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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 ...

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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 Jul 15, 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 job?

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 the Embedded Machine Learning position, and why are they important?

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 100% Full Time. Highlights an 100% In-person 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

$122K - $161K/yr

Full-time

Medical, Dental, Vision, PTO

Posted 5 days ago

Job description

Senior Embedded Machine Learning Engineer for Autonomous Anti-Drone Systems
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 venture acquired for a combined $180M in 2022, we are committed to ensuring that the groundbreaking technologies we develop will have a real-world impact.
Position Summary:
We are hiring a Senior Embedded Machine Learning Engineer to own the end-to-end process of taking trained machine learning models including any code supporting them and deploying them efficiently onto resource-constrained edge hardware. This person sits at the intersection of machine learning, embedded systems, and hardware engineering.
The role has two tightly linked primary responsibilities: integrating, converting, and optimizing models so they run within strict constraints on latency, memory, power, and thermal budget; and building and integrating the supporting C++ code that runs the models on device and performs any necessary pre or post processing. The role is highly cross-functional. You will partner with CVML who build the models, with embedded and firmware teams who own the device, and with product teams 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:
• Model optimization. Apply quantization, pruning, knowledge distillation, operator fusion, and graph optimization to shrink models and reduce inference cost while protecting accuracy.
• Model conversion and deployment. Convert trained models into formats suitable for edge runtimes using ONNX and TensorRT and deploy them to target hardware.
• Hardware bring-up and benchmarking. Profile inference on accelerators such as GPUs, NPUs, DSPs, TPUs, or FPGAs. Measure latency, throughput, memory footprint, and power, then drive the changes needed to hit targets.
• C++ application integration. Design, write, and maintain the supporting C++ code that hosts inference on device. This includes the application and library code that loads and runs models, the pre- and post-processing pipelines, data and memory management, threading, and the interfaces to the rest of the embedded system. Ensure the combined model and C++ stack meets real-time constraints, fits within the device memory budget, and behaves reliably on the target platform, using Python where appropriate for tooling and validation.
• Accuracy and quality validation. Build test harnesses that verify on-device accuracy against reference results and catch regressions introduced by optimization or quantization.
• Model update pipeline. Contribute to the tooling and processes for packaging, versioning, and delivering model updates to deployed devices, including over-the-air update paths where applicable.
• Cross-functional collaboration. Work closely with research, firmware, and product teams to set realistic performance targets early and to feed hardware constraints back into model design.
• Technical leadership. Set best practices for edge deployment, review designs and code, and mentor other engineers on optimization and embedded ML techniques.
What You'll Do:
  • Development and optimization of computer vision algorithms for our autonomous gun turret, focusing on real-time drone detection, tracking, and classification.
  • Design and implement machine learning models that can operate in resource-constrained environments while maintaining high accuracy and reliability.
  • Collaborate closely with electrical engineers to integrate computer vision systems into the turret's hardware architecture.
  • Conduct extensive testing and validation of computer vision algorithms in various scenarios to ensure robustness and performance under different environmental conditions.
  • Contribute to the hardening of the prototype turret into a military-grade system, and assist in developing variants for different weapon systems and engagement ranges.

What You'll Need:
  • A Bachelor's or Master's Degree in Computer Science, Electrical Engineering, Computer Engineering or a related field, or equivalent practical experience.
  • 10+ or more years of professional software or systems engineering experience, including at least 2 years focused on deploying ML models to embedded or edge devices.
  • Very strong proficiency in C/C++ (or Python but C++ most important)
  • Proficiency with CUDA
  • Hands-on experience with PyTorch and with at least one edge runtime or inference format (TensorFlow Lite, ONNX Runtime, TensorRT, or similar).
  • Practical experience with model optimization techniques such as quantization (post-training and quantization-aware), pruning, or distillation.
  • Demonstrated ability to profile and optimize for latency, memory, and power on constrained hardware.
  • Working knowledge of embedded or edge platforms (for example NVIDIA Jetson, Google Coral, 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

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.

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