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Pytorch Developer Jobs in Santa Cruz, CA (NOW HIRING)

SpaceXAI

ML Infrastructure Engineer

Palo Alto, CA · On-site

$180K - $440K/yr

As an ML Infrastructure Engineer, you will play a pivotal role in building and optimizing the ... Deep familiarity with modern ML frameworks such as JAX or PyTorch * Low-level understanding of ...

Sonatus

Staff Machine Learning Engineer

San Jose, CA · Hybrid

Port and optimize PyTorch/TensorFlow models into production-grade models for execution on CPU/GPU ... Bachelor's degree in Computer Science, Electrical Engineering, Software Engineering, or a related ...

Sonatus

Senior Staff Machine Learning Engineer

San Jose, CA · Hybrid

$122K - $168K/yr

Port and optimize PyTorch/TensorFlow models into production-grade models for execution on CPU/GPU ... Bachelor's degree in Computer Science, Electrical Engineering, Software Engineering, or a related ...

CYNET SYSTEMS

IT - AI/ML Engineer 1

San Jose, CA · On-site

$70 - $75/hr

Skills: * Proficient in machine learning frameworks (such as TensorFlow or PyTorch) and libraries (like scikit-learn). * Strong coding skills in programming languages like Python, R, or Java.

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Pytorch Developer information

What is a PyTorch developer?

A PyTorch Developer is a software engineer or data scientist who specializes in using PyTorch, an open-source machine learning library, to build and deploy deep learning models. Their responsibilities typically include designing neural network architectures, training and evaluating models, and optimizing code for performance. PyTorch Developers work in fields such as artificial intelligence, computer vision, and natural language processing, collaborating with teams to solve complex problems using machine learning. They are proficient in Python and have a strong understanding of deep learning concepts. Additionally, they often contribute to research, development, and the deployment of AI solutions in production environments.

What are some common challenges PyTorch developers face when deploying machine learning models to production environments?

Pytorch Developers often encounter challenges when transitioning models from research to production, such as optimizing model performance for inference speed and memory usage, ensuring compatibility with deployment frameworks like TorchScript or ONNX, and managing dependencies across different systems. Additionally, integrating PyTorch models into existing software stacks and maintaining reproducibility can be complex. Collaborating closely with DevOps and data engineering teams is crucial to address these issues and ensure smooth deployment.

What are the key skills and qualifications needed to thrive as a PyTorch developer, and why are they important?

To thrive as a Pytorch Developer, you need strong programming skills in Python, a solid grasp of machine learning concepts, and experience with deep learning frameworks—especially PyTorch itself. Familiarity with tools like CUDA, Jupyter Notebooks, and version control systems (e.g., Git) is typically expected, along with knowledge of cloud platforms or relevant certifications. Problem-solving ability, effective collaboration, and clear communication are crucial soft skills for success in this role. These skills and qualities are vital for efficiently building, optimizing, and deploying machine learning models in real-world applications.

What is the difference between Pytorch Developer vs Machine Learning Engineer?

AspectPytorch DeveloperMachine Learning Engineer
Required CredentialsBachelor's or higher in CS, experience with PyTorchBachelor's or higher in CS, data science, or related field, with ML experience
Work EnvironmentResearch labs, AI startups, tech companies focusing on deep learningTech companies, finance, healthcare, often involving deployment and scaling ML models
Industry UsagePrimarily in AI research and development teamsAcross industries implementing ML solutions in production

While both roles require knowledge of machine learning and experience with PyTorch, a Pytorch Developer mainly focuses on developing and optimizing deep learning models using PyTorch. A Machine Learning Engineer often has a broader scope, including deploying, maintaining, and scaling ML models across various platforms and industries.

What are popular job titles related to Pytorch Developer jobs in Santa Cruz, CA?

For Pytorch Developer jobs in Santa Cruz, CA, the most frequently searched job titles are:

What job categories do people searching Pytorch Developer jobs in Santa Cruz, CA look for?

The top searched job categories for Pytorch Developer jobs in Santa Cruz, CA are:

What cities near Santa Cruz, CA are hiring for Pytorch Developer jobs?

Cities near Santa Cruz, CA with the most Pytorch Developer job openings:

Pytorch Developer jobs near you

Hiring: AI Engineer (Applied AI / Machine Learning) at Palo Alto, CA

Realtech Services

Palo Alto, CA • On-site

Contractor

Re-posted 7 days ago

Job description


 

Position: AI Engineer (Applied AI / Machine Learning)

Location: Palo Alto, CA (5 Days a week work)

Duration: Long Term Contract

The Opportunity:

  • We are seeking an AI Engineer to help bring machine learning and generative AI capabilities into real-world products and platforms. You will work at the intersection of data, models, and systems to deliver scalable, production-ready AI solutions.

Role Summary:

  • Develop and operationalize machine learning and generative AI solutions, with a focus on model integration, evaluation, and production readiness.

What You’ll Do:

  • Select, fine-tune (where needed), and integrate ML and foundation models into production systems
  • Design and manage the end-to-end ML lifecycle (data, experimentation, deployment, monitoring)
  • Build robust evaluation frameworks to ensure model quality and performance
  • Apply relevant techniques in NLP, LLMs, or computer vision depending on domain
  • Develop data pipelines, feature engineering workflows, and model serving infrastructure
  • Optimize performance, cost, and scalability across cloud and compute environments
  • Collaborate with cross-functional teams to deliver AI-powered features

Qualifications:

  • Bachelor’s or Master’s in AI, Computer Science, Math, or related field
  • Proficiency in Python and modern ML frameworks (e.g., PyTorch, TensorFlow)
  • Experience deploying ML models into production environments

Core Competencies:

  • Machine Learning Development
  • MLOps & Model Operationalization
  • Data & Model Quality
  • Experimentation & Evaluation
  • Software Engineering
  • Technical Communication