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Remote Deep Learning Jobs in California (NOW HIRING)

Lynqex Labs

Senior AI Engineer

San Francisco, CA · On-site +1

$150K - $250K/yr

... neural networks, and deep learning algorithms that power our next-generation products ... Flexible work hours and remote work options * Top‑tier equipment and home office setup

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Technical Cofounder

San Diego, CA · On-site +1

Flexible (Office and Remote)* No salary* Not specified* Senior (6-10 years)* Post-Secondary Diploma ... Strong background in deep learning, computer vision, modeling and or supervised learning.

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

Remote Deep Learning information

See California salary details

$21.1K

$128.3K

$209.6K

How much do remote deep learning jobs pay per year?

As of Jul 10, 2026, the average yearly pay for remote deep learning in California is $128,275.00, according to ZipRecruiter salary data. Most workers in this role earn between $85,192.00 and $165,979.00 per year, depending on experience, location, and employer.

What is a Remote Deep Learning job?

A Remote Deep Learning job involves working with artificial intelligence and machine learning models, particularly using deep neural networks, from a location outside a traditional office, often from home. Professionals in this field design, build, and optimize algorithms that enable computers to learn from large amounts of data. They often work on projects such as image and speech recognition, natural language processing, or autonomous systems. The remote aspect allows flexibility and access to global opportunities, but requires strong communication skills and the ability to collaborate virtually with teams.

What are some common challenges faced by remote deep learning engineers, and how can they be addressed?

Remote deep learning engineers often encounter challenges such as limited access to high-performance computing resources, communication barriers with distributed teams, and difficulties in collaborating on large codebases or datasets. These issues can be mitigated by leveraging cloud-based platforms for scalable computing, using clear communication tools like Slack or Zoom for regular check-ins, and employing version control systems like Git for collaborative code management. Proactively setting up workflows and documentation helps ensure smooth collaboration and project continuity within a remote environment.

How can I make $100,000 a year working from home?

A remote deep learning professional can reach a $100,000 annual income by gaining advanced skills in machine learning frameworks, building a strong portfolio, and working for companies that offer competitive salaries or freelance projects. Earning this level often requires experience, specialized knowledge, and the ability to deliver high-quality models efficiently. Certifications in deep learning and proficiency with tools like Python, TensorFlow, or PyTorch can also enhance earning potential.

What is a $900000 AI job?

A $900,000 AI job typically refers to a high-level position in artificial intelligence, such as a senior machine learning engineer, AI research director, or chief AI officer, often requiring advanced skills in deep learning, data science, and programming. These roles usually involve leadership responsibilities, extensive experience, and may include stock options or bonuses as part of compensation packages.

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

AspectRemote Deep LearningRemote Machine Learning Engineer
Required CredentialsBachelor's/Master's in CS, AI, or related; experience with neural networksBachelor's/Master's in CS, Data Science, or related; experience with algorithms and data modeling
Work EnvironmentCollaborative teams, research-focused, often in tech or AI companiesDevelopment teams, data-driven projects, across various industries
Employer & Industry UsageTech firms, AI startups, research institutionsTech companies, finance, healthcare, e-commerce

Remote Deep Learning specialists focus on designing and training neural networks for AI applications, often requiring advanced knowledge of deep neural architectures. Remote Machine Learning Engineers work on developing algorithms and models for broader data analysis and predictive tasks. While both roles involve machine learning, deep learning emphasizes neural networks, whereas machine learning engineers may work with a variety of algorithms across industries.

Which 3 jobs will survive AI?

In the field of remote deep learning, roles such as data scientists, machine learning engineers, and AI research scientists are likely to persist due to their reliance on complex problem-solving, domain expertise, and ongoing innovation. These jobs require advanced skills in programming, mathematics, and understanding of AI frameworks, making them less susceptible to automation by AI systems. Continuous learning and staying updated with new tools and techniques are essential for long-term career stability in this area.

How to make $1000 a week remotely?

Remote deep learning professionals can earn $1000 or more weekly by taking on freelance projects, consulting, or working for companies that pay competitive rates. Building a strong portfolio, acquiring relevant skills in Python and machine learning frameworks, and obtaining certifications can help increase earning potential. Consistent work and specialized expertise are key to reaching this income level remotely.

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 strong programming skills in Python, a deep understanding of machine learning algorithms, and typically a degree in computer science, engineering, or a related field. Proficiency with frameworks like TensorFlow or PyTorch, as well as cloud computing platforms such as AWS or Google Cloud, is essential, and certifications in these technologies can be advantageous. Excellent problem-solving abilities, self-motivation, and clear communication are crucial soft skills for remote collaboration and project delivery. These skills ensure effective development, deployment, and maintenance of deep learning models while working independently in distributed teams.
What are the most commonly searched types of Deep Learning jobs in California? The most popular types of Deep Learning jobs in California are:
What are popular job titles related to Remote Deep Learning jobs in California? For Remote Deep Learning jobs in California, the most frequently searched job titles are:
What job categories do people searching Remote Deep Learning jobs in California look for? The top searched job categories for Remote Deep Learning jobs in California are:
What cities in California are hiring for Remote Deep Learning jobs? Cities in California with the most Remote Deep Learning job openings:
Remote Deep Learning jobs near you
Infographic showing various Remote Deep Learning 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, with an average salary of $128,275 per year, or $61.7 per hour.
Deep Learning Scientist, Speech Synthesis

Deep Learning Scientist, Speech Synthesis

Catapult Solutions Group

Santa Clara, CA • On-site, Remote

Contractor

Posted 15 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.

Apply