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Hourly Embedded Machine Learning Jobs in Boston, MA

MORSE Corp

Data Scientist- TS/SCI

Cambridge, MA · On-site

Responsibilities : • Perform data analysis, test and evaluation of existing machine learning ... real-time embedded systems. • Support the transition of the developed algorithms to software ...

MORSE Corp

Data Scientist- TS/SCI

Cambridge, MA · On-site

Responsibilities : • Perform data analysis, test and evaluation of existing machine learning ... real-time embedded systems. • Support the transition of the developed algorithms to software ...

MORSE Corp

Data Scientist

Cambridge, MA · On-site

Responsibilities : • Perform data analysis, test and evaluation of existing machine learning ... real-time embedded systems. • Support the transition of the developed algorithms to software ...

MORSE Corp

Data Scientist

Cambridge, MA · On-site

Responsibilities : • Perform data analysis, test and evaluation of existing machine learning ... real-time embedded systems. • Support the transition of the developed algorithms to software ...

Motional

Senior Software Engineer, Next Gen Compute

Boston, MA · Hybrid

$133K - $175K/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 ...

Boston Dynamics

Sr. Staff, ML Engineer R&D

Waltham, MA · On-site

$173K - $225K/yr

Sr. Staff ML Research Engineer As a Sr. Staff ML Research Engineer on the Machine Learning Safety ... You will help integrate your algorithms into embedded systems intended to make our robots safe 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

$173K - $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 ...

Bostondynamics

Sr. Staff, ML Engineer R&D

Waltham, MA · On-site

$173K - $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 ...

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Hourly Embedded Machine Learning information

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$76K

$166.6K

$189K

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

As of Jun 11, 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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Hourly Embedded Machine Learning jobs near you
Associate Director, Core Algorithms (Sensor Intelligence Group)

Associate Director, Core Algorithms (Sensor Intelligence Group)

Whoop

Boston, MA • On-site

Full-time

Posted 24 days ago

Job description

At WHOOP, we’re on a mission to unlock human performance and healthspan. WHOOP empowers members to perform at a higher level and live longer through a deeper understanding of their bodies and daily lives.

We are seeking an Associate Director of Engineering, Core Algorithms to define and lead WHOOP’s strategy for transforming physiological sensor data into real-time, production-grade intelligence. This role owns the technical vision and execution for core algorithm systems that power member-facing experiences across generations of WHOOP devices.

You will operate at the intersection of machine learning, signal processing, and embedded systems, defining multi-year technical direction, scaling high-performing teams, and shaping how WHOOP delivers differentiated edge intelligence at scale. You will lead teams driving innovation in on-device intelligence, translating complex physiological data into scalable, high-quality insights while navigating tradeoffs across accuracy, latency, and efficiency. Your leadership will directly influence how WHOOP delivers meaningful, member-centric experiences across product generations.

RESPONSIBILITIES:

  • Lead and scale high-performing teams of machine learning and signal processing engineers, including senior ICs and tech leads

  • Define and drive the roadmap for core algorithm systems and on-device intelligence

  • Guide key architectural and technical tradeoffs across accuracy, latency, power, and system constraints

  • Deliver end-to-end sensor-driven features from research through production deployment

  • Drive cross-functional execution across Firmware, WHOOP Labs, and Product; align stakeholders and manage complex dependencies
    Establish best practices for algorithm development, validation, integration, and lifecycle management

  • Balance long-term innovation with near-term product delivery

  • Develop technical leaders and foster a culture of execution excellence

QUALIFICATIONS:

  • Proven experience leading engineering teams in machine learning, signal processing, or embedded systems, including managing senior ICs or tech leads

  • Strong track record of delivering sensor-driven algorithms or systems into production environments

  • Experience working across hardware, firmware, and data science boundaries, with a systems-level mindset

  • Demonstrated ability to define and communicate technical strategy and guide teams through ambiguity

  • Experience driving cross-functional programs, aligning stakeholders, and influencing without direct authority

  • Strong communication skills, with the ability to translate complex technical concepts to diverse audiences

  • Passion for building teams, developing talent, and scaling engineering organizations

PREFERRED QUALIFICATIONS:

  • Experience with physiological sensing (e.g., PPG, IMU, ECG) or wearable technologies

  • Experience optimizing models for edge deployment (e.g., quantization, latency, power constraints)

  • Background in health, fitness, or consumer hardware products

  • Experience balancing long-term innovation vs. near-term product delivery

Whoop logo

About Whoop

Sourced by ZipRecruiter

At WHOOP, we're on a mission to unlock human performance. WHOOP empowers users (Olympians, Professional Athletes, Fitness Enthusiasts, etc) to perform at a higher level through a deeper understanding of their bodies and daily lives.

Industry

Fitness and sports centers

Company size

501 - 1,000 Employees

Headquarters location

Boston, MA, US

Year founded

2012

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