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Remote Embedded Machine Learning Jobs in Ashburn, VA

System One

Data Scientist

Mclean, VA · Remote

Design and develop machine learning models and analytical approaches to support search, discovery ... embedded analytics platforms). * Understanding of model evaluation, validation, and performance ...

System One

Data Scientist

Mclean, VA · Remote

Design and develop machine learning models and analytical approaches to support search, discovery ... embedded analytics platforms). * Understanding of model evaluation, validation, and performance ...

System One

Data Scientist

Mclean, VA · Remote

Design and develop machine learning models and analytical approaches to support search, discovery ... embedded analytics platforms). * Understanding of model evaluation, validation, and performance ...

System One

Data Scientist

Mclean, VA · Remote

Design and develop machine learning models and analytical approaches to support search, discovery ... embedded analytics platforms). * Understanding of model evaluation, validation, and performance ...

System One

Data Scientist

Mclean, VA · Remote

Design and develop machine learning models and analytical approaches to support search, discovery ... embedded analytics platforms). * Understanding of model evaluation, validation, and performance ...

System One

Data Scientist

Mclean, VA · Remote

Design and develop machine learning models and analytical approaches to support search, discovery ... embedded analytics platforms). * Understanding of model evaluation, validation, and performance ...

System One

Data Scientist

Mclean, VA · Remote

Design and develop machine learning models and analytical approaches to support search, discovery ... embedded analytics platforms). * Understanding of model evaluation, validation, and performance ...

System One

Data Scientist

Mclean, VA · Remote

Design and develop machine learning models and analytical approaches to support search, discovery ... embedded analytics platforms). * Understanding of model evaluation, validation, and performance ...

System One

Data Scientist

Mclean, VA · Remote

Design and develop machine learning models and analytical approaches to support search, discovery ... embedded analytics platforms). * Understanding of model evaluation, validation, and performance ...

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All JobsEmbedded Machine Learning JobsRemote Embedded Machine Learning JobsRemote Embedded Machine Learning Jobs in Ashburn, VA

Remote Embedded Machine Learning information

See Ashburn, VA salary details

$71.6K

$156.9K

$177.9K

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

As of Aug 22, 2026, the average yearly pay for remote embedded machine learning in Ashburn, VA is $156,851.00, according to ZipRecruiter salary data. Most workers in this role earn between $134,500.00 and $176,900.00 per year, depending on experience, location, and employer.

What is a remote embedded machine learning engineer?

A Remote Embedded Machine Learning Engineer is a professional who develops and deploys machine learning models on embedded systems like microcontrollers, IoT devices, and edge hardware, all while working remotely. Their work involves optimizing algorithms to run efficiently on devices with limited computing power, memory, and battery life. These engineers typically use frameworks such as TensorFlow Lite or TinyML to design intelligent features that operate directly on hardware, enabling real-time decision-making without relying heavily on cloud connectivity. They collaborate with cross-functional teams and often troubleshoot both software and hardware issues from a remote location.

What are the key skills and qualifications needed to thrive as a remote embedded machine learning engineer?

To thrive as a Remote 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 science, electrical engineering, or related fields. Familiarity with microcontrollers, edge AI frameworks (such as TensorFlow Lite or Edge Impulse), and version control systems is typically required. Strong problem-solving skills, effective communication, and self-motivation are essential soft skills for collaborating remotely and troubleshooting complex issues. These skills ensure successful deployment of intelligent solutions on resource-constrained devices and effective teamwork in distributed environments.

What are some common challenges faced by remote embedded machine learning engineers, and how can they be addressed?

Remote Embedded Machine Learning Engineers often encounter challenges related to hardware access, debugging embedded devices remotely, and collaborating with cross-functional teams across time zones. To address these, it's important to set up robust remote development environments, use simulation tools when physical hardware isn't available, and establish clear communication channels for effective teamwork. Regular virtual meetings and detailed documentation also help ensure alignment and smooth progress, despite the remote nature of the work.

What is the difference between Remote Embedded Machine Learning vs Remote Data Scientist?

AspectRemote Embedded Machine LearningRemote Data Scientist
Required CredentialsBachelor's or Master's in Computer Science, Electrical Engineering, or related fields; experience with embedded systems and ML frameworksBachelor's or Master's in Data Science, Statistics, or related fields; proficiency in data analysis and ML algorithms
Work EnvironmentEmbedded hardware devices, IoT systems, real-time processing environmentsCloud platforms, data analysis labs, remote offices
Employer & Industry UsageTech companies, IoT device manufacturers, automotive, roboticsFinance, healthcare, marketing, tech firms

Remote Embedded Machine Learning specialists focus on integrating ML models into embedded hardware for real-time applications, often working with IoT and robotics. In contrast, Remote Data Scientists analyze large datasets to extract insights, primarily working in cloud or office environments. Both roles require strong analytical skills but differ in technical focus and work settings.

What are the most commonly searched types of Embedded Machine Learning jobs in Ashburn, VA?

The most popular types of Embedded Machine Learning jobs in Ashburn, VA are:

What are popular job titles related to Remote Embedded Machine Learning jobs in Ashburn, VA?

For Remote Embedded Machine Learning jobs in Ashburn, VA, the most frequently searched job titles are:

What job categories do people searching Remote Embedded Machine Learning jobs in Ashburn, VA look for?

The top searched job categories for Remote Embedded Machine Learning jobs in Ashburn, VA are:

Remote Embedded Machine Learning jobs near you

Senior Machine Learning Engineer

Clearview AI, Inc.

Washington, DC • Remote

$107K - $146K/yr

Full-time

Medical, Dental, Vision, Retirement, PTO

Re-posted 26 days ago

Job description


Clearview AI is the leading provider of facial recognition technologies to US law enforcement, state, and federal agencies. Our mission is to help our users solve crimes and prevent financial fraud with the responsible use of our facial recognition software. Our company is a high-octane, fast growing startup looking to hire enthusiastic and intelligent team members to join our team. To learn more about us, and our revolutionary facial recognition technology, please visit www.clearview.ai.

Senior Machine Learning Engineer


Position Summary: We are hiring a highly technical individual contributor to push the limits of our computer vision and machine learning capabilities. This is a high-impact, hands-on role for a research-minded engineer who wants to build and ship models, not manage a team. Much of the work involves large-scale visual understanding, extracting structured signals from imagery and reasoning about the real-world context behind a photograph, but we care more about deep ML/CV ability than any one problem area and welcome strong generalists.

Responsibilities:
  • Build, train, evaluate, and deploy computer vision and multimodal models, taking them from early prototype through to production
  • Design systems that infer structured attributes and spatial context from imagery, combining learned models with geometric and heuristic reasoning
  • Train and fine-tune models on large, diverse real-world image datasets, and build the pipelines to curate and label that data at scale
  • Work with vision-language models (VLMs) and build rigorous evaluation frameworks to measure their accuracy on our tasks
  • Develop and benchmark high-performance image retrieval capabilities with embedding models and vector indexing strategies
  • Optimize models for inference latency and throughput using techniques like distillation, quantization, and GPU acceleration
  • Read current research, prototype novel algorithms from academic literature, and turn promising ideas into reliable production code
  • Implement efficient, scalable data pipelines and inference infrastructure
  • Develop high-performance tooling in ML and data engineering
  • Additional duties and responsibilities as reasonably required by the employee's supervisor or CEO
Requirements:
  • Experience building, training, evaluating, and deploying ML models in production
  • Strong experience using PyTorch, JAX, or other deep learning frameworks to develop and optimize models
  • Strong software engineering ability to build and maintain complex systems and work with large-scale datasets
  • Ability to solve open-ended problems and quickly learn new domains
  • Comfort operating with significant ownership and autonomy, making pragmatic trade-offs between model sophistication, velocity, inference and business constraints
  • BS, MS, or PhD in Computer Science or a related technical field, or equivalent practical experience

Nice to have:
  • Experience inferring structured, real-world attributes from images
  • Experience training models on large-scale, real-world image datasets
  • Familiarity with vision-language models (VLMs)
  • Ability to digest academic literature, prototype novel algorithms, and bridge the gap between research and production code
  • Experience building LLM or VLM pipelines and the evaluation frameworks to measure their performance
  • Experience in an ML role at a growth-stage startup
  • Publications in major ML or computer vision conferences (e.g., CVPR, ICML, ICCV, WACV)
  • Medical, Dental, Vision, STD and LTD Plans
  • FSA - Medical and Dependent Care
  • EAP and wellness programs
  • 13 Paid Holidays
  • Unlimited PTO
  • Flexible work environment - 100% remote
  • 401(k) plan

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