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Audio Speech Machine Learning Jobs in Seattle, WA

Machine Learning Manager

Seattle, WA · On-site

$180K - $250K/yr

Knowledgeable in at least one focus area of machine learning, such as computer vision, audio, or NLP * 2+ years experience managing machine learning teams * You have an ability to understand and make ...

Knowledgeable in at least one focus area of machine learning, such as computer vision, audio, or NLP * 2+ years experience managing machine learning teams * You have an ability to understand and make ...

Experience with psycho-acoustic metrics for speech quality and intelligibility * Experience ... Familiarity with audio algorithm development and/or machine learning techniques * 4+ years of ...

You have successfully trained and deployed a deep learning machine model (image, NLP, video, or audio) into production, with measurably improved performance over baseline, either in industry or as a ...

Machine Learning Engineer

Seattle, WA · On-site

$120K - $180K/yr

You have successfully trained and deployed a deep learning machine model (image, NLP, video, or audio) into production, with measurably improved performance over baseline, either in industry or as a ...

Machine Learning Engineer, SIML

Seattle, WA · On-site

$139.50 - $258.10/hr

Seattle, Washington, United States Machine Learning and AI Do you believe that generative models ... audio/multimodal foundation models. You will stay at the forefront of the latest AI research to ...

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Audio Speech Machine Learning information

What are some common challenges faced when developing machine learning models for audio speech applications?

A key challenge in audio speech machine learning roles is dealing with diverse and noisy audio data, which can significantly affect model accuracy. Additionally, models must be robust to different accents, languages, and speaking styles, requiring large and varied datasets for training and validation. Collaboration with data engineers, linguists, and software developers is often necessary to ensure high-quality data pipelines and model integration into production systems. Staying updated with the latest research and optimizing models for real-time performance are also ongoing aspects of the role.

What is an audio speech machine learning engineer?

An Audio Speech Machine Learning Engineer is a specialized professional who designs, develops, and implements machine learning models that process and analyze audio and speech data. Their work involves tasks like speech recognition, speaker identification, and audio event detection by leveraging algorithms and large datasets. These engineers collaborate with data scientists, software developers, and linguists to create applications such as voice assistants, transcription tools, and automated customer service systems. Expertise in signal processing, deep learning frameworks, and programming languages like Python is crucial for this role.

What is the difference between Audio Speech Machine Learning vs Speech Data Analyst?

AspectAudio Speech Machine LearningSpeech Data Analyst
Required CredentialsDegree in Computer Science, Data Science, or related fields; knowledge of ML frameworksDegree in Data Analysis, Statistics, or related fields; experience with data tools
Work EnvironmentResearch labs, tech companies, AI startupsData analysis teams, research institutions, tech firms
Industry UsageDeveloping speech recognition, voice assistants, NLP applicationsAnalyzing speech datasets, improving speech models, reporting insights

Audio Speech Machine Learning focuses on developing algorithms for speech recognition and processing, often involving model training and AI development. Speech Data Analysts interpret speech data, generate insights, and support model improvements. Both roles require strong analytical skills, but their core tasks differ: one builds models, the other analyzes data.

What are the key skills and qualifications needed to thrive as an audio speech machine learning engineer, and why are they important?

To thrive as an Audio Speech Machine Learning Engineer, you need a solid background in machine learning, signal processing, and programming (typically Python), along with a relevant degree in computer science or a related field. Familiarity with tools like TensorFlow or PyTorch, audio processing libraries (such as Librosa), and experience with speech datasets and ASR systems are commonly required. Critical soft skills include problem-solving, innovation, and effective communication for collaborating with cross-functional teams. These skills are essential to develop accurate, scalable speech recognition systems that advance voice-driven technology.
What are popular job titles related to Audio Speech Machine Learning jobs in Seattle, WA? For Audio Speech Machine Learning jobs in Seattle, WA, the most frequently searched job titles are:
What job categories do people searching Audio Speech Machine Learning jobs in Seattle, WA look for? The top searched job categories for Audio Speech Machine Learning jobs in Seattle, WA are:
Infographic showing various Audio Speech Machine Learning job openings in Seattle, WA as of August 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution.

AIML - Machine Learning Engineer - Computer Vision & Audio, MIND

Apple Inc.

Seattle, WA • On-site

$142.30 - $263.30/hr

Other

Medical, Dental, Retirement

Re-posted 22 days ago


Apple rating

8.0

Company rating: 8.0 out of 10

Based on 677 frontline employees who took The Breakroom Quiz

7th of 30 rated technology retailers


Job description

AIML - Machine Learning Engineer - Computer Vision & Audio, MIND

Seattle, Washington, United States Machine Learning and AI

The Machine Intelligence, Neural Design (MIND) team, part of Apple’s AIML organization, is leading Apple-wide innovation on HW/SW co-design for efficient inference. With roots in ML, computer vision, and energy efficiency research, our team is strategically positioned to contribute to diverse initiatives ranging from shipping features in well-known Apple products to ambitious, long‑term research projects.

We are seeking a hands‑on Machine Learning Engineer to drive the data & evaluation lifecycle for our production models. In this role, you will focus on designing and scaling high‑performance data processing pipelines, ensuring data quality, performing in-depth failure analysis on production models, and implementing advanced data augmentation techniques to boost model performance. This includes but is not limited to crafting creative techniques to analyze audio & video datasets, designing metrics to understand user behavior & evaluate performance of machine learning models. You will innovate across the entire end‑to‑end ML production pipeline, bridging the gap between hardware, software, and modeling, ensuring our ML systems are robust, efficient, and scalable.

Description

We are seeking a Machine Learning Engineer to design and deliver innovative features and models that advance our ML systems. In this role, you will scale model evaluation workflows, build robust data pipelines, and optimize performance across the stack.

  • Pipeline Scaling & Optimization: Design, build, and maintain scalable ETL/ELT data pipelines using tools like Spark and Airflow to handle large‑scale datasets. Optimize existing pipelines for efficiency, latency, and cost.
  • Data Augmentation & Synthesis: Research and implement advanced data augmentation techniques (e.g., GANs, semantic augmentation, synthetic data generation) to address data scarcity and imbalanced datasets.
  • Data Quality & Monitoring: Implement data observability and automated data validation checks to identify data drift, schema violations, and outliers in real‑time.
  • Failure Analysis & Debugging: Perform root‑cause analysis on production model failures, diagnosing issues between data inputs and model outputs using advanced statistical methods.
  • Model Evaluation: Collaborate with other machine learning engineers to productize models, implementing robust evaluation frameworks, including experimentation and performance monitoring.
Minimum Qualifications
  • Proficiency in working with unstructured data, specifically video & audio signals, for object detection, pattern recognition, feature extraction and segmentation.
  • Proficiency with Python and deep learning frameworks like PyTorch.
  • Expertise in designing metrics, and conducting metric change & performance analysis for model evaluation.
  • Strong problem‑solving skills in analyzing complex, ambiguous problems and clearly presenting sophisticated technical concepts to both expert and non‑expert audiences.
  • Master’s degree or equivalent experience in a technical or quantitative field.
Preferred Qualifications
  • Experience with shipping ML features and products
  • Strong verbal and written communications skills with demonstrated experience in authoring & presenting analytical insights via papers & presentations.
  • Self‑motivated and curious with creative and critical thinking capabilities and drive to figure out and improve how things work.
  • High tolerance for ambiguity. You find a way through. You anticipate. You connect and synthesize.
  • Experience with large scale training ML models including deep learning based models.
  • Experience with GPU‑based distributed training & evaluation.
  • Background in Computer Vision (image augmentation), Audio and Natural Language Processing.
Compensation & Benefits

At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $142,300 and $263,300, and your base pay will depend on your skills, qualifications, experience, and location.

Apple employees also have the opportunity to become an Apple shareholder through participation in Apple’s discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple’s Employee Stock Purchase Plan. You’ll also receive benefits including comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses — including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation.

Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.

Equal Opportunity & Accessibility

Apple is an equal opportunity employer that is committed to inclusion and diversity. We seek to promote equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics. Learn more about your EEO rights as an applicant.

At Apple, we believe accessibility is a fundamental human right. You’ll find that idea reflected in everything here — in our culture, our benefits and our digital tools. By welcoming as many perspectives as possible, we help you build a career where you feel like you belong.

Apple accepts applications to this posting on an ongoing basis.

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About Apple

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Imagine what you could do here! At Apple, new ideas have a way of becoming extraordinary products, services, and customer experiences very quickly. Bring passion and dedication to your job and there's no telling what you could accomplish. Dynamic, intelligent people and inspiring, innovative technologies are the norm here. The people who work here have reinvented entire industries with all Apple Hardware products. The same real passion for innovation that goes into our products also applies to our practices strengthening our dedication to leave the world better than we found it.

Industry

Computer and electronic product manufacturing

Company size

10,000+ Employees

Headquarters location

Cupertino, CA, US

Year founded

1976