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Remote Deep Learning Engineer Jobs in California

DocuSign

Machine Learning Engineer

San Francisco, CA · On-site +1

$164K - $266K/yr

Employee divides their time between in-office and remote work. Access to an office location is ... Deep understanding of the ML lifecycle, from data ingestion and training to production monitoring

PhysicsX

Machine Learning Engineer

San Francisco, CA · On-site +1

Experience applying Machine learning methods (including 3D graph/point cloud deep learning methods) to real-world engineering applications, with a focus on driving measurable impact in industry ...

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All JobsDeep Learning Engineer JobsRemote Deep Learning Engineer JobsRemote Deep Learning Engineer Jobs in California

Remote Deep Learning Engineer information

How do Remote Deep Learning Engineers typically collaborate with cross-functional teams despite working remotely?

Remote Deep Learning Engineers frequently collaborate with data scientists, product managers, and software engineers using digital tools such as Slack, Zoom, and collaborative code platforms like GitHub. Regular virtual meetings and sprint planning sessions help ensure alignment on project goals and milestones. Clear documentation and asynchronous communication are crucial for effective teamwork, especially when team members are in different time zones. This collaborative structure enables remote engineers to contribute meaningfully to model development, deployment, and integration while maintaining flexibility.

What are the key skills and qualifications needed to thrive as a Remote Deep Learning Engineer, and why are they important?

To thrive as a Remote Deep Learning Engineer, you need a strong background in machine learning, deep learning frameworks, and programming languages like Python, usually supported by a degree in computer science or a related field. Familiarity with tools such as TensorFlow, PyTorch, cloud platforms (e.g., AWS, GCP), and version control systems is typically required, with certifications in AI or cloud technologies being advantageous. Excellent problem-solving, communication, and self-management skills make candidates stand out in remote environments. These skills and qualities are essential for developing effective AI solutions, collaborating across distributed teams, and driving innovation in the fast-evolving field of deep learning.

What is the difference between Remote Deep Learning Engineer vs Remote Machine Learning Engineer?

AspectRemote Deep Learning EngineerRemote Machine Learning Engineer
Required CredentialsBachelor's/Master's in CS, AI, or related; experience with deep learning frameworksBachelor's/Master's in CS, Data Science, or related; experience with ML algorithms
Work EnvironmentResearch and development, model training, neural network designData analysis, model deployment, algorithm development
Employer & Industry UsageTech companies, AI startups, research institutionsTech firms, finance, healthcare, e-commerce

Remote Deep Learning Engineers focus on designing and training neural networks for complex AI tasks, while Remote Machine Learning Engineers work on broader ML models and algorithms. Both roles require strong programming skills and knowledge of machine learning frameworks, but Deep Learning Engineers specialize in neural networks and large-scale data processing.

What is a Remote Deep Learning Engineer?

A Remote Deep Learning Engineer is a professional who works primarily online to design, develop, and implement deep learning models and algorithms. These engineers use neural networks and large datasets to solve complex problems in fields like computer vision, natural language processing, and more. Working remotely, they collaborate with team members via digital tools, write code, optimize models, and often deploy solutions to cloud environments. This role requires strong programming skills, experience with deep learning frameworks (like TensorFlow or PyTorch), and the ability to work independently in a distributed team setting.
What are the most commonly searched types of Deep Learning Engineer jobs in California? The most popular types of Deep Learning Engineer jobs in California are:
What are popular job titles related to Remote Deep Learning Engineer jobs in California? For Remote Deep Learning Engineer jobs in California, the most frequently searched job titles are:
What job categories do people searching Remote Deep Learning Engineer jobs in California look for? The top searched job categories for Remote Deep Learning Engineer jobs in California are:
What cities in California are hiring for Remote Deep Learning Engineer jobs? Cities in California with the most Remote Deep Learning Engineer job openings:
Remote Deep Learning Engineer jobs near you
Infographic showing various Remote Deep Learning Engineer job openings in California as of July 2026, with employment types broken down into 74% Full Time, 23% Part Time, 1% Temporary, and 2% Contract. Highlights an 72% Physical, 2% Hybrid, and 26% Remote job distribution.
Deep Learning Scientist, Speech Synthesis

Deep Learning Scientist, Speech Synthesis

Catapult Solutions Group

Santa Clara, CA • On-site, Remote

Contractor

Posted 14 days ago

Job description

Deep Learning Scientist - Speech Synthesis
Location: 100% Remote (Anywhere in the U.S.)
Duration: 6-Month Contract
Position Overview
We are seeking a Deep Learning Scientist - Speech Synthesis to support the development of next-generation speech AI technologies. This role focuses on training and optimizing speech models, improving model performance, and solving complex machine learning challenges related to speech applications.
The ideal candidate has strong experience in speech synthesis (Text-to-Speech) or Speech-to-Text, deep learning, and Python development. Success in this role requires the ability to analyze model behavior, diagnose training issues, and improve model performance-not just collect or evaluate data.
Key Responsibilities
  • Train and optimize speech synthesis models, including mel spectrogram and vocoder models.
  • Analyze training metrics, validation losses, and model performance to identify root causes of model issues and recommend improvements.
  • Benchmark and optimize speech models across multiple use cases.
  • Improve speech data preparation, augmentation, filtering, and dataset quality.
  • Develop and refine high-quality training datasets for speech AI models.
  • Measure and characterize model accuracy, quality, and bias.
  • Collaborate with cross-functional teams to develop and deliver new speech AI features.
  • Participate in software development, design reviews, testing, and code reviews.
  • Troubleshoot technical issues and contribute to continuous model improvements.
Required Qualifications
  • Master's degree or Ph.D. in Computer Science, Electrical Engineering, Artificial Intelligence, Applied Mathematics, Linguistics, Computational Linguistics, or a related field (or equivalent experience).
  • 3+ years of relevant industry experience.
  • Strong Python programming skills.
  • Strong understanding of machine learning and deep learning concepts.
  • Experience with Text-to-Speech (TTS), Speech Synthesis, or Speech-to-Text (STT) technologies.
  • Hands-on experience training deep learning models using PyTorch.
  • Ability to analyze training behavior, validation losses, and model performance to troubleshoot and improve machine learning models.
  • Knowledge of speech signal processing concepts, including FFT, MFCC, and mel spectrograms.
  • Strong understanding of software development fundamentals.
  • Experience using version control systems such as Git, Gerrit, or GitLab.
  • Excellent communication and collaboration skills.
Preferred Qualifications
  • Experience with deep learning architectures such as CNNs, RNNs, LSTMs, and Transformers.
  • Experience with voice cloning or multilingual speech systems.
  • Knowledge of text normalization (TN), inverse text normalization (ITN), or grapheme-to-phoneme (G2P) systems.
  • Fluency in one or more languages such as Spanish, Mandarin, German, Japanese, Russian, French, Arabic, Hindi, Korean, Italian, or Portuguese.
  • Interest in linguistics, phonetics, and speech technologies.
  • Strong C++ programming skills.
  • Familiarity with GPU technologies such as CUDA, cuDNN, or TensorRT.
  • Experience deploying machine learning models to cloud, data center, or embedded environments.
What We're Looking For
The ideal candidate is someone who enjoys solving difficult machine learning problems and has hands-on experience training speech models. Beyond building models, we're looking for someone who can investigate why a model is underperforming, analyze validation losses, identify root causes, and improve overall model quality and performance.
Additional Information
  • 100% remote position within the United States.
  • No specific U.S. time zone requirement.
  • This is a contract opportunity.
  • Opportunity to contribute to cutting-edge speech AI and deep learning technologies.

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