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Audio Machine Learning Jobs in Texas (NOW HIRING)

Apple

Applied Scientist

Austin, TX · On-site

$175K - $308K/yr

... audio, world-class workouts and meditations, super fun games and more! The Services Data Science ... machine learning to help optimize marketing channels, via observational testing frameworks ...

Apple

Applied Scientist

Austin, TX

$175K - $308K/yr

... audio, world-class workouts and meditations, super fun games and more! The Services Data Science ... machine learning to help optimize marketing channels, via observational testing frameworks ...

Nutanix

Compiler Tools Engineer

Austin, TX · On-site

We develop LLVM compilation suite for Qualcomm's Hexagon DSP delivering rich performance for machine learning, wireless communication, audio, and image processing applications on the Android platform.

Qualcomm

Compiler Tools Engineer

Austin, TX · On-site

$81K - $109K/yr

We develop LLVM compilation suite for Qualcomm's Hexagon DSP delivering rich performance for machine learning, wireless communication, audio, and image processing applications on the Android platform.

Meta

SOC Architect, Reality Labs Silicon

Austin, TX

$178K/yr

... machine learning, graphics, displays, and sensors. Our chips will enable wearable devices where our ... audio, and ML accelerators * Collaborate with algorithms, firmware, and software teams to drive ...

Cook & Boardman

Audio Visual Technician I

Hutto, TX · On-site

Career Growth: You'll benefit from continuous learning, mentorship, and leadership training ... Work Environment Conditions will include proximity to forklifts or other heavy machinery and using ...

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All JobsAudio Machine Learning JobsAudio Machine Learning Jobs in Texas

Audio Machine Learning information

See Texas salary details

$27.5K

$78.7K

$159.8K

How much do audio machine learning jobs pay per year?

As of Sep 10, 2026, the average yearly pay for audio machine learning in Texas is $78,683.00, according to ZipRecruiter salary data. Most workers in this role earn between $46,600.00 and $105,300.00 per year, depending on experience, location, and employer.

What is an audio machine learning?

An Audio Machine Learning job involves developing algorithms and models that analyze, process, and generate audio data. Responsibilities typically include working with speech recognition, music analysis, sound classification, and audio enhancement. Professionals in this field use deep learning, signal processing, and neural networks to improve audio-based applications like voice assistants, noise reduction systems, and music recommendation engines. They often work with datasets of speech, music, or environmental sounds to build models that understand and manipulate audio signals effectively.

What does an audio machine learning do?

Professionals in Audio Machine Learning typically spend their days designing, developing, and optimizing machine learning models tailored to audio data, such as speech or music recognition systems. You may also preprocess large datasets, extract and engineer relevant features, and collaborate closely with data scientists, audio engineers, and software developers to integrate your work into larger applications. Regular tasks often include running experiments, evaluating model performance, tuning hyperparameters, and keeping up with the latest advancements in the field. Team meetings, code reviews, and presenting findings to stakeholders are also common parts of the workweek.

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

To thrive in Audio Machine Learning, you need a strong background in machine learning, digital signal processing, and proficiency with programming languages such as Python or MATLAB, typically supported by a relevant degree in computer science, electrical engineering, or a related field. Familiarity with frameworks like TensorFlow or PyTorch, experience with audio libraries (e.g., Librosa), and knowledge of cloud computing tools are highly valued, as are certifications in AI or data science. Strong problem-solving skills, creativity, and effective communication are essential soft skills for success in this field. These skills are crucial for developing innovative solutions, collaborating across multidisciplinary teams, and addressing complex audio data challenges in real-world projects.

What are the most commonly searched types of Audio Machine Learning jobs in Texas?

The most popular types of Audio Machine Learning jobs in Texas are:

What are popular job titles related to Audio Machine Learning jobs in Texas?

For Audio Machine Learning jobs in Texas, the most frequently searched job titles are:

What job categories do people searching Audio Machine Learning jobs in Texas look for?

The top searched job categories for Audio Machine Learning jobs in Texas are:

Audio Machine Learning jobs near you
Infographic showing various Audio Machine Learning job openings in Texas as of August 2026, with employment types broken down into 1% As Needed, 75% Full Time, 21% Part Time, 2% Contract, and 1% Nights. Highlights an 83% Physical, 3% Hybrid, and 14% Remote job distribution, with an average salary of $78,683 per year, or $37.8 per hour.

Embedded AI/ML Developer

Spring, TX • On-site

HP Development Company, L.P.
Construction of Buildings
Learn more about HP Development Company, L.P.external

$117K - $154K/yr

Full-time

Medical, Dental, Vision, Life, PTO

Posted 23 days ago

Job description

Embedded AI/ML Developer
Description -
The Embedded AI/ML Developer will design, develop, and optimize AI-enabled embedded software solutions for HP's commercial PC and connected device portfolio. This role focuses on deploying efficient machine learning models at the edge, integrating AI capabilities with firmware and system software, and enabling intelligent user experiences across resource-constrained platforms.
The engineer will work closely with hardware, firmware, software, and data science teams to translate AI/ML concepts into production-ready embedded implementations. Responsibilities include model optimization, inference runtime integration, performance tuning, debugging, documentation, and staying current with emerging edge AI technologies, tools, and industry best practices.
Responsibilities
  • Designs, develops, and optimizes embedded AI/ML software for edge devices, including PCs, docking solutions, displays, peripherals, and other intelligent client platforms.
  • Converts AI/ML algorithms and proof-of-concept models into efficient, production-quality embedded implementations optimized for latency, memory, power, and compute constraints.
  • Integrates machine learning inference engines, model runtimes, and AI accelerators into embedded firmware and system software environments.
  • Collaborates with cross-functional teams to define AI feature requirements, system architecture, data flow, model deployment strategy, and validation plans.
  • Profiles and tunes embedded AI workloads to improve inference performance, reduce memory footprint, improve responsiveness, and optimize power consumption.
  • Develops and maintains software interfaces between AI/ML components, firmware, device drivers, sensors, embedded controllers, and host applications.
  • Supports model compression, quantization, pruning, benchmarking, and deployment using embedded AI frameworks and hardware acceleration technologies.
  • Troubleshoots complex system-level issues involving AI inference, firmware behavior, sensor data, device communication, and platform integration.
  • Creates and maintains technical documentation, including architecture descriptions, design specifications, model deployment guides, validation procedures, and integration notes.
  • Explores emerging embedded AI, TinyML, NPU, MCU, sensor fusion, and edge inference technologies to help drive innovation across future HP platforms.

Education & Experience Recommended
  • Bachelor's or Master's degree in Computer Science, Computer Engineering, Statistics, Mathematics, Artificial Intelligence, Machine Learning, Robotics or a related technical discipline.
  • 7-10 years of relevant experience in embedded software, firmware, AI/ML deployment, edge inference, or system-level software development.

Preferred Certifications
Embedded AI/ML Engineering
  • Hands-on experience deploying AI/ML models on embedded systems, edge devices, MCUs, SoCs, NPUs, DSPs, or other constrained compute platforms.
  • Experience with model optimization techniques such as quantization, pruning, compression, TensorRT, ONNX, TFLite, or similar deployment toolchains.
  • Strong understanding of ML inference pipelines, data preprocessing, sensor input handling, feature extraction, and runtime performance tuning.
  • Experience integrating AI workloads with embedded firmware, device drivers, RTOS, Linux, Windows, or low-level system software environments.
  • Familiarity with AI validation, automated testing, CI/CD pipelines, model benchmarking, and regression testing for embedded platforms.

Embedded Systems and Platform Integration
  • Strong embedded software development experience using C/C++ and Python for prototyping, automation, model conversion, and testing.
  • Experience working with sensors, camera/audio pipelines, telemetry data, wireless modules, or contextual signals used by AI-enabled experiences.
  • Knowledge of embedded communication protocols such as UART, I2C, SPI, USB, PCIe, Bluetooth, Wi-Fi, or other device interconnect standards.
  • Ability to read hardware specifications, device datasheets, schematics, and platform architecture documents to support AI/ML feature integration.

Knowledge & Skills
  • Proficient in C/C++ and Python; familiar with embedded scripting, automation, build systems, and model deployment workflows.
  • Experience with AI/ML frameworks and formats such as PyTorch, TensorFlow, ONNX, TensorFlow Lite, OpenVINO, or similar toolchains.
  • Knowledge of embedded system architecture, boot flow, firmware interfaces, memory constraints, power management, and real-time execution tradeoffs.
  • Skilled in embedded debugging and profiling using JTAG, SWD, logic analyzers, oscilloscopes, performance counters, tracing tools, or vendor-specific debug environments.
  • Experience optimizing AI workloads for latency, throughput, memory footprint, thermal behavior, and battery life on constrained devices.
  • Familiarity with AI accelerator SDKs, NPU/DSP/GPU offload, heterogeneous compute, and hardware/software co-optimization.
  • Understanding of RTOS concepts, Linux/Windows system software, multi-threaded development, secure update mechanisms, and production-quality embedded software practices.
  • Strong analytical and problem-solving skills with the ability to debug complex interactions across model behavior, firmware, drivers, sensors, and host software.
  • Ability to work independently and collaboratively in a cross-functional engineering environment, translating AI concepts into reliable product experiences.

The pay range for this role is $147,050 to $230,850 USD annually with additional opportunities for pay in the form of bonus and/or equity (applies to United States of America candidates only). Pay varies by work location, job-related knowledge, skills, and experience.
Benefits:
HP offers a comprehensive benefits package for this position, including:
  • Health insurance
  • Dental insurance
  • Vision insurance
  • Long term/short term disability insurance
  • Employee assistance program
  • Flexible spending account
  • Life insurance
  • Generous time off policies, including;
  • 4-12 weeks fully paid parental leave based on tenure
  • 11 paid holidays
  • Additional flexible paid vacation and sick leave (US benefits overview)

The compensation and benefits information is accurate as of the date of this posting. The Company reserves the right to modify this information at any time, with or without notice, subject to applicable law.
Job -
Software
Schedule -
Full time
Shift -
No shift premium (United States of America)
Travel -
Relocation -
Equal Opportunity Employer (EEO) -
HP, Inc. provides equal employment opportunity to all employees and prospective employees, without regard to race, color, religion, sex, national origin, ancestry, citizenship, sexual orientation, age, disability, or status as a protected veteran, marital status, familial status, physical or mental disability, medical condition, pregnancy, genetic predisposition or carrier status, uniformed service status, political affiliation or any other characteristic protected by applicable national, federal, state, and local law(s).
Please be assured that you will not be subject to any adverse treatment if you choose to disclose the information requested. This information is provided voluntarily. The information obtained will be kept in strict confidence.
For more information, review HP's EEO Policy or read about your rights as an applicant under the law here: "Know Your Rights: Workplace Discrimination is Illegal"

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