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Pytorch Huggingface Jobs (NOW HIRING)

TEKsystems

Principal Data Scientist

Raleigh, NC

$75 - $90/hr

Proficiency with PyTorch, HuggingFace, LangChain/LlamaIndex, RAG, Kubernetes, and vector databases. Experience designing production‑grade ML systems with monitoring, evaluation, and observability.

Zortech Solutions

MLOps Engineer

Bellevue, WA · Remote

... PyTorch, HuggingFace Transformers and libraries (like scikit-learn, etc.). * 4-6 years of experience with ClassicAI/GenAI ML Model Operationalization in Production. * 4 to 6 years of strong ...

Citadel

Research Engineer

Miami, FL · On-site

PyTorch, HuggingFace) and a demonstrated passion for leveraging AI and engineering excellence to make big impact in quantitative finance. Bonus Points * Experience working across the entire product ...

Roku

Senior Machine Learning Engineer

San Jose, CA · On-site

$229K - $360K/yr

Ray, PyTorch, HuggingFace, AWS Sagemaker * AI literacy and curiosity. You have either tried Gen AI in your previous work or outside of work, or are curious about Gen AI and have explored it. * MS in ...

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Pytorch Huggingface information

What is a PyTorch Huggingface engineer?

PyTorch Hugging Face developers are professionals who specialize in building and deploying machine learning and natural language processing (NLP) models using PyTorch, an open-source deep learning framework, and the Hugging Face library, which provides a wide range of pre-trained models and tools for NLP tasks. These developers create, fine-tune, and implement models for tasks like text classification, question answering, and language generation. Their expertise includes working with model architectures such as BERT, GPT, and others, as well as integrating models into applications or research projects.

What are the key skills and qualifications needed to thrive as a PyTorch Huggingface engineer?

To thrive as a PyTorch Hugging Face Engineer, you need a strong background in deep learning, Python programming, and experience with machine learning frameworks, supported by a relevant degree such as computer science or engineering. Familiarity with PyTorch, Hugging Face Transformers library, version control systems like Git, and often cloud platforms (e.g., AWS, GCP) is essential, with certifications in machine learning or cloud technologies being advantageous. Strong problem-solving skills, collaboration, and clear communication help you effectively design, implement, and optimize NLP models in cross-functional teams. These skills ensure you can build state-of-the-art AI solutions efficiently, troubleshoot complex challenges, and deliver impactful results in the fast-evolving field of natural language processing.

How do PyTorch Huggingface engineers typically collaborate with data scientists and researchers in a project setting?

PyTorch Huggingface engineers often work closely with data scientists and researchers to implement, fine-tune, and deploy state-of-the-art machine learning models. Collaboration involves regular discussions to understand project objectives, translating research ideas into efficient code, and iterating on model performance. Engineers are responsible for optimizing model pipelines, integrating new features, and ensuring compatibility with the Huggingface ecosystem. Effective communication and teamwork are essential, as projects usually require frequent feedback loops and joint problem-solving sessions.

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

AspectPytorch HuggingfaceMachine Learning Engineer
CredentialsProficiency in Python, deep learning frameworks, familiarity with NLP librariesDegree in CS, data science, or related field; experience with ML models
Work EnvironmentResearch labs, AI startups, tech companies focusing on NLP and deep learningTech companies, consulting firms, R&D departments across industries
UsageDeveloping NLP models, fine-tuning transformers, deploying AI solutionsDesigning, building, and deploying ML models across various domains

While Pytorch Huggingface specializes in NLP model development using transformer architectures, Machine Learning Engineers work across diverse ML applications. Pytorch Huggingface skills are often part of a Machine Learning Engineer's toolkit, but the roles differ in scope and focus.

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Pytorch Huggingface jobs near you
Infographic showing various Pytorch Huggingface job openings in the United States as of August 2026, with employment types broken down into 3% Internship, 91% Full Time, 3% Part Time, and 3% Contract. Highlights an 77% Physical, 4% Hybrid, and 19% Remote job distribution.

Internship

Kinetic Systems and Technologies, Inc

San Francisco, CA • On-site

Full-time

Re-posted 2 days ago

Job description

Kinetic Systems is an applied research lab building data infrastructure for the autonomous hospital.
We spun out of the Stanford PhD program in 2025 and are backed by General Catalyst. Our mission is to advance the capabilities of frontier models for solving clinically and economically meaningful healthcare tasks.
The Role
As an Intern, you'll get hands-on experience across the full stack of what we do: product, research, training, evals, and infrastructure.
Note: This is a full-time role, required to be in-person in SF. Internships must be at least 3-months.
What You'll Do
  • Whatever it takes! Please read the other job descriptions to get a sense of what we do

Who You Are
  • Have experience with PyTorch, HuggingFace, or similar libraries
  • Familiar with best practices around RLEs, benchmarks, evals, and post-training
  • Interested in healthcare as an application (prior background not necessary)

Why Us
If you join our team, you will be joining a team with leading healthcare + AI expertise, and an opportunity to help advance AI research in one of its most meaningful application areas.

Apply