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Hourly Embedded Machine Learning Jobs (NOW HIRING)

Machine Learning Engineer

Fremont, CA ยท On-site

$150K - $220K/yr

We are seeking a Machine Learning Engineer to join our team developing machine learning solutions ... Work with print software and embedded teams to integrate validated models into production code ...

Machine Learning Engineer

Fremont, CA ยท On-site

$150K - $220K/yr

We are seeking a Machine Learning Engineer to join our team developing machine learning solutions ... Work with print software and embedded teams to integrate validated models into production code ...

Machine Learning Engineer

Burlington, MA ยท Remote

$165K - $200K/yr

Experience with embedded systems, GPUs, NPUs, FPGAs, or hardware acceleration. * Familiarity withMLOps, CI/CD, model monitoring, and large-scale production systems. At MatrixSpace, Machine Learning ...

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Machine Learning Engineer

Los Angeles, CA ยท On-site

$150K - $180K/yr

Stay current with the latest machine learning research for wireless and embedded systems, applying ingenuity and a deep understanding of the problems at hand Required Skills * 4+ years experience as ...

About the Team You'll lead the Machine Learning and FPT teams, working closely with the Director of ... Edge ML deployment experience (ONNX, TensorRT, mobile/embedded inference) * Familiarity with ...

The Machine Learning Engineer will leverage their strong technical background and knowledge to ... There are differentiating factors that can impact a final salary/hourly rate, including, but not ...

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Hourly Embedded Machine Learning information

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$70K

$153.4K

$174K

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

As of Jul 11, 2026, the average yearly pay for hourly embedded machine learning in the United States is $153,383.00, according to ZipRecruiter salary data. Most workers in this role earn between $131,500.00 and $173,000.00 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.

More about Hourly Embedded Machine Learning jobs
What cities are hiring for Hourly Embedded Machine Learning jobs? Cities with the most Hourly Embedded Machine Learning job openings:
What are the most commonly searched types of Embedded Machine Learning jobs? The most popular types of Embedded Machine Learning jobs are:
What states have the most Hourly Embedded Machine Learning jobs? States with the most job openings for Hourly Embedded Machine Learning jobs include:
Infographic showing various Hourly Embedded Machine Learning job openings in the United States as of July 2026, with employment types broken down into 1% Internship, 92% Full Time, 5% Part Time, and 2% Contract. Highlights an 85% Physical, 4% Hybrid, and 11% Remote job distribution, with an average salary of $153,383 per year, or $73.7 per hour.
Mid- level Machine Learning Engineer

Mid- level Machine Learning Engineer

InstantServe LLC

San Jose, CA โ€ข On-site

Full-time

Posted 17 days ago


Job description

Job Title: Senior Machine Learning Engineer
Client: TetraMem Inc
Location: San Jose, California
Senior Machine Learning Engineer
About the job
Responsibilities
  • Develop, optimize, and deploy lightweight machine learning models for edge AI applications, particularly for audio processing.
  • Implement and optimize ML models on embedded platforms, including FPGA and custom ASIC solutions.
  • Work closely with hardware and software teams to integrate ML models into production systems.
  • Research and implement state-of-the-art ML techniques to enhance model efficiency, latency, and power consumption for embedded AI applications.
  • Improve inference efficiency and model compression techniques, including quantization, pruning, and knowledge distillation.
  • Collaborate with cross-functional teams to drive innovation and contribute to the overall system architecture.
  • Provide technical leadership and mentorship to junior engineers.
  • Publish research findings, present at conferences, and contribute to open-source projects when applicable.
Requirements
  • 5+ years of relevant industry experience (or a PhD) in Computer Science, Electrical Engineering, Machine Learning, or related fields.
  • Must have prior experience managing a team, serving in a Team Lead role, or demonstrating strong technical leadership and cross-functional coordination capabilities.
  • Strong hands-on experience in machine learning, with a focus on edge AI, on-device inference, and deploying lightweight models on resource-constrained devices.
  • Expertise in modern ML frameworks such as PyTorch, TensorFlow (including TensorFlow Lite), and JAX.
  • Proficiency in Python and C/C++, with practical experience in ML model optimization and production deployment.
  • Deep experience with model quantization (PTQ/QAT), pruning, knowledge distillation, sparsity, and other compression techniques for efficient edge inference.
  • Hands-on experience developing for or integrating with AI chip SDKs, neural accelerators (NPUs/DSPs), or hardware-specific toolchains (e.g., NVIDIA TensorRT, Qualcomm Neural Processing SDK, ARM Ethos, or similar).
  • Familiarity with edge inference runtimes (ONNX Runtime, ExecuTorch, TVM) and optimizing models for hardware constraints (latency, memory footprint, power consumption).
Experience in one or more of the following areas considered a strong plus:
  • Understanding of ML compiler and runtime design.
  • Experience working with tools such as Optimum, ONNX, TensorRT, TFLite/LiteRT, ncnn, or CoreML.
  • Familiarity with hardware acceleration techniques.
  • Experience in embedded system development.

InstantServe logo

About InstantServe

Sourced by ZipRecruiter

InstantServe provides a one-stop solution to all Healthcare, IT/Non-IT Staffing needs. Established in 2016, InstantServe is a strong workforce of over 100+ go-getters with a demonstrated background in IT/Non-IT service. We are a nationally certified SBE from the Department of Administration (State of PA). As a proud Minority Woman Owned Small Business Enterprise (M/WBE), InstantServe boasts of a strong team of professionals who have extensive experience catering to several Federal, Public, Commercial, and Healthcare Clients which includes 26 States and 46 government agencies. InstantServe is a client-centric organization that offers cost-effective and reliable solutions. Client satisfaction is sacrosanct! Our team strives to provide the best staffing and IT solutions to take your business to the next level.

Industry

Recruiting and staffing services

Company size

11 - 50 Employees

Headquarters location

Wayne, PA, US

Year founded

2016

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