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Remote Embedded Machine Learning Jobs in California

Freenome

Staff Machine Learning Scientist

Brisbane, CA · On-site +1

$199K - $283K/yr

... remote. What you'll do: * Independently pursue cutting edge research in AI applied to biological ... machine learning, deep learning and complex data modeling. * Practical and theoretical ...

Freenome

Senior Machine Learning Scientist

Brisbane, CA · On-site +1

$110K - $150K/yr

... remote. What you'll do: * Independently pursue cutting edge research in AI applied to biological ... machine learning, deep learning and complex data modeling. * Practical and theoretical ...

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All JobsRemote Embedded Machine Learning JobsRemote Embedded Machine Learning Jobs in California

Remote Embedded Machine Learning information

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.
What are the most commonly searched types of Embedded Machine Learning jobs in California? The most popular types of Embedded Machine Learning jobs in California are:
What are popular job titles related to Remote Embedded Machine Learning jobs in California? For Remote Embedded Machine Learning jobs in California, the most frequently searched job titles are:
What job categories do people searching Remote Embedded Machine Learning jobs in California look for? The top searched job categories for Remote Embedded Machine Learning jobs in California are:
What cities in California are hiring for Remote Embedded Machine Learning jobs? Cities in California with the most Remote Embedded Machine Learning job openings:
Remote Embedded Machine Learning jobs near you
Infographic showing various Remote Embedded Machine Learning job openings in California as of June 2026, with employment types broken down into 73% Full Time, 16% Part Time, and 11% Contract. Highlights an 94% Physical, 1% Hybrid, and 5% Remote job distribution.

Machine Learning Engineer (Hybrid- Greenfield Opportunity)

Match Made Tech

Irvine, CA • On-site, Remote

$75 - $95/hr

Other

This job post has expired 1 day ago. Applications are no longer accepted.

Job description

AI/ML Engineer - Greenfield AI Project

UNABLE TO OFFER SPONSORSHIP- US CITIZENS & GREEN CARD ONLY

LOCATION: Irvine, CA (onsite). Monday through Thursday onsite, Fridays remote.

SPONSORSHIP NOT AVAILABLE- MUST BE US CITIZEN/ GREEN CARD HOLDER

COMPENSATION: $75-95 an hour. This is a 2-year contract that will convert to full-time.

About Us

We are on a mission to develop innovative AI solutions that will revolutionize our workforce. As we embark on an exciting new greenfield AI project, we are seeking an exceptional AI/ML Engineer to join our team and lead the development of machine learning models as part of this groundbreaking initiative.

Job Description

About the Role

We are seeking a skilled AI/ML Engineer to join our team to design, develop, and deploy machine learning models that solve real-world business challenges. You will work cross-functionally with data scientists, engineers, and product teams to bring cutting-edge AI solutions to production, with a strong focus on NLP, supervised learning, experimentation, and optimization.

Key Responsibilities
  • Model Development & Training
    • Collaborate with data scientists and stakeholders to translate project goals into scalable ML solutions.
    • Design, develop, and train models using state-of-the-art machine learning techniques and tools.
    • Select appropriate annotated datasets and transform raw data into machine learning-ready formats.
  • Data Preparation & Feature Engineering
    • Analyze and process structured/unstructured data for training and evaluation.
    • Develop feature extraction and selection pipelines to improve model performance.
  • Experimentation & Optimization
    • Run controlled experiments and perform statistical analysis to validate models.
    • Refine model hyperparameters and evaluation metrics for optimal performance.
  • Deployment & Integration
    • Work closely with ML Ops to deploy and monitor models in production environments.
    • Ensure all models are integrated seamlessly into existing systems.
  • Collaboration & Code Quality
    • Participate in code reviews, pair programming, and knowledge-sharing sessions.
    • Write testable, production-quality code that aligns with engineering best practices.
Qualifications & Skills
  • 3–5 years as an ML/AI Engineer or 1–3 years in an ML/AI leadership role
  • Proven experience building and deploying machine learning models in production
  • Solid understanding of classical ML algorithms (classification, regression, clustering)
  • Experience working with changing datasets and real-time data pipelines
  • Hands-on experience with Python and frameworks like PyTorch, TensorFlow, Scikit-learn
  • Strong knowledge of data processing (ETL), feature engineering, and statistical evaluation
  • Solid understanding of REST APIs, CI/CD, and containerized deployments (Docker, Kubernetes)
  • Strong communication, analytical thinking, and problem-solving skills
  • Bachelor's degree in Computer Science, Mathematics, Engineering, or a related quantitative field
Preferred Qualifications (Nice-to-Have)
  • Master's or PhD degree in Computer Science, Engineering, or a related field
  • Experience with neural networks and deep learning applications in computer vision, time-series analysis, or reinforcement learning
  • Familiarity with MLOps tools (MLflow, Kubeflow, SageMaker, etc.)
  • Exposure to cloud platforms (AWS, GCP, Azure)
  • Familiarity with version control and experimentation tracking tools
  • Basic knowledge of data governance, security, and compliance standards

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