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Pytorch Huggingface Jobs in Texas (NOW HIRING)

AMSYS Talent

LLM Infrastructure Engineer

Houston, TX · On-site

$97K - $127K/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

ClifyX

Python GenAI Model Evaluator

Austin, TX · On-site

PyTorch, TensorFlow, HuggingFace, LangChain. • Evaluation/annotation tools: Scale AI, GroundTruth, Labelbox, Prodigy. • Prompt testing tools: Weights & Biases, MLflow, OpenAI evals, LLM-as-a ...

Vytwo

Data Scientist - INDIA

Prosper, TX · On-site

PyTorch * TensorFlow * Transformers (HuggingFace) * Experience with model monitoring and ML observability. * Ability to write clean, optimized code and leverage AI code assistants. ✅ NLP / GenAI ...

Select Minds LLC

Sr Data Scientist GenAI

Dallas, TX · On-site

$150K - $210K/yr

... PyTorch, TensorFlow, HuggingFace etc. - Proven track record building transformer/NLP / LLM models; experience with fine-tuning, prompt engineering. - Solid experience with information retrieval ...

Brightstar Lottery

AI Engineer

Roma, TX

  • Medical

  • Life

  • Retirement

  • PTO

Proficiency in Python and familiarity with deep learning/NLP libraries (LangChain, PyTorch, TensorFlow, HuggingFace Transformers). * Experience with building Q&A systems and retrieval-augmented ...

Noblesoft Technologies

Gen AI Lead

Dallas, TX · On-site

$138K - $170K/yr

... HuggingFace * Tools/Framework: Git, TensorFlow, PyTorch, PySpark, AWS, MLflow, Docker, Kubernetes, Databricks, SparkSQL, OpenCV, Azure, YOLO, Scikit-Learn, FastAPI, Flask, Django, Keras, Pandas ...

Info Way Solutions

AI Tool Developer

Plano, TX · On-site

... PyTorch, TensorFlow, HuggingFace or similar libraries. • Experience with building or integrating ML pipelines into production‐quality tools (API services, microservices, or batch systems). • ...

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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.

What are popular job titles related to Pytorch Huggingface jobs in Texas?

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The top searched job categories for Pytorch Huggingface jobs in Texas are:

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Cities in Texas with the most Pytorch Huggingface job openings:

Pytorch Huggingface jobs near you

Senior Data Scientist (NLP and GenAI Specialist)

Morgan Stanley

Coppell, TX • On-site

Full-time

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

Morgan Stanley rating

8.4

Company rating: 8.4 out of 10

Based on 155 frontline employees who took The Breakroom Quiz

31st of 150 rated financial services

Job description

Job Summary:
Morgan Stanley is a global leader in financial services, and they are seeking a Senior Data Scientist (NLP Specialist) to join their Non-Financial Risk team. This role involves developing and deploying advanced AI models using NLP and Machine Learning to enhance surveillance and compliance monitoring.
Responsibilities:
• Design high-performance systems leveraging cutting-edge techniques including Large Language Models (LLMs), Retrieval Augmented Generation (RAG), Agentic AI architecture, and knowledge graph analytics.
• Create GenAI-based solutions to automate manual tasks and drive cost efficiency.
• Conduct research to identify novel methods for enhancing analytical solutions.
• Lead and develop junior data scientists to achieve key business objectives
• Collaborate with Compliance, Legal, Financial Crimes, and IT stakeholders to champion the adoption of new AI/ML/NLP approaches, techniques and capabilities
• Champion innovative approaches to improve detection of suspicious activity.
Qualifications:
Required:
• Master's or PhD degree in Computer Science, Machine Learning, Intelligent Systems, Statistics, Mathematics, Engineering or other highly quantitative fields
• 10+ years of hands-on industry experience in building AI/ML/NLP solutions and applied statistical analysis to solve complex business problems
• Knowledge of software design and system principles and excellent skills in either Python (preferred) or Java
• Experience with AI/ML/NLP software packages such as LangChain, LangGraph, Semantic Kernel, CrewAI, OpenAI SDK, PyTorch, HuggingFace, etc.
• Experience in adhering to Software Development Life Cycle (SDLC) principles include GIT related operations
• Strong problem solving and time management skills
• Excellent written and oral communication skills
• Familiarity with financial markets, especially in Compliance, Non-Financial Risk, and Fraud analytics
• Experience with benchmark creation and evaluation including LLM-as-a-Judge based techniques
• Knowledge of Vector Stores, Linux, SPARQL, and Graph Databases
Company:
Morgan Stanley is a financial services institution that delivers capital management, investment banking, and advisory solutions. Founded in 1935, the company is headquartered in New York, USA, with a team of 10001+ employees. The company is currently Late Stage.

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