility with various microcontrollers and ensuring secure, reliable updates for deployed models. Close collaboration with hardware engineers and software developers is essential to overcome these challenges and deliver robust embedded AI solutions.

What does an Embedded Machine Learning Engineer do?

An Embedded Machine Learning Engineer designs and implements machine learning models that can run efficiently on embedded systems, such as microcontrollers and edge devices. Their work involves optimizing algorithms to fit within the resource constraints of these devices, integrating ML models into hardware, and ensuring real-time performance. They collaborate closely with hardware engineers and software developers to deploy intelligent features in products like smart sensors, IoT devices, and autonomous systems.

What is the difference between Embedded Machine Learning Engineer vs Firmware Engineer?

AspectEmbedded Machine Learning EngineerFirmware Engineer
Required CredentialsBachelor's/Master's in Computer Science, Electrical Engineering, or related; knowledge of ML frameworksBachelor's in Electrical Engineering, Computer Engineering, or related; embedded systems experience
Work EnvironmentDevelops ML models for embedded devices, often in IoT or smart devicesDesigns and implements low-level firmware for hardware devices
Industry UsageTech companies, IoT, consumer electronics, automotiveConsumer electronics, automotive, industrial equipment

The Embedded Machine Learning Engineer focuses on integrating machine learning models into embedded systems, while the Firmware Engineer specializes in developing low-level software for hardware devices. Both roles require embedded systems knowledge but differ in their core focus and skill sets.

What are popular job titles related to Embedded Machine Learning Engineer jobs in Massachusetts? For Embedded Machine Learning Engineer jobs in Massachusetts, the most frequently searched job titles are:
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What cities in Massachusetts are hiring for Embedded Machine Learning Engineer jobs? Cities in Massachusetts with the most Embedded Machine Learning Engineer job openings:
Embedded Machine Learning Engineer jobs near you

Machine Learning Engineer II / Senior Machine Learning Engineer I, Physical Sciences

Lila Sciences

Cambridge, MA • On-site

$114.20K - $156.80K/yr

Other

Posted 24 days ago

Job description

Your Impact at LILA

This Machine Learning Engineer for the Physical Sciences team focuses on building and operating end-to-end, scalable machine learning workflows that solve a diversity scientific use cases in materials, chemistry and physical sciences. Your work will advance research efforts on state-of-the-art algorithms to build towards scientific superintelligence across today's greatest challenges in physical sciences.

What You'll Be Building

  • Design, implement, and maintain endtoend ML pipelines (data ingestion, feature engineering, training, evaluation, deployment, monitoring).
  • Productionize models and services with robust testing, observability, and documentation in collaboration with cross-functional software teams and build CI/CD workflows and automated evaluations to ensure safe, frequent releases.
  • Collaborate with domain scientists and platform engineers to translate research insights into performant, scalable systems.
  • Contribute to technical design reviews, coding standards, and mentoring of best practices.

What You'll Need to Succeed

  • BS/MS/PhD in Computer Science, Engineering, or a related quantitative field, or equivalent industry experience.
  • Strong Python software engineering fundamentals (testing, packaging, typing); experience with machine learning frameworks (e.g., PyTorch, Huggingface, etc.).
  • Experience deploying ML services to production in cloud-based infrastructure (FastAPI/GRPC, containers, orchestration, cloud infra).
  • Handson experience with model deployment in production systems (LLMs, multimodal models, databases, RAG) with strong debugging and profiling skills.
  • Clear communication and collaboration in crossfunctional settings.

Bonus Points For

  • Exposure to scientific or engineering domains (materials, chemistry, physics) and related data formats/benchmarks.
  • GPU optimization experience (CUDA, Triton, compilation, distributed training).
  • Prior contributions to opensource ML or scientific software.
  • Experience with workflow orchestration, data provenance, or largescale compute environments.

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