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Pytorch Huggingface Jobs (NOW HIRING)

... 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 ...

Software Engineer, Inference

San Francisco, CA · On-site

$187.50K - $395K/yr

Experience with model deployment using PyTorch, Huggingface, vLLM, SGLang, tensorRT-LLM, or similar * Experience with queues, scheduling, traffic-control, fleet management at scale * Experience with ...

Senior AI/ML Engineer

Manhattan, NY · On-site +1

$115.20K - $158.20K/yr

Working at Wisq Our platform tech stack includes (but isn't limited to) Python, Pytorch, Huggingface, Java, Kafka, vector databases, reactive programming frameworks, commercial and open-weight LLMs ...

Senior AI/ML Engineer

Redwood City, CA · On-site +1

$127.90K - $175.70K/yr

Working at Wisq Our platform tech stack includes (but isn't limited to) Python, Pytorch, Huggingface, Java, Kafka, vector databases, reactive programming frameworks, commercial and open-weight LLMs ...

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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.

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 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.

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.

More about Pytorch Huggingface jobs
What cities are hiring for Pytorch Huggingface jobs? Cities with the most Pytorch Huggingface job openings:
What states have the most Pytorch Huggingface jobs? States with the most job openings for Pytorch Huggingface jobs include:
Infographic showing various Pytorch Huggingface job openings in the United States as of May 2026, with employment types broken down into 1% Internship, 95% Full Time, and 4% Contract. Highlights an 27% Physical, and 73% Remote job distribution.

Specialist - Data Sciences

Futran Tech Solutions Pvt. Ltd.

Charlotte, NC • On-site

Full-time

This job post has expired 1 day ago. Applications are no longer accepted.


Job description

AWS Databricks MLOps Requirement
Job Title MLOps Engineer to work on AWS GovCloud Databricks
Bachelors degree in computer science Engineering Applied Mathematics or related field
4 to 6 years of strong experience with AWS Gov Cloud environments Export Control FedRAMP environments
Overall 810 years of solid experience in the areas of data engineering machine learning data science
46 years of strong experience with the following machine learning topics classification clustering optimization deep learning NLP with Python in a programming intensive role
68 years of strong experience in Python PySpark coding
46 years of industry experience with popular ML frameworks such as Keras Tensorflow PyTorch HuggingFace Transformers and libraries like scikitlearn etc
46 years of experience with ClassicAIGenAI ML Model Operationalization in Production
4 to 6 years of strong experience in Azure Databricks AWS Databricks specializing in EndtoEnd MLOps architecture with practical expertise in Databricks Unity Catalog MosaicAI serverless solutions MLOps stacks and Lakehouse monitoring among other areas
46 years of industry experience with distributed computing frameworks such as Spark Kubernetes ecosystem etc 46 years of experience with CICD Dev Ops process
Effective communication skills and succinct articulation
Experience working with remote and global teams and cross team collaboration