ZipRecruiter

Log In

2

Remote Embedded Machine Learning Jobs (NOW HIRING)

Apex Informatics

Machine Learning Engineer

Remote We are seeking an Applied Machine Learning Engineer with a strong focus on practical solutions and software development (ability to work on both open-ended research problems and production ...

Spear AI

Machine Learning Engineer

Washington, DC · On-site +1

We're seeking a skilled Machine Learning Engineer to build and deploy production ML systems for the ... Onsite / Remote / Flexible work arrangements or hybrid options (position dependent) * Relocation ...

Match Group

Machine Learning Engineer II

Palo Alto, CA · On-site +1

$114K - $156K/yr

Machine Learning Software Engineers who bridge the gap between research and production by ... are embedded in core Tinder user flows at scale. Where You'll Work: This is a hybrid role and ...

DocuSign

Machine Learning Engineer

Seattle, WA · On-site +1

$164K - $266K/yr

What you'll do As a Machine Learning Engineer on the AI Platform team, you will design and build ... Employee divides their time between in-office and remote work. Access to an office location is ...

Match Group

Machine Learning Engineer II

Palo Alto, CA · On-site +1

$145K - $165K/yr

Machine Learning Software Engineers who bridge the gap between research and production by ... are embedded in core Tinder user flows at scale. Where You'll Work: This is a hybrid role and ...

DocuSign

Machine Learning Engineer

San Francisco, CA · On-site +1

$164K - $266K/yr

What you'll do As a Machine Learning Engineer on the AI Platform team, you will design and build ... Employee divides their time between in-office and remote work. Access to an office location is ...

Canopy

Senior Machine Learning Engineer

Detroit, MI · On-site +1

$126K - $180K/yr

... devices/embedded systems. * White-box understanding of classical ML algorithms (SVMs, HMMs ... Proficiency in Unix-based environments (Linux, macOS) including working with remote servers and ...

Canopy

Senior Machine Learning Engineer

Detroit, MI · Remote

$126K - $180K/yr

... devices/embedded systems. * White-box understanding of classical ML algorithms (SVMs, HMMs ... Proficiency in Unix-based environments (Linux, macOS) including working with remote servers and ...

Paylocity

Senior Machine Learning Engineer

$125K - $165K/yr

This is a fully remote position, allowing you to work from home or location of record within the U ... Senior Engineer Machine Learning Position Overview Paylocity is growing its Machine Learning ...

Paylocity

Senior Machine Learning Engineer

$125K - $165K/yr

This is a fully remote position, allowing you to work from home or location of record within the U ... Our machine learning engineering team is responsible for developing infrastructure and tooling to ...

next page

Showing results 1-20

All JobsEmbedded Machine Learning JobsRemote Embedded Machine Learning Jobs

Remote Embedded Machine Learning information

See salary details

$70K

$153.4K

$174K

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

As of Jul 10, 2026, the average yearly pay for remote 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 a Remote Embedded Machine Learning Engineer, and why are they important?

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 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 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 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.
More about Remote Embedded Machine Learning jobs
What cities are hiring for Remote Embedded Machine Learning jobs? Cities with the most Remote 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 Remote Embedded Machine Learning jobs? States with the most job openings for Remote Embedded Machine Learning jobs include:
What job categories do people searching Remote Embedded Machine Learning jobs look for? The top searched job categories for Remote Embedded Machine Learning jobs are:
Remote Embedded Machine Learning jobs near you
Infographic showing various Remote Embedded Machine Learning job openings in the United States as of July 2026, with employment types broken down into 43% Full Time, 14% Part Time, and 43% Contract. Highlights an 100% Remote job distribution, with an average salary of $153,383 per year, or $73.7 per hour.
Machine Learning Engineer

Machine Learning Engineer

Apex Informatics

Remote

Other

This job post has expired today. Applications are no longer accepted.

Job description

Applied Machine Learning Engineer | Music Software (Multiple Roles open)
Role: Applied Machine Learning Engineer (Mid - Senior Opportunity) Company: Splash
Employment Type: Contract (3 months +, potential for extension) Location: Remote
We are seeking an Applied Machine Learning Engineer with a strong focus on practical solutions and software development (ability to work on both open-ended research problems and production-ready API code). In this role, you'll leverage off-the-shelf tools and custom-built ML models to solve challenges in music product development and improve manual music processes. This position is ideal for engineers with demonstrable experience building functional, production-ready models and who are passionate about user experience and Product.
Key Responsibilities:
• Design and implement ML algorithms to enhance music creation tools and solve various user problems in line with product goals.
• Identify and implement off-the-shelf ML and AI tools to solve practical problems efficiently.
• Understand the requirements of running models in production, including domain shift testing, QA, A/B testing and so on.
• Maintain production-ready code with considerations for how solutions fit the product and enhance the user experience.
• Build scalable, maintainable data pipelines to handle audio and other unstructured data.
• Collaborate with Product and Engineering teams to ensure seamless integration of ML solutions into production systems.
• Evaluate, deploy, and fine-tune pre-trained models for tasks like audio analysis, melody generation, and process automation.
• Uphold ethical AI practices, ensuring fairness and responsible AI use in music-related applications.
What You Bring
• Proven software development experience, ideally in Python (other languages a plus).
• Experience implementing and deploying ML models, using PyTorch framework.
• Familiarity with AWS cloud environment for deploying and scaling ML solutions.
• Ability to preprocess and model unstructured data, especially audio.
• A strong focus on applied problem-solving, with a practical approach to integrating existing tools and systems.
• A good understanding of music, production, or audio technology processes (or a strong interest in music)
• Familiarity with GenAI architectures like transformers, LLMs, or diffusion models.
• Proactive nature, ability to creatively solve problems you face and bring new ideas to the team.
• Clear and effective communication with technical and non-technical stakeholders.
• Ability to work independently and remotely while collaborating closely with cross-functional teams.

Apply