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Hourly 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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Hourly Embedded Machine Learning information

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

To thrive as an Hourly 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 engineering or a related field. Familiarity with tools such as TensorFlow Lite, embedded Linux, microcontroller development environments, and model optimization frameworks is typically required. Strong problem-solving skills, adaptability, and effective communication help you address complex technical challenges and collaborate with cross-functional teams. These skills are crucial for designing efficient, real-time ML solutions that operate reliably on resource-constrained embedded devices.

How does an Hourly Embedded Machine Learning professional typically collaborate with hardware and software teams during a project?

As an Hourly Embedded Machine Learning professional, you will often work closely with both hardware and software engineering teams to ensure that machine learning models are efficiently integrated into embedded systems. This typically involves frequent communication to align on hardware constraints, such as memory and processing power, and to optimize algorithms for real-time performance. You may also participate in joint debugging sessions and code reviews to address integration issues and streamline deployment. Collaboration is key, as successful projects depend on the seamless interaction between machine learning solutions and the embedded hardware platform.

What is an Hourly Embedded Machine Learning engineer?

An Hourly Embedded Machine Learning engineer is a professional who specializes in developing and deploying machine learning models on embedded systems, such as microcontrollers, IoT devices, or edge devices, and is compensated on an hourly basis rather than a salaried or project-based arrangement. These engineers work to optimize algorithms so they can run efficiently on devices with limited computing power, memory, and energy resources. Their responsibilities often include model selection, quantization, optimization, and integration of machine learning pipelines into hardware. Hiring on an hourly basis allows for flexibility in project scope and duration, making it ideal for companies with specific, time-limited needs. They often collaborate with hardware engineers, data scientists, and software developers to create intelligent embedded solutions.

What is the difference between Hourly Embedded Machine Learning vs Hourly Data Scientist?

AspectHourly Embedded Machine LearningHourly Data Scientist
CredentialsKnowledge of embedded systems, programming, ML algorithmsDegree in Data Science, Statistics, or related field
Work EnvironmentEmbedded hardware, IoT devices, real-time systemsData analysis, modeling, visualization in office or cloud
Industry UsageConsumer electronics, automotive, IoT devicesFinance, healthcare, marketing, research

Hourly Embedded Machine Learning specialists focus on integrating ML models into embedded systems and hardware, often working with IoT devices and real-time constraints. In contrast, Hourly Data Scientists analyze large datasets to develop predictive models primarily in cloud or office environments. While both roles require programming skills, embedded ML emphasizes hardware integration, whereas data science centers on data analysis and visualization.

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 Hourly Embedded Machine Learning jobs in Texas? For Hourly Embedded Machine Learning jobs in Texas, the most frequently searched job titles are:
What job categories do people searching Hourly Embedded Machine Learning jobs in Texas look for? The top searched job categories for Hourly Embedded Machine Learning jobs in Texas are:
Hourly Embedded Machine Learning jobs near you
Infographic showing various Hourly Embedded Machine Learning job openings in Texas as of July 2026, with employment types broken down into 91% Full Time, 7% Part Time, and 2% Contract. Highlights an 82% Physical, 5% Hybrid, and 13% Remote job distribution.

Sr. Embedded Machine Learning Engineer

Allen Control Systems

Austin, TX • On-site

$122K - $161K/yr

Full-time

Medical, Dental, Vision, PTO

Posted 22 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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