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Tensorflow Pytorch Jobs (NOW HIRING)

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 ...

Flexton Inc

Expert AI/ML Engineer

Oakland, CA · On-site

Experience with Python and common ML/data science libraries such as pandas, NumPy, scikit-learn, XGBoost, TensorFlow, PyTorch, or similar. * Practical experience with MLOps concepts such as model ...

1 point system

Senior Data Scientist

New York, NY · Remote

Expertise in Python, SQL, and Spark, and a broad array of machine learning frameworks (Scikit-Learn, XGBoost, Tensorflow, PyTorch, MXNet, LLM, etc). * Experience in developing and deploying solutions ...

Vultus Inc

Machine Learning Engineer

Honolulu, HI · On-site +1

$110K - $145K/yr

Hands-on experience with TensorFlow, PyTorch, or Scikit-learn. * Knowledge of statistics, probability, and linear algebra. * Experience with SQL and NoSQL databases. * Familiarity with REST APIs and ...

Connexions Data Inc

Gen AI Developer

Seattle, WA · On-site

$57.25 - $78.75/hr

Must Have Technical/Functional Skills Experience in executing projects in Agile Framework Proven experience in machine learning and deep learning frameworks (e.g., TensorFlow, PyTorch). Strong ...

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Tensorflow Pytorch information

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How much do tensorflow pytorch jobs pay per year?

As of Jul 28, 2026, the average yearly pay for tensorflow pytorch in the United States is $122,738.00, according to ZipRecruiter salary data. Most workers in this role earn between $98,500.00 and $136,000.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Deep Learning Engineer specializing in TensorFlow and PyTorch, and why are they important?

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.

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 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.

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.
More about Tensorflow Pytorch jobs
What cities are hiring for Tensorflow Pytorch jobs? Cities with the most Tensorflow Pytorch job openings:
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Tensorflow Pytorch jobs near you
Infographic showing various Tensorflow Pytorch job openings in the United States as of July 2026, with employment types broken down into 50% Internship, and 50% Full Time. Highlights an 100% In-person job distribution, with an average salary of $122,738 per year, or $59 per hour.
Data Scientist

Data Scientist

VTG

Mclean, VA

Full-time

Posted 5 days ago

Job description

Overview

VTG is seeking a highly analytical and driven Data Scientist with at least 5 years of experience leveraging data to drive business insights and decision-making. The ideal candidate will have strong expertise in statistical analysis, machine learning, and data modeling, with the ability to translate complex data into actionable insights.


What will you do?
  • Analyze large, complex datasets to identify trends, patterns, and actionable insights
  • Develop, train, and validate machine learning and statistical models
  • Design and implement experiments, including A/B testing and predictive analytics
  • Collaborate with data engineers and analysts to ensure data quality and accessibility
  • Communicate findings and recommendations to technical and non-technical stakeholders
  • Build data visualizations and dashboards to present insights effectively
  • Deploy and monitor machine learning models in production environments
  • Continuously improve model performance and accuracy
  • Stay current with emerging data science techniques, tools, and technologies

Do you have what it takes?
  • Active TS/SCI with Polygraph required.
  • Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Mathematics, or related field (PhD preferred for some roles)
  • 5+ years of experience in data science or a related field
  • Strong proficiency in programming languages such as Python or R
  • 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 with large datasets
  • Experience with data visualization tools (e.g., Tableau, Power BI, Matplotlib, Seaborn)
  • Familiarity with cloud platforms (AWS, Azure, or GCP)
Qualifications:
  • Active TS/SCI with Polygraph required.
  • Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Mathematics, or related field (PhD preferred for some roles)
  • 5+ years of experience in data science or a related field
  • Strong proficiency in programming languages such as Python or R
  • 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 with large datasets
  • Experience with data visualization tools (e.g., Tableau, Power BI, Matplotlib, Seaborn)
  • Familiarity with cloud platforms (AWS, Azure, or GCP)
Education:UNAVAILABLEEmployment Type: FULL_TIME

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