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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.

Ekcel Technologies Inc

Python LLM Consultant

Dallas, TX · On-site

... PyTorch, HuggingFace Ecosystem). - Expertise in JavaScript framework (e.g., React), HTML5, and CSS. - Service Oriented and Microservices architectures with REST and GraphQL - knowledge of SQL and PL ...

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 ...

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Pytorch Huggingface information

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

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.

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.

What are Pytorch Huggingface developers?

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.

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.
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Pytorch Huggingface jobs near you
Infographic showing various Pytorch Huggingface job openings in the United States as of July 2026, with employment types broken down into 98% Full Time, and 2% Temporary. Highlights an 86% Physical, 1% Hybrid, and 13% Remote job distribution.

Quantitative Research Intern (NLP)

Point72

New York, NY

Other

Re-posted 5 days ago

Job description

ROLE/RESPONSIBILTIES:

We are seeking a quant research intern to join an NLP quant team within Point72. We believe the significant advances in NLP methods show promise for finance. We develop and launch end-to-end signals, from data processing to performance testing.

The ideal candidate will have strong machine learning, data science and software engineering skills, some experience with modern NLP, and a curiosity about finance and trading.

Responsibilities may include:

  • Using NLP to construct features from varied datasets
  • Formulating research hypotheses to derive alpha
  • Building and testing the performance of trading signals based on NLP and financial features
  • Launching identified signals into production

REQUIREMENTS:

  • Bachelor's, Master's or PhD candidate in computer science or other quantitative discipline
  • Proficient in Python and general software engineering principles (github, testing, dev workflow)
  • Strong understanding of machine learning and statistics
  • Prior research experience preferred
  • Experience with deep learning, specifically NLP (PyTorch, HuggingFace, LLMs, etc.) preferred
  • Data science stack familiarity preferred
  • An interest in financial markets
  • Great attitude
  • A collaborative mindset
  • Commitment to the highest ethical standards

ABOUT POINT72:

Point72 is a leading global alternative investment firm led by Steven A. Cohen. Building on more than 30 years of investing experience, Point72 seeks to deliver superior returns for its investors through fundamental and systematic investing strategies across asset classes and geographies. We aim to attract and retain the industry's brightest talent by cultivating an investor-led culture and committing to our people's long-term growth.

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