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Tensorflow Pytorch Jobs (NOW HIRING)

Transparent Search Group

AI Engineer

Sunnyvale, CA · Hybrid

$225K - $300K/yr

Key Responsibilities Design and develop AI/ML models using Python, TensorFlow, PyTorch, and ONNX. Optimize model performance and deploy solutions across distributed automotive environments.

Palnar

AI/ML Architect

Nashville, TN · On-site

$61.50 - $79.25/hr

Strong expertise in machine learning frameworks such as TensorFlow, PyTorch, or scikit-learn. * Experience with designing and implementing end-to-end machine learning pipelines. * Proficiency in data ...

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 ...

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 ...

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 ...

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Tensorflow Pytorch information

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$37.5K

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$196.5K

How much do tensorflow pytorch jobs pay per year?

As of Jul 10, 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.
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Tensorflow Pytorch jobs near you

AI Engineer

Transparent Search Group

Sunnyvale, CA • Hybrid

$225K - $300K/yr

Full-time

Re-posted 6 hours ago

Job description

AI Engineer Automotive Software Innovation (Sunnyvale, CA | Hybrid | $225K to $300K)

About the Company

A rapidly growing automotive technology company is revolutionizing the evolution of vehicles through software. Their AI-driven platform enables next-generation, software-defined vehicles, bridging the gap between automotive reliability and consumer-grade innovation. With technology already deployed in over 5 million vehicles worldwide, they are trusted by top global OEMs to power the future of connected mobility.

Role Overview

We are seeking a highly skilled AI Engineer with deep expertise in machine learning, data science, and model optimization. This is a hands-on role where you'll help design, implement, and deploy scalable AI models that enhance intelligent vehicle systems. You'll collaborate closely with cross-functional teams across data, embedded systems, and cloud infrastructure to deliver production-ready AI solutions.

Key Responsibilities

Design and develop AI/ML models using Python, TensorFlow, PyTorch, and ONNX.

Optimize model performance and deploy solutions across distributed automotive environments.

Collaborate with data and engineering teams to operationalize ML pipelines using MLOps best practices.

Conduct experiments, evaluate models, and iterate to ensure reliability, efficiency, and scalability.

Present solutions and technical insights to leadership and technical peers.

Requirements

Bachelors or Masters degree in Computer Science, AI/ML, or related field.

4+ years of experience in applied machine learning or AI engineering.

Strong proficiency in Python and experience with TensorFlow, PyTorch, or similar frameworks.

Solid understanding of data pipelines, MLOps, and model deployment.

Experience with real-time, embedded, or distributed systems is a strong plus.

Excellent communication skills and a collaborative, problem-solving mindset.

Location & Work Setup

Hybrid Sunnyvale, CA

Compensation

$225,000 $300,000 + competitive benefits

Interview Process

Recruiter Screening

Technical Interview (Python Coding)

Onsite Technical Presentation (Algorithms, ML, Data Science)

Final Interview with CTO