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Pytorch Huggingface Jobs in Oregon (NOW HIRING)

Experience with AI/ML technologies, including machine learning frameworks (TensorFlow, PyTorch) or ... Experience working with LLM frameworks and AI SDKs (OpenAI, LangChain, HuggingFace, etc.

Expert proficiency in PyTorch and modern machine learning infrastructure (e.g., HuggingFace ecosystem, PEFT, Captum, MLflow, and distributed GPU computing setups) * Documented technical leadership ...

OR · On-site

... keras, pytorch, tensorflow, pandas, numpy, carot, tidyverse * Command of data science and ... HuggingFace, and GPT-x etc.) * Experience with natural language processing toolkits like NLTK ...

... HuggingFace, Langchain, LLAMA/Mistral and OpenAI, vector databases. * 1+ years of hands-on data ... PyTorch, GPU.\ * Experience building production-grade machine learning deployments on AWS, Azure ...

... HuggingFace, Langchain, LLAMA/Mistral and OpenAI, vector databases. * 5+ years of hands-on data ... and TensorFlow/PyTorch, GPU. * General understanding of Responsible AI (RAI), including ...

... HuggingFace, Langchain, LLAMA/Mistral and OpenAI, vector databases. * 3+ years of hands-on data ... nltk, and TensorFlow/PyTorch, GPU. * Experience building production-grade machine learning ...

... HuggingFace, Langchain, LLAMA/Mistral and OpenAI, vector databases. * 9+ years of hands-on data ... PyTorch, GPU. * Good to have publication record in journals/conferences and/or history of ...

... HuggingFace, Langchain, Llama/Mistral and OpenAI, vector databases. * 7+ years of hands-on data ... PyTorch, GPU's. * Experience building production-grade machine learning deployments on AWS, Azure ...

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.
What are popular job titles related to Pytorch Huggingface jobs in Oregon? For Pytorch Huggingface jobs in Oregon, the most frequently searched job titles are:
What job categories do people searching Pytorch Huggingface jobs in Oregon look for? The top searched job categories for Pytorch Huggingface jobs in Oregon are:
Software Engineer (US-Remote)

Software Engineer (US-Remote)

Gnostech LLC

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Posted 8 days ago


Job description

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Software Engineer (US-Remote)
Summary
Title: Software Engineer (US-Remote) ID: 1191 Location: US-Remote or Marlton, NJ area
Description

A Software Engineer is needed to design, develop, and maintain modern software applications and services. The engineer will work within a cross-functional team to build scalable cloud-native applications, microservices, and AI-enabled systems supporting both internal and external products. The role involves collaborating with software development, cybersecurity, and test engineering teams to design, implement, document, and deploy high-quality software solutions.

Overview

Location

  • US-Remote or Marlton, NJ area

Job Title

  • Software Engineer

Salary

  • Commensurate with industry position, depending on experience

Shift

  • Typical: Monday through Friday 9am to 5pm

Travel

  • Less than 5%

Position Responsibilities

  • Design, develop, test, and deploy scalable applications, APIs, and microservices using modern software engineering practices.
  • Build and integrate AI-enabled capabilities into applications, including machine learning models, LLM integrations, and data-driven services.
  • Participate in architecture and design reviews to ensure solutions meet scalability, security, and performance requirements.
  • Develop cloud-native services and containerized applications deployed using Docker and Kubernetes.
  • Integrate AI models, data pipelines, and inference services into production systems.
  • Collaborate with cross-functional teams including DevOps, cybersecurity, QA, and product stakeholders.
  • Develop and maintain technical documentation including system architecture diagrams, API specifications, and AI model documentation.
  • Maintain code repositories using modern version control and CI/CD pipelines.
  • Apply secure coding practices and support DevSecOps workflows.
  • Utilize AI-assisted development tools (e.g., LLM coding assistants, code analysis tools) to enhance productivity and code quality.
  • Monitor and troubleshoot applications in distributed environments using logging, observability, and telemetry tools.

Minimum Security Clearance

  • Must be eligible and pass security screening to obtain DoD Top Secret

Required Qualifications and Skills

  • Strong proficiency in software design, development, and debugging.
  • Experience with modern programming languages such as Java, Python, or similar backend technologies.
  • Experience with Spring Boot, REST APIs, and microservices architectures.
  • Experience with AI/ML technologies, including machine learning frameworks (TensorFlow, PyTorch) or modern AI integration tools.
  • Experience integrating AI services, LLM APIs, or intelligent automation capabilities into applications.
  • Experience building containerized applications using Docker and Kubernetes.
  • Experience with cloud platforms such as AWS, Azure, or Google Cloud.
  • Experience with relational databases (MySQL, MariaDB, PostgreSQL).
  • Understanding of CI/CD pipelines, automated testing, and DevSecOps practices.
  • Familiarity with the AI/ML lifecycle including data preparation, model training, evaluation, deployment, and monitoring.
  • Ability to analyze problems, design solutions, and work collaboratively in a team environment.

Additional Desired Qualifications, Skills, Certifications

  • Experience working with LLM frameworks and AI SDKs (OpenAI, LangChain, HuggingFace, etc.).
  • Experience with vector databases or embeddings systems (Pinecone, Weaviate, Elasticsearch, etc.).
  • Experience implementing MLOps pipelines for model deployment and monitoring.
  • Experience with distributed systems, event streaming, or message queues (Kafka, RabbitMQ).
  • Familiarity with observability platforms (Prometheus, Grafana, ELK stack).
  • Experience with NoSQL databases (MongoDB, Cassandra).
  • Active or ability to obtain DoD Top Secret clearance.

Education and Training Required

  • Bachelor's degree (in Engineering, Computer Science, Math, or related field)

Minimum Years of Experience

  • 2+

===============================================================

Gnostech is an Equal Opportunity/Affirmative Action Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, age, protected veteran status, or disability status. For more information, please visit www.eeoc.gov 
If this position requires a government clearance, the applicants selected will be subject to a government security investigation and must meet eligibility requirements for accessing classified information.

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