1

Embedded Machine Learning Engineer Jobs in Houston, TX

Senior Machine Learning Engineer

Houston, TX · On-site

$117K - $154K/yr

The Machine Learning Engineer at Vitol has visibility and impact across the full project workflow: from working with business stakeholders to help define the project, to data collation and processing ...

Senior Machine Learning Engineer

Houston, TX · On-site

$117K - $154K/yr

The Machine Learning Engineer at Vitol has visibility and impact across the full project workflow: from working with business stakeholders to help define the project, to data collation and processing ...

Senior Machine Learning Engineer

Houston, TX · On-site

$117K - $154K/yr

The Machine Learning Engineer at Vitol has visibility and impact across the full project workflow: from working with business stakeholders to help define the project, to data collation and processing ...

* Own model training and post-training pipelines end to end: SFT, RLHF, PPO, DPO, and reward model training in PyTorch * Build and maintain the infrastructure around RL training: rollout collection ...

* Own model training and post-training pipelines end to end: SFT, RLHF, PPO, DPO, and reward model training in PyTorch * Build and maintain the infrastructure around RL training: rollout collection ...

Senior Machine Learning Engineer

Houston, TX · On-site

$99K - $137K/yr

Senior Machine Learning Engineer Location: Houston, TX Environment: Standard, 5-days onsite : Must-Have (Technical Expertise & Core Responsibilities) * Deep Neural Networks (DNN): * Hands-on ...

As an AI Engineering team member, you will be instrumental in advancing new features and/or solutions from the Proof of Concept stage to full production readiness. Your role involves refining and ...

As an AI Engineering team member, you will be instrumental in advancing new features and/or solutions from the Proof of Concept stage to full production readiness. Your role involves refining and ...

Lead Machine Learning Engineer

Houston, TX · On-site +1

$97K - $128K/yr

As a Lead Machine Learning Engineer specializing in conversational and agentic systems at NobleAI, you will be responsible for architecting, building, and deploying intelligent features to our VIP ...

Lead Machine Learning Engineer

Houston, TX · Remote

$104K - $138K/yr

As a Lead Machine Learning Engineer specializing in conversational and agentic systems at NobleAI, you will be responsible for architecting, building, and deploying intelligent features to our VIP ...

next page

Showing results 1-20

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 Sep 9, 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 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 are the key skills and qualifications needed to thrive as an embedded machine learning engineer?

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 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 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:

Infographic showing various Embedded Machine Learning Engineer job openings in Houston, TX as of June 2026, with employment types broken down into 24% Full Time, 72% Part Time, 2% Temporary, 1% Contract, and 1% Nights. Highlights an 88% Physical, 2% Hybrid, and 10% Remote job distribution, with an average salary of $146,477 per year, or $70.4 per hour.

Machine Learning Engineer (Autonomy)

Houston, TX • On-site

$140K - $180K/yr

Full-time

Posted 18 days ago


Job description

About Mariana Minerals
Mariana Minerals is a software-first, vertically integrated minerals company on a mission to supply the critical minerals powering modern energy, AI, and defense technologies. We're reimagining the minerals supply chain by combining deep industry expertise with advanced software, automation, and data-driven decision-making.
Role Overview
We are hiring Machine Learning Engineers (Autonomy) to build the autonomy and sensor-integration software that lets our mining vehicles perceive, decide, and drive themselves.
In this role you will work on parts of the software and sensor integration that enables full mining autonomy - sensor fusion across LiDAR, cameras, radar, and IMU/GNSS; perception, localization, and mapping; and the autonomy stack that turns sensing into safe vehicle motion. You will carry work from architecture and algorithm design through implementation, simulation, and bench and field validation, working hand in hand with our hardware, controls, and systems-engineering teams. This is a hands-on, first-principles role for an engineer who wants to develop the world's first fully autonomous mines.
What You'll Do
  • Develop autonomy software for autonomous mining vehicles focusing on one or more area: perception, SLAM, motion planning, and control
  • Integrate and calibrate the sensing suite (LiDAR, cameras, radar, IMU, GNSS), implementing sensor fusion and time synchronization robust to dust, vibration, and corrosion-heavy mining environments
  • Build and maintain embedded and real-time software that bridges sensing, compute, and actuation, with attention to safety, latency, and reliability
  • Develop simulation, logging, and data pipelines to test autonomy behavior and drive performance against safety and availability targets
  • Build bench, rig, and field validation of the autonomy stack, debugging across the full software-hardware boundary
  • Collaborate with hardware and controls engineers to integrate sensing, compute, and actuation into a complete vehicle

What You'll Bring
  • Bachelor's degree in Mechatronics, Robotics, Computer Science, Electrical, or related engineering discipline
  • 2+ years developing autonomy, robotics, or embedded software, ideally for mobile robots or vehicles
  • Strong proficiency in C++ and/or Python, and with a robotics middleware such as ROS/ROS 2
  • Hands-on experience with sensor integration and fusion - LiDAR, cameras, radar, IMU, GNSS - and with perception, localization, or motion-planning algorithms
  • Working knowledge of real-time and embedded systems, and of the controls and software-hardware integration that drive actuation
  • Experience in autonomous vehicles, robotics, automotive, or off-highway equipment strongly preferred
Our culture is built on four principles:
Everyone Gets Home Safe. We never put speed or cost ahead of people.
Extreme Ownership. We take full responsibility for outcomes, relentlessly driving toward solutions.
Engineer Out Requirements, then Automate. We simplify, optimize, and then automate for scale.
Share Your Legos. We collaborate openly, share knowledge, and empower each other to build bigger, better solutions.
Join us as we build the future of responsible mineral sourcing and supply!