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Pytorch Huggingface Jobs (NOW HIRING)

York Solutions, LLC

ML Engineer

Saint Paul, MN · On-site +1

Strong proficiency in Python and experience with NLP techniques, resources, and methodologies such as Scikit-learn, TensorFlow, PyTorch, HuggingFace, Comprehend, XGBoost, LangChain, etc. Experience ...

Yum! Brands

Associate Manager Machine Learning

Irvine, CA · Hybrid

$134.50K - $158.30K/yr

Strong programming skills in Python and ML tooling (e.g., PyTorch, HuggingFace, ONNX, MLflow). * Experience optimizing model latency and integrating ML with backend infrastructure. Preferred ...

Virtue AI

AI Product Testing Engineer

San Francisco, CA · On-site

$120K - $200K/yr

Familiarity with LLM libraries like PyTorch, HuggingFace, or agent development kits. * Enthusiasm for thriving in a fast-paced startup environment . Why Join Virtue AI * Competitive base salary ...

AMSYS Talent

LLM Infrastructure Engineer

Houston, TX · On-site

$97.10K - $127.40K/yr

Build and deploy LLM inference services using HuggingFace Transformers and PyTorch * Optimize GPU workloads and CUDA memory usage * Implement streaming inference APIs for real-time model responses

Microsoft

Research Intern - Self-Improving AI

Cambridge, MA · On-site

$6.71K - $13.27K/mo

Experience with deep learning and language modeling frameworks including pytorch, huggingface transformers, vLLM. Applied Sciences IC2 - The base pay range for this internship is USD $5,610 - $11,010 ...

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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.

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Pytorch Huggingface jobs near you
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.
Senior Machine Learning Engineer, Platform

Senior Machine Learning Engineer, Platform

Roku

San Jose, CA

$229.50K - $367.10K/yr

Other

Medical, Life, PTO

Posted 24 days ago

Job description

About the Team

The Recommendations team drives personalized experiences across our platform by leveraging state-of-the-art machine learning. Our mission is to deliver meaningful, context-aware recommendations that adapt to each user's preferences in real time. We believe that true innovation in personalization requires more than great models-it depends on a robust, flexible ML platform built for experimentation and scale. To that end, we design and build the underlying ML infrastructure, ensuring our systems remain fast, reliable, and at the forefront of technology. Our work blends innovation, engineering excellence, and a deep commitment to understanding our users, shaping how they discover and engage with content every day.


About the Role

We seek an outstanding, creative, and passionate Machine Learning Platform Engineer to join Roku's Recommendation team. In this role, you will design, build, and scale robust distributed systems that power the next generation of personalized content recommendations for millions of Roku users. You will focus on developing end-to-end machine learning platforms and infrastructure, ensuring seamless deployment, monitoring, and optimization of algorithms and operational workflows that deliver unique experiences at scale.
For California Only - The estimated annual salary for this position is between $229,500 - $367,100 annually. Compensation packages are based on factors unique to each candidate, including but not limited to skill set, certifications, and specific geographical location. This role is eligible for health insurance, equity awards, life insurance, disability benefits, parental leave, wellness benefits, and paid time off.
    

What You'll Be Doing
  • Design, build, and maintain scalable platform services: feature store, real-time inference services, vector DBs etc., that serve millions of transactions per second
  • Run and monitor online AB tests via robust platform services, analyzing platform metrics and business KPIs to optimize recommendation system performance
  • Collaborate closely with US-based engineering and cross-functional teams to translate business requirements into modular platform components and APIs
  • Enhance and evolve the ML platform ecosystem to support high developer velocity, system scalability, and adaptability to future business needs
  • Contribute to onboarding, training, and mentoring new team members on emerging platform engineering best practices and technologies
We're Excited If You Have
  • 5+ years of experience building software solutions to concrete problems
  • Strong CS fundamentals. Should be able to write an algorithm with ease
  • You are fluent with one of high-level programming languages like Java, Scala, Kotlin or Python
  • We'd love to see that you've worked with big data systems (Spark, Kafka, Flink, S3, AirFlow)
  • Familiar with model ML framework and tools: 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 Computer Science or related field

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