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Embedded Machine Learning Engineer Jobs in Houston, TX

SynergisticIT

Junior Machine Learning

Houston, TX

Computer Science Fundamentals and Programming. NLP and Text Mining Required. Pandas Data Analysis Required Experience on Cleansing Data with Python. Education Requirement: Bachelors, Masters in ...

Pivotal Solutions

Senior AI Engineer

Houston, TX · On-site

$99.80K - $137K/yr

Design, develop, and deploy advanced AI and machine learning models to solve complex business ... Mentor junior engineers and provide technical guidance on AI best practices, model development, and ...

Pivotal Solutions Inc

Senior AI Engineer

Houston, TX

$117K - $154.20K/yr

Job Posting Responsibilities Design, develop, and deploy advanced AI and machine learning models to ... Mentor junior engineers and provide technical guidance on AI best practices, model development, and ...

Varsity Tutors

Machine Learning Tutor

Missouri City, TX · Remote

$40/hr

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

Varsity Tutors

Machine Learning Tutor

Sugar Land, TX · Remote

$40/hr

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

Varsity Tutors

Machine Learning Tutor

Pearland, TX · Remote

$40/hr

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

Varsity Tutors

Machine Learning Tutor

Houston, TX · Remote

$40/hr

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

Deloitte

AI Solutions Architect

Houston, TX · On-site

$60.25 - $79.25/hr

... machine learning, and generative artificial intelligence use cases, including secure and high-availability deployment models • Collaborating with architects, engineers, data scientists, and ...

Intuitive Machines LLC

Embedded Software Engineer

Houston, TX · On-site

$113K - $148.70K/yr

Embedded Software Engineer Houston, TX About Intuitive Machines Intuitive Machines is an innovative and cutting-edge space company making cislunar space accessible to both public and private ...

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All JobsEmbedded Machine Learning Engineer JobsEmbedded Machine Learning Engineer Jobs in Houston, TX

Embedded Machine Learning Engineer information

See Houston, TX salary details

$66.8K

$146.5K

$166.2K

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

As of Jun 1, 2026, the average yearly pay for embedded machine learning engineer in Houston, TX is $146,477.00, according to ZipRecruiter salary data. Most workers in this role earn between $125,600.00 and $165,200.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as an Embedded Machine Learning Engineer, and why are they important?

To thrive as an Embedded Machine Learning Engineer, you need expertise in machine learning algorithms, embedded systems programming (C/C++ or Python), and a solid understanding of hardware constraints, usually supported by a degree in computer science, electrical engineering, or related fields. Familiarity with tools like TensorFlow Lite, ONNX, microcontroller SDKs, and experience with real-time operating systems (RTOS) are typically required. Strong problem-solving, communication skills, and the ability to collaborate across multidisciplinary teams help you stand out in this role. These skills are crucial for efficiently deploying intelligent models on resource-constrained devices, ensuring optimal performance and seamless integration in real-world applications.

What are some common challenges faced by Embedded Machine Learning Engineers when deploying models to hardware devices?

One of the main challenges for Embedded Machine Learning Engineers is optimizing machine learning models to run efficiently on devices with limited memory, processing power, and energy capacity. Ensuring real-time performance while maintaining accuracy often requires model quantization, pruning, or using lightweight architectures. Additionally, engineers must carefully manage hardware-software integration and address issues like compatibility with various microcontrollers and ensuring secure, reliable updates for deployed models. Close collaboration with hardware engineers and software developers is essential to overcome these challenges and deliver robust embedded AI solutions.

What does an Embedded Machine Learning Engineer do?

An Embedded Machine Learning Engineer designs and implements machine learning models that can run efficiently on embedded systems, such as microcontrollers and edge devices. Their work involves optimizing algorithms to fit within the resource constraints of these devices, integrating ML models into hardware, and ensuring real-time performance. They collaborate closely with hardware engineers and software developers to deploy intelligent features in products like smart sensors, IoT devices, and autonomous systems.

What is the difference between Embedded Machine Learning Engineer vs Firmware Engineer?

AspectEmbedded Machine Learning EngineerFirmware Engineer
Required CredentialsBachelor's/Master's in Computer Science, Electrical Engineering, or related; knowledge of ML frameworksBachelor's in Electrical Engineering, Computer Engineering, or related; embedded systems experience
Work EnvironmentDevelops ML models for embedded devices, often in IoT or smart devicesDesigns and implements low-level firmware for hardware devices
Industry UsageTech companies, IoT, consumer electronics, automotiveConsumer electronics, automotive, industrial equipment

The Embedded Machine Learning Engineer focuses on integrating machine learning models into embedded systems, while the Firmware Engineer specializes in developing low-level software for hardware devices. Both roles require embedded systems knowledge but differ in their core focus and skill sets.

What are popular job titles related to Embedded Machine Learning Engineer jobs in Houston, TX? For Embedded Machine Learning Engineer jobs in Houston, TX, the most frequently searched job titles are:
What job categories do people searching Embedded Machine Learning Engineer jobs in Houston, TX look for? The top searched job categories for Embedded Machine Learning Engineer jobs in Houston, TX are:
What cities near Houston, TX are hiring for Embedded Machine Learning Engineer jobs? Cities near Houston, TX with the most Embedded Machine Learning Engineer job openings:
Embedded Machine Learning Engineer jobs near you
Lead Machine Learning Ops Engineer

Lead Machine Learning Ops Engineer

Syntricate Technologies

Houston, TX

$50.25 - $69/hr

Other

Posted 14 days ago

Job description

Job Description:
Role: Lead Machine Learning Ops Engineer
Location: Houston, TX
Duration: 6+ months - Probably long term
Lead Machine Learning Ops Engineer - Must have 10 plus years and solid DevOps and Client experience
As a Lead Machine Learning Ops Engineer, you will play a pivotal role in implementing DevOps and Client Ops practices within the Corporate Data & Analytics Team to support AI/Client application enablement across all operating companies. Your primary responsibility will be to drive the adoption of best practices in DevOps and Client Ops, accelerating the deployment of AI/Client and data-driven solutions that meet the business needs.
We seek a motivated and skilled individual with a strong background in DevOps and Client Ops, a deep understanding of Infra Ops, and solid knowledge of AI/Client data and analytics cloud services and components. You will collaborate closely with data scientists, machine learning engineers, data engineers, software engineers, and platform architects, utilizing the latest tools and technologies to deploy and maintain AI/Client and advanced analytics solutions, as well as integrate analytic models with existing business applications.
Skills and Experience
  • Bachelor's Degree Computer Science, Computer Engineering, Information Technology, Software Engineering or equivalent technical discipline and 10+ years of experience in software engineering with a strong background in DevOps and Infrastructure as Code, supporting Machine Learning and Data Science workloads preferred. or
  • Master's Degree Computer Science, Computer Engineering, Information Technology, Software Engineering or equivalent technical discipline and 5+ years of experience in software engineering with a strong background in DevOps and Infrastructure as Code, supporting Machine Learning and Data Science workloads preferred.
  • Expertise on code versioning tools, such as Gitlab, GitHub, Azure DevOps, Bitbucket etc., GitHub Preferred, familiar with branch level code repository management.
  • Experience deploying Machine Learning solutions on cloud platforms (e.g., AWS, Azure, or GCP). Databricks, and AWS Preferred.
  • Proficient with GitHub actions to automate testing and deployment of data and Client workloads from CI/CD provider to Databricks.
  • Strong knowledge of infrastructure automation tools such as Terraform, Ansible, CloudFormation etc.
  • Experience with data processing frameworks/tools/platform such as Databricks, Apache Spark, Kafka, Flink, AWS cloud services for batch processing, batch streaming and streaming.
  • Experience containerizing analytical models using Docker and Kubernetes or other container orchestration platforms.
  • Technical expertise across all deployment models on public cloud, private cloud, and on-premises infrastructure.
Responsibilities
  • Develop automated build and deployment processes to enable continuous delivery of software releases, enhance the existing CI/CD pipelines for AIML application development and deployment.
  • Collaborate with data scientists, data engineers, data analysts, software engineers, IT specialists, and stakeholders to accelerate deployment of AI applications via CI/CD pipelines and maintain the SLAs of those applications at the centralized platform.
  • Design, develop and maintain infrastructure using infrastructure as code tools such as Terraform, Ansible, CloudFormation etc.
  • Templatize existing Databricks CLI codes to manage Databricks platform as code for AIML data pipelines (batch processing, batch streaming and streaming) and model serving endpoints.
  • Enhance the existing DevOps practices to improve the overall AIML application development lifecycle.
  • Work closely with cross-functional teams to ensure that applications are highly available and scalable.
  • Collaborate with development teams and cloud platform team to ensure that infrastructure meets the requirements of the application.
  • Establish and maintain best practices for cloud security, compliance, and cost optimization.

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