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Audio Machine Learning Jobs in Texas (NOW HIRING)

Senior Machine Learning Engineer

Austin, TX · On-site

$103K - $142K/yr

Senior Machine Learning Engineer We are seeking a Senior Machine Learning Engineer to support our ... Work with multi-modal AI systems across computer vision, audio, and natural language domains.

Senior Machine Learning Engineer

Austin, TX

$103K - $142K/yr

We are seeking a Senior Machine Learning Engineer to support our Public Sector initiatives focused ... Work with multi-modal AI systems across computer vision, audio, and natural language domains.

Senior Machine Learning Engineer

Austin, TX · On-site

$103K - $142K/yr

We are seeking a Senior Machine Learning Engineer to support our Public Sector initiatives focused ... Work with multi-modal AI systems across computer vision, audio, and natural language domains.

Big Data Developer

Austin, TX · On-site

$52.50 - $68.25/hr

... audio, and images. Relational databases and NoSQL databases, such as Apache Hadoop, Apache Spark ... Experience with machine learning algorithms and automated machine learning to automate and build ...

... and machine learning. • Strong background in signal processing theory and application (radar, communication, electronic warfare, sonar, audio, or biomedical) • Experience with current AI ...

Senior AI Engineer

Fort Worth, TX · On-site +1

$117K - $154K/yr

... and machine learning. • Strong background in signal processing theory and application (radar, communication, electronic warfare, sonar, audio, or biomedical) • Experience with current AI ...

... and machine learning. • Strong background in signal processing theory and application (radar, communication, electronic warfare, sonar, audio, or biomedical) • Experience with current AI ...

... and machine learning. • Strong background in signal processing theory and application (radar, communication, electronic warfare, sonar, audio, or biomedical) • Experience with current AI ...

Senior AI Engineer

Fort Worth, TX · On-site +1

$117K - $154K/yr

... and machine learning. • Strong background in signal processing theory and application (radar, communication, electronic warfare, sonar, audio, or biomedical) • Experience with current AI ...

Bachelor's or Master's degree in Computer Science, Machine Learning, Artificial Intelligence, or a ... Experience with audio-focused ML projects or similar domains involving unstructured data.

Bachelor's or Master's degree in Computer Science, Machine Learning, Artificial Intelligence, or a ... Experience with audio-focused ML projects or similar domains involving unstructured data.

... audio, world-class workouts and meditations, super fun games and more! The Services Data Science ... Master's degree in Statistics, Economics, Mathematics, Machine Learning, Computer Science ...

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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 Jun 19, 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 are the key skills and qualifications needed to thrive in the Audio Machine Learning position, and why are they important?

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 typical daily responsibilities of someone working in Audio Machine Learning?

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 is an Audio Machine Learning job?

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 is the salary of audio AI engineer?

The salary of an audio AI engineer typically ranges from $80,000 to $150,000 annually, depending on experience, location, and company size. Professionals with expertise in machine learning, signal processing, and programming languages like Python or TensorFlow tend to earn higher salaries.

What is a $900,000 AI job?

A $900,000 AI job typically refers to a high-level position in artificial intelligence, such as senior machine learning engineer or AI research director, often requiring advanced skills in deep learning, data science, and programming. These roles usually involve leadership, extensive experience, and may include stock options or bonuses, especially in large tech companies or startups. Compensation at this level reflects significant expertise and responsibility in developing and deploying AI systems.

Which 5 jobs will survive AI?

Audio Machine Learning professionals are likely to continue in roles that require creative interpretation, complex problem-solving, and domain expertise, such as audio engineers, sound designers, data scientists, AI specialists, and research scientists. These roles involve tasks that are difficult to fully automate, especially those requiring nuanced understanding of sound and human perception. Skills in programming, signal processing, and domain-specific knowledge will remain valuable in these jobs.

What engineers make $500,000?

Senior audio machine learning engineers with extensive experience, advanced skills in deep learning, and proficiency in tools like Python and TensorFlow can reach salaries of $500,000 or more, especially in high-cost-of-living areas or at leading tech companies. Achieving this level often requires a strong track record, specialized knowledge in audio processing, and sometimes equity or bonuses as part of compensation packages.
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:
Infographic showing various Audio Machine Learning job openings in Texas as of June 2026, with employment types broken down into 71% Full Time, 17% Part Time, 8% Temporary, and 4% Contract. Highlights an 88% Physical, 1% Hybrid, and 11% Remote job distribution, with an average salary of $78,683 per year, or $37.8 per hour.

Senior Machine Learning Engineer

G2 Venture Partners

Austin, TX

$103K - $142K/yr

Other

Medical, Dental, Vision, Retirement

Posted 4 days ago


Job description

Senior Machine Learning Engineer

We are seeking a Senior Machine Learning Engineer to support our Public Sector initiatives focused on building and optimizing production ready AI systems for secure and distributed environments.

You will be responsible for transforming prototype models into scalable, efficient, and reliable production systems that operate seamlessly across a spectrum of hardware from government cloud infrastructure to edge devices in restricted or disconnected environments.

Responsibilities:

  • Design, develop, and deploy agentic workflows to orchestrate multi-step reasoning, tool use, and decision-making across production systems.
  • Productionize AI models from research prototypes into scalable, deployable systems used in real world applications.
  • Engineer adaptive ML systems using LoRA, PEFT, and on-device inference strategies, leveraging PyTorch, TensorFlow, and Hugging Face Transformers for model development, fine-tuning, and optimization.
  • Implement model optimization techniques such as quantization, pruning, distillation, and hardware specific acceleration.
  • Build and maintain Retrieval Augmented Generation (RAG) pipelines, including vector database integration for contextual retrieval.
  • Work with multi-modal AI systems across computer vision, audio, and natural language domains.
  • Optimize model execution for distributed and resource constrained environments, ensuring reliability under variable connectivity conditions.

Qualifications:

  • Active US Security clearance
  • 4+ years of experience in applied AI, ML engineering, or production AI systems.
  • Deep proficiency in PyTorch, TensorFlow, or Hugging Face Transformers.
  • Proven experience deploying AI models across cloud, edge, and mobile hardware environments.
  • Expertise in model compression and optimization (quantization, pruning, distillation).
  • Experience building RAG pipelines and integrating vector databases (e.g., Quadrant, ChromaDB, FAISS, Milvus, Pinecone).
  • Familiarity with multi-modal models and synthetic data generation methods.
  • Strong algorithmic and problem solving skills, especially in distributed or constrained compute environments.

Preferred Skills:

  • Experience with edge AI, federated learning, or offline inference systems.
  • Understanding of AI governance and compliance frameworks relevant to public sector deployments.
  • Experience integrating models into large scale distributed systems or microservice architectures.
  • Excellent communication and technical documentation skills for collaboration across multi disciplinary teams.
  • Strong understanding of GPU computing, CUDA, and performance profiling.

We at webAI are committed to living out the core values we have put in place as the foundation on which we operate as a team. We seek individuals who exemplify the following:

  • Truth - Emphasizing transparency and honesty in every interaction and decision.
  • Ownership - Taking full responsibility for one's actions and decisions, demonstrating commitment to the success of our clients.
  • Tenacity - Persisting in the face of challenges and setbacks, continually striving for excellence and improvement.
  • Humility - Maintaining a respectful and learning-oriented mindset, acknowledging the strengths and contributions of others.

Benefits:

  • Competitive salary
  • Comprehensive health, dental, and vision benefits package
  • 401(k) match (U.S.-based employees only)
  • $200/month Health & Wellness stipend
  • Continuing Education support
  • $500/year Function Health subscription (U.S.-based employees only)
  • Free parking for in-office employees
  • Flexible Time Off (FTO)
  • Parental leave for eligible employees
  • Supplemental life insurance

webAI is an Equal Opportunity Employer and does not discriminate against any employee or applicant on the basis of age, ancestry, color, family or medical care leave, gender identity or expression, genetic information, marital status, medical condition, national origin, physical or mental disability, protected veteran status, race, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable laws, regulations and ordinances. We adhere to these principles in all aspects of employment, including recruitment, hiring, training, compensation, promotion, benefits, social and recreational programs, and discipline. In addition, it is the policy of webAI to provide reasonable accommodation to qualified employees who have protected disabilities to the extent required by applicable laws, regulations and ordinances where a particular employee works.