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Hourly Embedded Machine Learning Jobs (NOW HIRING)

Apple

Machine Learning Engineer

Cupertino, CA

$150K - $225K/yr

... shared AI platform and embedded across products - Design, build, and own end-to-end GenAI ... machine learning concepts, including supervised and unsupervised learning; exposure to ...

PROPRIUS

Senior Machine Learning Engineer

New York, NY

$114K - $157K/yr

Expert level coding skills (Python, C++ at minimum) * 3+ years' experience working with machine learning in embedded applications: model quantization, fixed point neural networks (CNN and RNN)

Apple

Embedded ISP Engineer

San Diego, CA · On-site

... and machine learning techniques, all while contributing to the future of photography and ... Build drivers for advanced image processing pipelines in embedded systems, working with the latest ...

Gather AI

Principal Machine Learning Scientist

Explore next-generation perception capabilities, including embedded and on-prem inference optimization for new deployment targets What You'll Need * 10+ years of experience in machine learning or ...

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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:
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Hourly Embedded Machine Learning jobs near you
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.
Software Engineer - Machine Learning

Software Engineer - Machine Learning

Quantiply Corporation

San Jose, CA

Full-time

Posted 23 days ago

Job description

Company Description

Did you know that Money Laundering is a primary enabler of criminal activity like drug trafficking, smuggling, terrorism, and corruption around the world with an estimated $2.3 Trillion laundered annually? Criminals are becoming more and more sophisticated in rapidly innovating new ways to launder money and current methods of detecting money laundering are antiquated and ineffective. Quantiply's Sensemaker application suite and platform solutions use AI and machine learning algorithms to identify money laundering and other criminal activities and automatically recommend mitigation strategies and actions. If you are looking for an opportunity to work on complex business problems using the latest AI and Machine Learning technologies, work with best and brightest in crafting innovative solutions while making a positive impact on society, Quantiply is the place for you.

Job Description

We're looking for software engineers with experience in machine learning and artificial intelligence. You will be embedded as part of a team that collaborates with researchers on conceiving, researching, and prototyping new machine learning techniques and use cases with the goal of driving Quantiply's growth in the Anti-Money Laundering space.

Ideal candidates will have a good understanding of state-of-the-art techniques in machine learning and deep learning, performance optimization, and benchmarking, along with a strong understanding of high-performance computer architecture. Candidates must also possess strong verbal and written communication skills and the demonstrated ability to work in a demanding team-oriented environment.

Responsibilities:

  • Develop highly scalable deep learning, reinforcement learning and bayesian models.
  • Support research projects by providing innovative designs for end-to-end Machine Learning systems.
  • Optimize performance of complex machine learning systems. Exploit modern parallel environments.
  • Design and develop software libraries.
  • Partner with Product and Engineering teams to explore new opportunities.
  • Influence product features and product roadmap through exploratory analysis.
  • Report and present software developments verbally and in writing.
Qualifications

Minimum qualifications:

  • PhD in computer science, machine learning, electrical engineering, mathematics, or equivalent pracitical experience.
  • Strong knowledge and experience in Python, Docker and Kubernetes.
  • Working experience in Tensorflow.
  • Working experience with distributed software architecture.
  • Knowledge of machine learning and statistics.

Preferred qualifications:

  • Strong experience in Tensorflow or similar frameworks.
  • Strong experience with concurrent and distributed software architecture.
  • Experience with Hadoop.
  • Experience with C++/Java/Scala.
Additional Information

All your information will be kept confidential according to EEO guidelines.

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