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Full Time Audio Machine Learning Jobs (NOW HIRING)

FieldAI

3D Machine Learning Engineer

Irvine, CA · On-site

As a 3D Machine Learning Engineer , you will focus on designing, implementing, training, and ... Analyze diverse sensor inputs, including RGBD imagery, LiDAR point clouds, 360 photos, audio, and ...

The New York Times

Machine Learning Scientist

New York, NY · On-site

$121K - $131K/yr

It's why we focus deeply on how our readers will experience our journalism, from print to audio to ... We are a group of machine learning scientists and data analysts that partner with teams across The ...

PROPRIUS

Senior Machine Learning Engineer

New York, NY

$114.30K - $157K/yr

Sr. Machine Learning Engineer Location: New York, NY Sponsorship: Yes Relocation: Yes Industry ... audio cues. When successful, your research will be deployed into millions of vehicles worldwide.

Roku

Software Engineer, Machine Learning

San Jose, CA · On-site

$135K - $185K/yr

You will explore Picture Quality (PQ) and Audio Quality (AQ) improvements using AI in a resource ... Hands-on experience with Machine Learning / Deep Learning frameworks like TensorFlow or PyTorch

GN Group

Machine Learning Engineer - Cloud

Dover, NH · On-site +1

$86K - $135K/yr

Machine Learning Engineer - Cloud *Please consider before applying: This is a hybrid role, and ... Fundamentals of audio and speech signal processing. Pay Transparency Notice * Depending on your ...

GN Group

Machine Learning Engineer - Cloud

Dover, NH · On-site

$86K - $135K/yr

Machine Learning Engineer - Cloud *Please consider before applying: This is a hybrid role, and ... Fundamentals of audio and speech signal processing. Pay Transparency Notice * Depending on your ...

ManTech International

Machine Learning Engineer

$128.80K - $214.50K/yr

... Type Full time Description & Requirements Elevate your career with MANTECH International ... The Machine Learning Engineer will leverage their strong technical background and knowledge to ...

GN Group

Machine Learning Engineer - Cloud

Lowell, MA · On-site +1

$86K - $135K/yr

Machine Learning Engineer - Cloud *Please consider before applying: This is a hybrid role, and ... Fundamentals of audio and speech signal processing. Pay Transparency Notice * Depending on your ...

FieldAI

3D Machine Learning Engineer

Irvine, CA

What You'll Do * Design and implement scalable machine learning pipelines for large-scale 3D ... Analyze diverse sensor inputs, including RGBD imagery, LiDAR point clouds, 360 photos, audio, and ...

IMC

Machine Learning Engineer

Chicago, IL · On-site

$175K - $250K/yr

As a Machine Learning Engineer, you will play a pivotal role in building systems that drive the ... Base salary is only one component of total compensation; all full-time, permanent positions are ...

Netflix

OR

$523K - $920K/yr

Lead a broad portfolio of end-to-end initiatives in multimodal LLM and audio algorithms to achieve ... Full-time hourly employees accrue 35 days annually for paid time off to be used for vacation ...

Sonsoft Inc

C# / Machine Learning

Redmond, WA

Machine Learning, Deep Learning/neural networks. * Data mining. * Azure ML, Cortana Intelligence ... This is a Full-Time & Permanent job opportunity for you. 2.Only US Citizen, Green Card Holder, GC ...

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All JobsFull Time Audio Machine Learning Jobs

Full Time Audio Machine Learning information

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$29.5K

$84.5K

$171.5K

How much do full time audio machine learning jobs pay per year?

As of May 29, 2026, the average yearly pay for full time audio machine learning in the United States is $84,456.00, according to ZipRecruiter salary data. Most workers in this role earn between $50,000.00 and $113,000.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Full Time Audio Machine Learning Engineer, and why are they important?

To thrive as a Full Time Audio Machine Learning Engineer, you need a solid background in machine learning, digital signal processing, and proficiency in programming languages like Python or C++, often supported by a degree in computer science, engineering, or a related field. Experience with frameworks such as TensorFlow or PyTorch, and familiarity with audio analysis libraries and cloud computing platforms, are typically required. Strong analytical thinking, collaboration, and problem-solving skills help you tackle complex audio data challenges and work effectively in multidisciplinary teams. These competencies are crucial for developing, optimizing, and deploying robust audio ML solutions that drive innovation in areas like speech recognition, music analysis, and sound classification.

What are some common challenges faced by professionals in full-time audio machine learning roles, and how can they be addressed?

Professionals in full-time audio machine learning roles often face challenges such as dealing with noisy or unbalanced datasets, managing high computational requirements for model training, and ensuring real-time processing capabilities. Overcoming these challenges typically involves applying advanced data augmentation techniques, leveraging specialized hardware (like GPUs), and optimizing models for efficiency. Collaboration with data engineers and domain experts is also crucial to refine data pipelines and validate model outputs. Staying updated with the latest research and open-source tools can further enhance problem-solving in this rapidly evolving field.

What is a Full Time Audio Machine Learning job?

A Full Time Audio Machine Learning job involves developing and applying machine learning algorithms to process and analyze audio data, such as music, speech, or environmental sounds. Professionals in this role work on tasks like audio classification, speech recognition, sound synthesis, and noise reduction. They often collaborate with data scientists, audio engineers, and software developers to build AI-driven applications for industries like entertainment, healthcare, and virtual assistants. This position typically requires strong programming skills, experience with machine learning frameworks, and a background in audio signal processing.

What is the difference between Full Time Audio Machine Learning vs Audio Data Scientist?

AspectFull Time Audio Machine LearningAudio Data Scientist
Required CredentialsDegree in Computer Science, Electrical Engineering, or related field; experience in machine learning and audio processingSimilar credentials; strong background in data science, statistics, and audio analysis
Work EnvironmentResearch labs, tech companies, startups focusing on audio applicationsData-driven teams, analytics departments, R&D units in tech or entertainment industries
Employer & Industry UsageTech firms developing speech recognition, audio enhancement, or sound classificationCompanies analyzing audio data for insights, product development, or quality control

Both roles require expertise in audio processing and machine learning, often sharing similar educational backgrounds. Full Time Audio Machine Learning specialists focus on developing models and algorithms, while Audio Data Scientists analyze audio data to extract insights. The roles are closely related and often overlap, but the former emphasizes model development, whereas the latter emphasizes data analysis and interpretation.

More about Full Time Audio Machine Learning jobs
What are the most commonly searched types of Audio Machine Learning jobs? The most popular types of Audio Machine Learning jobs are:
What job categories do people searching Full Time Audio Machine Learning jobs look for? The top searched job categories for Full Time Audio Machine Learning jobs are:
Full Time Audio Machine Learning jobs near you
Infographic showing various Full Time Audio Machine Learning job openings in the United States as of May 2026, with employment types broken down into 1% Internship, 17% Full Time, 79% Part Time, and 3% Temporary. Highlights an 68% Physical, 1% Hybrid, and 31% Remote job distribution, with an average salary of $84,456 per year, or $40.6 per hour.

3D Machine Learning Engineer

FieldAI

Irvine, CA • On-site

Full-time

Posted 17 days ago

Job description

FieldAI's Irvine team is where embodied AI meets real robots, real sensors, and real field deployments. Based in the heart of Southern California's robotics ecosystem, we build risk-aware, reliable, field-ready AI systems that solve the hardest problems in robotics and unlock the full potential of embodied intelligence. If you want your work to ship, get tested on hardware, and improve through real deployments, Irvine is the place. We go beyond typical data-driven approaches or pure transformer-only architectures, combining rigorous engineering with learning systems proven in globally deployed solutions that deliver results today and get better every time our robots run in the field.
As a 3D Machine Learning Engineer, you will focus on designing, implementing, training, and maintaining cutting-edge 3D and multimodal machine learning models that process reality capture data such as 3D point clouds, 360 photos, and RGBD images. Your work will directly contribute to automated progress tracking, deviation analysis, and semantic scene understanding of construction sites. You will collaborate closely with software, autonomy, and product teams to ensure seamless integration of these AI models into our production environments.
What You'll Do
  • Design and implement scalable machine learning pipelines for large-scale 3D spatial data processing for point cloud analysis, object detection, segmentation, and scene understanding.
  • Train, optimize, and deploy deep learning models using PyTorch, TensorFlow, or equivalent frameworks on cloud platforms such as AWS (e.g., SageMaker, EC2).
  • Collaborate with software and systems engineers to integrate models into production environments and continuously improve inference pipelines.
  • Analyze diverse sensor inputs, including RGBD imagery, LiDAR point clouds, 360 photos, audio, and Building Information Models (BIM).
  • Work closely with the labeling and data operations teams to define robust data annotation strategies and ensure high model performance and generalization.

What You Have
  • Bachelor's or Master's degree in Computer Science, Machine Learning, Robotics, or a related technical field.
  • 2+ years of hands-on industry experience developing and deploying machine learning systems for 3D point clouds, perception, or spatial understanding tasks.
  • Strong background in 3D machine learning, with experience in deep learning for point clouds, multi-view fusion, or geometric learning.
  • Strong expertise in Python and deep learning frameworks: PyTorch, TensorFlow, or similar.
  • Familiarity with OpenCV and PCL (Point Cloud Library) for classical computer vision and 3D data preprocessing.
  • Experience training, evaluating, and deploying ML models using cloud infrastructure (e.g., AWS, SageMaker) and containerized workflows.
  • Solid understanding of the end-to-end ML lifecycle, including experiment tracking, reproducibility, model versioning, and optimization for production.
  • Proven ability to work in fast-paced, interdisciplinary teams across software, ML, and product teams.

The Extras That Set You Apart
  • Experience working with BIM data, digital twins, or construction-related sensor data.
  • Background in geometric deep learning, 3D mesh analysis, GIS systems, or structured scene representations.
  • Familiar with MLOps pipelines using Ray, SageMaker, MLflow, or Kubeflow.
  • Strong foundation in geometric computer vision, robotics, or algorithmic 3D reasoning.
  • Exposure to graph neural networks, geodesic computations, or neural implicit representations (e.g., NeRF, Occupancy Networks).
  • Deep experience with point cloud and graph learning frameworks such as Open3D-ML, Torch-Points3D, PyG, or MMDetection3D.
  • Experience building custom modules for SparseConvNet or 3D transformers.

Our salary range is generous and we consider each individual's background and experience when determining final compensation. Base pay may vary based on role scope, job-related knowledge, skills, experience, and the Irvine, California market.
Why Join FieldAI in Irvine?
In Irvine, you will work where the robots are. Our local team builds and tests systems on real hardware with real sensors, then ships them to operate in unstructured, previously unknown environments around the world. We are solving one of robotics' hardest challenges: reliable deployment outside the lab. Our Field Foundational Models™ raise the bar for perception, planning, localization, and manipulation, with an emphasis on explainability and safety for real-world use.
You will collaborate with a world-class team that thrives on creativity, resilience, and bold thinking. We bring deep experience from organizations such as DeepMind, NASA JPL, Boston Dynamics, NVIDIA, Amazon, Tesla Autopilot, Cruise, Zoox, Toyota Research Institute, and SpaceX, along with a track record of field deployments and strong performance in DARPA challenge segments.
Be Part of the Next Robotics Revolution
We are looking for builders who want their work to leave the whiteboard and show up on robots. If you enjoy tackling tough, uncharted questions and working across disciplines, you will find your people here. Our teams span AI, software, robotics engineering, product, field deployment, and technical communication, all focused on shipping systems that perform in the real world.
Our headquarters is in Irvine, and we partner closely with teams there as well as colleagues across the US and around the world. Join us in Southern California and help define what dependable, field-ready autonomy looks like.
We value diverse perspectives and are committed to fostering an inclusive workplace. We evaluate candidates and employees based on merit, qualifications, and performance, and we do not discriminate on the basis of race, color, gender, national origin, ethnicity, veteran status, disability status, age, sexual orientation, gender identity, marital status, or any other legally protected statu
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.

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