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Remote Deep Learning Engineer Jobs in Santa Clara, CA

Machine Learning Engineer

Santa Clara, CA ยท On-site +1

$184K - $253K/yr

We're looking for a seasoned machine learning engineer with a deep ML foundation who has actively kept pace with the field-someone equally comfortable with classical ML and the latest generative ...

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Remote Deep Learning Engineer information

See Santa Clara, CA salary details

$12.9K

$98.5K

$164.4K

How much do remote deep learning engineer jobs pay per year?

As of Aug 9, 2026, the average yearly pay for remote deep learning engineer in Santa Clara, CA is $98,518.00, according to ZipRecruiter salary data. Most workers in this role earn between $84,600.00 and $163,200.00 per year, depending on experience, location, and employer.

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?

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 Santa Clara, CA? The most popular types of Deep Learning Engineer jobs in Santa Clara, CA are:
What are popular job titles related to Remote Deep Learning Engineer jobs in Santa Clara, CA? For Remote Deep Learning Engineer jobs in Santa Clara, CA, the most frequently searched job titles are:
What job categories do people searching Remote Deep Learning Engineer jobs in Santa Clara, CA look for? The top searched job categories for Remote Deep Learning Engineer jobs in Santa Clara, CA are:
What cities near Santa Clara, CA are hiring for Remote Deep Learning Engineer jobs? Cities near Santa Clara, CA with the most Remote Deep Learning Engineer job openings:

Lead Machine Learning Engineer - Remote (US) or CA - Only W2

Saransh Inc

Mountain View, CA โ€ข Remote

$104K - $138K/yr

Contractor

Re-posted 22 days ago


Job description

Role: Lead Machine Learning Engineer
Location: Mountain View, CA (3 days a week onsite) (OR) Remote
Job Type: W2 Contract
Duration: 12 months
 
 
Experience: Senior/Lead Level
 
Short Overview of JD:
Looking for a ML Engineer who will be working on the products related to seismic and well log data, identifying simple geologic characteristics of the data (faults, horizons), and working knowledge of the different subsurface data formats and types.
 
Primary Skills:
MLOps, Deep learning, GPU training and inference, Image models, GCP, TensorFlow, PyTorch, Agentic coding tools
 
We are looking for a candidate who:
  • Senior-level experience leading small engineering teams, setting technical goals in a business context, and remaining hands-on.
  • Familiarity with agentic coding tools (e.g., Claude).
  • Is well-versed in deep learning, GPU training and inference, and image models.
  • Has extensive experience in model training and setting up distributed model training pipelines, especially using platforms like Vertex AI and Kubeflow for large-scale image and language model training.
  • Possesses a strong background in building and deploying machine learning models, with a focus on image processing and time series signal processing.
  • Has hands-on experience in training and fine-tuning ML models.
  • Is skilled in building and maintaining data pipelines for image and sensor data.
  • Is familiar with ML Ops tools and practices, including model monitoring, versioning, and deployment.
  • Has experience working with data labeling tools.
  • Is comfortable with cloud platforms, particularly Google Cloud Platform (GCP); experience with edge deployments is a plus.
Additional (Nice to Have) Skills:
  • Experience with GCP is highly desirable; if not, the ability and willingness to learn quickly is expected.
  • Proficiency in TensorFlow and PyTorch.
  • Familiarity with Protocol Buffers and containerization technologies.
  • Experience with rapid prototyping to validate hypotheses.