ZipRecruiter

Log In

1

Tensorflow Pytorch Jobs in Virginia (NOW HIRING)

Virtual Networx

AI/ML Engineer

Mclean, VA · On-site

Python, Machine Learning, Deep Learning, Scikit-learn, TensorFlow, PyTorch, Pandas, NumPy, SQL, NLP, Computer Vision, Generative AI, LLM, Prompt Engineering, RAG, Vector Databases, REST APIs ...

VTG

Data Scientist

Mclean, VA

Experience with machine learning frameworks (e.g., Scikit-learn, TensorFlow, PyTorch) * Strong foundation in statistics, probability, and data analysis techniques * Experience with SQL and working ...

VTG

Data Scientist

Chantilly, VA

Experience with machine learning frameworks (e.g., Scikit-learn, TensorFlow, PyTorch) * Strong foundation in statistics, probability, and data analysis techniques * Experience with SQL and working ...

VTG

Data Scientist

Herndon, VA

Experience with machine learning frameworks (e.g., Scikit-learn, TensorFlow, PyTorch) * Strong foundation in statistics, probability, and data analysis techniques * Experience with SQL and working ...

MDAEdge

Data Scientist - TS/SCI with Poly

Mclean, VA · On-site

Responsibilities : • Develop code to perform complex modeling to detect and characterize objects (using Python, TensorFlow, Pytorch, and related software packages) and enhance evolving analytic ...

Staffingine LLC

AI Platform Developer

Herndon, VA · On-site

Proficiency is required in tools like TensorFlow, PyTorch, Keras, and scikit-learn. * Data Science and Analysis: Skills in data acquisition, cleaning, preprocessing, and feature engineering are ...

ECS

AI Model Developer with Security Clearance

Fairfax, VA · On-site

$125K - $150K/yr

Strong proficiency in Python (NumPy, Pandas, scikit-learn, TensorFlow, PyTorch, Transformers). * Full Stack: Experience with backend frameworks (Django, Flask, FastAPI) and frontend frameworks (React ...

next page

Showing results 1-20

All JobsTensorflow Pytorch JobsTensorflow Pytorch Jobs in Virginia

Tensorflow Pytorch information

What are TensorFlow and PyTorch?

TensorFlow and PyTorch are two of the most popular open-source deep learning frameworks used by researchers and developers to build, train, and deploy machine learning models. TensorFlow, developed by Google, offers robust support for production environments and has a large ecosystem. PyTorch, developed by Facebook, is known for its flexibility, ease of use, and dynamic computational graph, making it popular in academia and research. Both frameworks support a wide range of neural network architectures and are used extensively for tasks such as computer vision, natural language processing, and reinforcement learning.

What are the key skills and qualifications needed to thrive as a deep learning engineer specializing in TensorFlow and PyTorch?

To thrive as a Deep Learning Engineer with a focus on TensorFlow and PyTorch, you need a strong background in computer science, mathematics, and machine learning, typically supported by a relevant degree. Proficiency in programming languages like Python, experience with TensorFlow and PyTorch frameworks, and familiarity with cloud platforms or GPU computing are essential. Analytical thinking, problem-solving, and effective communication are standout soft skills for collaborating with teams and interpreting model results. These skills are crucial for developing, deploying, and optimizing AI models that drive innovation and solve complex real-world problems.

How do TensorFlow/PyTorch engineers typically collaborate with data scientists and other team members in a production environment?

TensorFlow and PyTorch engineers often work closely with data scientists to transform experimental machine learning models into efficient, scalable production solutions. Collaboration involves frequent code reviews, shared development environments, and regular meetings to align model requirements with deployment constraints. Engineers also coordinate with DevOps teams to ensure smooth integration and monitoring of models in production. Strong communication skills and a willingness to iterate on solutions are essential for bridging the gap between research and real-world application.

What is the difference between Tensorflow Pytorch vs Data Scientist?

AspectTensorflow PytorchData Scientist
Required SkillsDeep learning frameworks, Python, machine learningData analysis, statistical skills, Python/R, machine learning
Work EnvironmentAI/ML development, research, software engineeringData analysis, reporting, business insights
Industry UsageAI/ML projects, research labs, tech companiesBusiness, finance, healthcare, tech

Tensorflow and Pytorch are deep learning frameworks used primarily by AI/ML developers, while Data Scientists utilize these tools for data analysis and modeling. Although their skill sets overlap, Tensorflow Pytorch focus on model development, whereas Data Scientists apply these models to derive insights and inform decisions.

What are popular job titles related to Tensorflow Pytorch jobs in Virginia?

For Tensorflow Pytorch jobs in Virginia, the most frequently searched job titles are:

What job categories do people searching Tensorflow Pytorch jobs in Virginia look for?

The top searched job categories for Tensorflow Pytorch jobs in Virginia are:

What cities in Virginia are hiring for Tensorflow Pytorch jobs?

Cities in Virginia with the most Tensorflow Pytorch job openings:

Tensorflow Pytorch jobs near you

AI/ML Engineer

Virtual Networx

Mclean, VA • On-site

Other

This job post has expired today. Applications are no longer accepted.

Job description

Role: AI/ML Engineer
Experience: 10+ Years
Duration: 12 months
Location: MC Lean , VA

Skills: Python, Machine Learning, Deep Learning, Scikit-learn, TensorFlow, PyTorch, Pandas, NumPy, SQL, NLP, Computer Vision, Generative AI, LLM, Prompt Engineering, RAG, Vector Databases, REST APIs

Responsibilities:

  • Design, develop, and deploy Machine Learning and AI solutions for business applications.
  • Build and optimize ML models for classification, regression, forecasting, recommendation, and NLP use cases.
  • Develop data preprocessing, feature engineering, model training, and evaluation pipelines.
  • Work with Python, Pandas, NumPy, Scikit-learn, TensorFlow, and/or PyTorch.
  • Develop and integrate Generative AI and LLM-based solutions where applicable.
  • Work with OpenAI/LLM APIs, prompt engineering, embeddings, vector databases, and RAG architectures.
  • Build scalable ML pipelines using MLflow, Kubeflow, Databricks, AWS, Azure, or Google Cloud Platform.
  • Deploy models through REST APIs, Docker, Kubernetes, and cloud platforms.
  • Monitor model performance, data quality, drift, and production issues.
  • Collaborate with Data Engineers, Software Engineers, Data Scientists, Product Owners, and business stakeholders.
  • Perform model tuning, experimentation, validation, and performance optimization.
  • Implement MLOps practices for CI/CD, model versioning, experiment tracking, and automated deployment.
  • Ensure AI solutions meet requirements for security, scalability, reliability, and responsible AI.
  • Document models, architectures, workflows, and technical processes.

Required Skills

  • Python
  • Machine Learning
  • Deep Learning
  • Scikit-learn
  • TensorFlow / PyTorch
  • Pandas / NumPy
  • SQL
  • NLP / Computer Vision as applicable
  • Generative AI / LLM
  • Prompt Engineering
  • RAG
  • Vector Databases
  • REST APIs
  • Docker / Kubernetes
  • Cloud: AWS / Azure / Google Cloud Platform
  • Git
  • MLOps / MLflow

Apply