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Embedded Machine Learning Jobs in Texas (NOW HIRING)

Bumble Inc.

Senior Machine Learning Engineer

Austin, TX · On-site

$220K - $250K/yr

AI is deeply embedded in how we evolve at Bumble. In this role, you'll independently apply modern machine learning and emerging AI techniques, contributing to scalable systems while ensuring ...

Bumble Inc.

Senior Machine Learning Engineer

Austin, TX · On-site

$220K - $250K/yr

AI is deeply embedded in how we evolve at Bumble. In this role, you'll independently apply modern machine learning and emerging AI techniques, contributing to scalable systems while ensuring ...

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All JobsEmbedded Machine Learning JobsEmbedded Machine Learning Jobs in Texas

Embedded Machine Learning information

See Texas salary details

$65.2K

$142.9K

$162.1K

How much do embedded machine learning jobs pay per year?

As of Aug 5, 2026, the average yearly pay for embedded machine learning in Texas is $142,900.00, according to ZipRecruiter salary data. Most workers in this role earn between $122,500.00 and $161,200.00 per year, depending on experience, location, and employer.

What are some common challenges faced by professionals working in embedded machine learning roles?

Professionals in embedded machine learning roles often face the challenge of optimizing machine learning models to run efficiently on resource-constrained hardware, such as microcontrollers or edge devices with limited memory and processing power. Balancing model accuracy, inference speed, and energy consumption can require creative problem-solving and deep knowledge of both hardware and software. Additionally, collaboration with hardware engineers, data scientists, and software developers is key, as projects typically require cross-functional teamwork to meet performance and deployment goals. Staying current with rapidly evolving tools and best practices is also important in this dynamic field.

What is an embedded machine learning?

An Embedded Machine Learning job involves developing and optimizing machine learning models to run efficiently on resource-constrained devices like microcontrollers, edge devices, and IoT hardware. Professionals in this role work on model compression, low-power inference, and real-time processing, ensuring AI capabilities can function without relying on cloud computing. Responsibilities often include data preprocessing, feature extraction, model training, and deployment on embedded systems using frameworks like TensorFlow Lite or Edge Impulse.

What are the key skills and qualifications needed to thrive in embedded machine learning?

To thrive in Embedded Machine Learning, you should have expertise in machine learning algorithms, embedded systems programming (e.g., C/C++, Python), and a solid understanding of hardware-software integration, typically backed by a degree in computer engineering, electrical engineering, or a related field. Familiarity with edge AI tools (such as TensorFlow Lite, ONNX, or Edge Impulse), microcontrollers, and real-time operating systems is highly valued, alongside relevant certifications such as Embedded Systems or AI certificates. Strong problem-solving skills, effective communication, and the ability to work cross-functionally are crucial soft skills in this field. These qualifications and qualities are vital for creating efficient, reliable AI solutions that operate seamlessly within resource-constrained environments and interdisciplinary project teams.

What are the most commonly searched types of Embedded Machine Learning jobs in Texas? The most popular types of Embedded Machine Learning jobs in Texas are:
What are popular job titles related to Embedded Machine Learning jobs in Texas? For Embedded Machine Learning jobs in Texas, the most frequently searched job titles are:
What job categories do people searching Embedded Machine Learning jobs in Texas look for? The top searched job categories for Embedded Machine Learning jobs in Texas are:
What cities in Texas are hiring for Embedded Machine Learning jobs? Cities in Texas with the most Embedded Machine Learning job openings:
Embedded Machine Learning jobs near you
Infographic showing various Embedded Machine Learning job openings in Texas as of July 2026, with employment types broken down into 92% Full Time, 6% Part Time, and 2% Contract. Highlights an 84% Physical, 5% Hybrid, and 11% Remote job distribution, with an average salary of $142,900 per year, or $68.7 per hour.

Sr. Embedded Machine Learning Engineer

Allen Control Systems

Austin, TX • On-site

$195K - $261K/yr

Full-time

Medical, Dental, Vision, PTO

Posted 26 days ago

Job description

Company Overview
Allen Control Systems (ACS) is a cutting-edge defense startup founded by two former Navy electrical engineers with a proven track record in robotics and software. We are developing an autonomous gun turret using advanced computer vision and control systems to precisely detect, track, and neutralize enemy drones.
With an engineering-first culture, ACS values technical excellence and innovation. Backed by our founders' successful exits from two previous ventures acquired for a combined $180M in 2022, we are committed to ensuring that the groundbreaking technologies we develop will have a real-world impact.
About The Role
We are looking for a Senior Embedded Machine Learning Engineer to own the end-to-end process of taking trained ML models and deploying them efficiently onto resource-constrained edge hardware. This role sits at the intersection of machine learning, embedded systems, and hardware engineering. You will integrate, convert, and optimize models to run within strict constraints on latency, memory, power, and thermal budget, and build the supporting C++ infrastructure that hosts them on device. You will partner closely with the CVML team who build the models, the embedded and firmware teams who own the device, and the product team who define performance targets. Success means models that are not just accurate in the lab but fast, small, and dependable in the field.
What You'll Do
  • Apply quantization, pruning, knowledge distillation, operator fusion, and graph optimization to shrink models and reduce inference cost while protecting accuracy; convert trained models into edge-deployable formats using ONNX and TensorRT.
  • Profile inference on target accelerators including GPUs, NPUs, DSPs, and FPGAs; measure latency, throughput, memory footprint, and power consumption, then drive the changes needed to hit performance targets.
  • Design, write, and maintain the C++ application code that hosts inference on device, including pre- and post-processing pipelines, data and memory management, threading, and interfaces to the rest of the embedded system; ensure the combined model and C++ stack meets real-time constraints and fits within device memory budget.
  • Build test harnesses to verify on-device accuracy against reference results and catch regressions from optimization or quantization; contribute to tooling for packaging, versioning, and delivering model updates to deployed devices.
  • Set best practices for edge deployment, review designs and code, and mentor other engineers on optimization and embedded ML techniques; work closely with research, firmware, and product teams to set realistic performance targets and feed hardware constraints back into model design.

What You'll Need
  • 10+ years of professional software or systems engineering experience, including at least 2 years focused on deploying ML models to embedded or edge devices; Bachelor's or Master's degree in Computer Science, Electrical Engineering, Computer Engineering, or equivalent practical experience.
  • Very strong C++ proficiency; working knowledge of CUDA; hands-on experience with PyTorch and at least one edge inference runtime such as TensorFlow Lite, ONNX Runtime, or TensorRT.
  • Practical experience with model optimization techniques including post-training quantization, quantization-aware training, pruning, and distillation; demonstrated ability to profile and optimize for latency, memory, and power on constrained hardware.
  • Working knowledge of embedded or edge platforms such as NVIDIA Jetson, Qualcomm, ARM Cortex, or comparable NPUs and SoCs, and of Linux or an RTOS; solid grasp of computer architecture concepts relevant to inference including memory hierarchy, fixed-point arithmetic, and accelerator offload; domain experience in computer vision or sensor processing on device.

You'll Stand Out
  • Hands-on experience deploying computer vision models for detection or tracking tasks on embedded or edge hardware.
  • Experience with NVIDIA Jetson specifically, including TensorRT optimization and deployment on Jetson platforms.
  • Background in defense, autonomous systems, or robotics where real-time reliability matters.
  • Experience building or contributing to model update and OTA delivery pipelines for deployed edge devices.

What We Offer
  • Competitive salary
  • ACS Equity Package
  • Health, Dental, Vision Insurance
  • Paid Time Off

Allen Control Systems is an Equal Opportunity Employer, providing equal employment opportunities to all employees and applicants for employment. Allen Control Systems prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws. #LI-AS1

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