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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 1% Internship, 90% Full Time, 6% Part Time, 2% Contract, and 1% Nights. Highlights an 85% Physical, 4% Hybrid, and 11% 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 yesterday

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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