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Hourly Embedded Machine Learning Jobs (NOW HIRING)

Velo3D

Machine Learning Engineer

Fremont, CA · 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 ...

Qualcomm

Machine Learning Compiler

New York, NY · On-site

$140K - $211K/yr

As a Qualcomm Machine Learning Engineer, you will create and implement machine learning techniques ... of experience in embedded system development and optimization with application to a specific ...

Velo3D

Machine Learning Engineer

Fremont, CA

$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 ...

Velo3D

Machine Learning Engineer

Fremont, CA · 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 ...

Nutanix

Machine Learning Compiler

Manhattan, NY · On-site

$141 - $211/hr

... of experience in embedded system development and optimization with application to a specific ... Applies Machine Learning knowledge to assist in extending training or runtime frameworks or model ...

New

MatrixSpace

Machine Learning Engineer

Burlington, MA · 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 ...

PassFort

Machine Learning Engineer

Berlin, MD · On-site

$79.93 - $137.02/hr

Practical experience deploying deep learning models in real time on embedded hardware (TensorRT, ONNX, Jetson/Orin). * Solid understanding of machine learning, deep learning, and autonomous systems.

sensmore

Machine Learning Engineer

Berlin, NH · On-site

$102.41 - $159.31/hr

Practical experience deploying deep learning models in real time on embedded hardware (TensorRT, ONNX, Jetson/Orin). * Solid understanding of machine learning, deep learning, and autonomous systems.

Mixpeek

Machine Learning Engineer

Washington, DC · On-site

$180 - $240/hr

The Machine Forward Deployed Learning Engineer position requires a mix of software development, LLM ... Experience in a customer-facing or embedded delivery role. * Exposure to federated or privacy ...

SimpliSafe

Staff Embedded ML Engineer, Edge AI

Boston, MA

$142K - $187K/yr

About the Role We are seeking a highly motivated and experienced Embedded Machine Learning Engineer to join our growing Edge AI team. As a key contributor, you will lead the on-device inference and ...

Gather AI

Director of Machine Learning

Pittsburgh, PA · On-site

They are seeking a Director of Machine Learning to define the ML strategy, lead the computer vision ... embedded inference) • Familiarity with warehouse, logistics, or supply chain domain • ...

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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 Aug 21, 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 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.

More about Hourly Embedded Machine Learning jobs

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What states have the most Hourly Embedded Machine Learning jobs?

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The top searched job categories for Hourly Embedded Machine Learning jobs are:

Hourly Embedded Machine Learning jobs near you
Infographic showing various Hourly Embedded Machine Learning job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 75% Full Time, 23% Part Time, and 1% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $153,383 per year, or $73.7 per hour.

Machine Learning Engineer

Escalon Services, LLC.

Santa Monica, CA

$100K - $120K/yr

Full-time

Medical, PTO

Posted 15 days ago

Job description


About Our Client

Our client is a technology company developing next-generation intelligent systems at the intersection of AI, XR, robotics, autonomy, and spatial computing. Their products support mission-critical applications across defense, public safety, and critical infrastructure. They are seeking passionate professionals who thrive in fast-paced environments and enjoy building impactful products from concept to deployment.

The Role

Our client is seeking a Machine Learning Engineer to help design and implement intelligent systems that extract meaning and predictive value from computer vision and behavioral datasets. This is a junior-level, in-person role suited for candidates with 2-3 years of experience and a solid foundation in deep learning, embeddings, and modern neural architectures.

As a member of the AI team, the ideal candidate will work on projects that leverage CNNs, transformer models, and embedding architectures to encode and reason over pose, facial, and action-based visual data. These systems support downstream tasks such as future action prediction, semantic matching, and similarity-based inference.
  • Design and implement machine learning pipelines that encode visual input (pose, face, object/classification) into shared embedding spaces for similarity and predictive tasks.
  • Build and fine-tune convolutional and transformer-based neural architectures optimized for visual recognition and representation learning.
  • Develop encoding and embedding techniques that allow consistent comparison across multiple data types (e.g., pose vectors, facial landmarks, class labels).
  • Apply techniques such as cosine similarity, distance metrics, and latent clustering to perform behavioural inference and action prediction.
  • Contribute to model training, evaluation, and deployment workflows, including data preprocessing, augmentation, hyperparameter tuning, and performance profiling.
  • Collaborate closely with engineers in computer vision, embedded systems, software, and UI/UX to ensure seamless integration of AI pipelines into real-time systems.
  • Produce clean, well-documented code and maintain version-controlled model artefacts and experiment logs.
  • Write technical documentation for models, training procedures, evaluation criteria, and system integration.
  • Bachelor's or Master's degree in Artificial Intelligence, Data Science, Computer Science, Machine Learning, or a closely related discipline.
  • 2-3 years of experience in machine learning roles through internships, academic labs, or early career positions.
  • Strong understanding of Convolutional Neural Networks (CNNs) for image and video-based tasks.
  • Strong understanding of transformer architectures and their applications in vision or multimodal learning.
  • Strong understanding of embedding systems and vector space modeling for semantic and similarity-based tasks.
  • Strong understanding of encoding mechanisms and dimensionality reduction techniques for latent representation.
  • Proficiency in Python and deep learning frameworks such as PyTorch or TensorFlow.
  • Familiarity with pose estimation, facial recognition, or classification models (e.g., OpenPose, MediaPipe, FaceNet, ResNet variants).
  • Experience training models with structured and unstructured visual datasets.
  • Exposure to techniques like cosine similarity, triplet loss, contrastive learning, or temporal prediction modeling.
  • Strong computer science fundamentals, including data structures, algorithms, and software design patterns.
  • Comfort working in Linux-based development environments and version control systems (Git).
  • A collaborative mindset, with excellent communication skills and a willingness to learn across domains.
Bonus (Nice to have):

  • Experience integrating vision-based AI models into embedded or robotics systems.
  • Familiarity with ONNX or TensorRT for model optimization and deployment.
  • Background in sequence modeling, recurrent architectures, or video-based action recognition.
  • Exposure to multimodal AI systems that blend image, pose, and metadata representations.
  • Familiarity with techniques like CLIP, DINO, or self-supervised representation learning.
  • Experience with MLOps or training orchestration tools such as MLflow, Weights & Biases, or DVC.
Other Requirements:
  • Must be a US Citizen or a valid Green Card holder. Visa sponsorship is not available for this role at this time.
  • Candidates must reside within a commutable distance of Santa Monica, California.
  • Compensation:  $100,000 to $120,000 per year
  •  Comprehensive health coverage and flexible PTO
  • Opportunity to work on innovative AI, robotics, XR, and autonomous technologies
  • Collaborative multidisciplinary engineering environment
  • Career growth and professional development opportunities

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