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Tensorflow Pytorch Jobs (NOW HIRING)

Purple Drive

AI/ML Engineer

Chicago, IL

ExpertiseGood knowledge of AIML frameworks (TensorFlow, PyTorch,etc.) and libraries Experience with cloud based AIML platforms (e.g. Dataiku, AWS.) Strong programming skills in Python, Java or C ...

Masterapp Labs

Data AI/ML Engineer

Milton, GA

TensorFlow, PyTorch, Scikit-learn, Vertex AI * Data Engineering: Apache Spark (PySpark), ETL pipelines * MLOps: Docker, Kubernetes (GKE), CI/CD pipelines (Jenkins / GitHub Actions / Cloud Build)

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Saxon Global

AI/ML Engineer

Pasadena, CA

... TensorFlow, PyTorch) * 2+ yrs of Chatbot development and Retrieval-Augmented Generation (RAG) applications is a strong advantage. * 2+ yrs of Large Language Models (LLMs) or Small Language Models ...

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.

Diverse Lynx

AI/ML Engineer

Detroit, MI

$55/hr

Strong hands on experience with o Python and ML frameworks (TensorFlow, PyTorch, scikit learn) o Cloud platforms (Azure preferred) o ML model deployment and productionization Experience building and ...

Saxon Global

Platform Developer

Herndon, VA

Hands-on experience with TensorFlow, PyTorch, Keras, and scikit-learn. Experience with data processing, SQL and NoSQL databases. Familiarity with Apache Spark and large-scale data processing. Solid ...

Prophecy Technologies

AI/ML Engineer

Philadelphia, PA · On-site

Experience with AI frameworks including TensorFlow, PyTorch, or Keras * Strong understanding of machine learning, deep learning, NLP, and computer vision * Knowledge of cloud platforms such as AWS ...

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

Mclean, VA · On-site

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 ...

PALNAR

AI/ML Architect

Nashville, TN

$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 · On-site

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 ...

Saxon Global

Platform Developer

Reston, VA

Hands-on experience with TensorFlow, PyTorch, Keras, and scikit-learn. * Experience with data processing, SQL and NoSQL databases. * Familiarity with Apache Spark and large-scale data processing.

Aptino

AI/ML Engineer

Exton, PA

TensorFlow / PyTorch / Scikit-learn * Machine Learning & AI Model Development * AWS / Azure / Google Cloud Platform * Data Pipelines & MLOps * NLP / Generative AI experience is a plus Qualifications:

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How much do tensorflow pytorch jobs pay per year?

As of Jun 19, 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
GenAI Engineer - Remote

GenAI Engineer - Remote

MM International

San Francisco, CA • Remote

Contractor

Posted yesterday

Job description

GenAI/LLM Engineer (NLP, TensorFlow, PyTorch SME)

San Francisco, Bay Area, CA

Duration: Six months may extend to 12 months

Must be in the Greater Bay area – or in California

Domain: utilities

GenAI/LLM Engineer (NLP, TensorFlow, PyTorch SME)

Implementing GenAI requires specialized expertise in large language models. Traditional data scientists often haven't had the opportunity to dive deep into the practical intricacies of LLMs—particularly advanced fine-tuning techniques, model compression strategies, memory optimization approaches, and specialized training workflows. This role requires a hands-on deep learning practitioner comfortable with modern frameworks and libraries specific to LLM development.

  • Enables domain-specific fine-tuning of models to client's unique utility context
  • Improves model performance while reducing computational costs through advanced optimization techniques
  • Creates Client-specific AI capabilities that address our unique operational challenges
  • Enables the CoE to move beyond generic AI tools to customized solutions that deliver higher business value

Key Responsibilities:

  • Implement and optimize advanced fine-tuning approaches (LoRA, PEFT, QLoRA) to adapt foundation models to client's domain
  • Develop systematic prompt engineering methodologies specific to utility operations, regulatory compliance, and technical documentation
  • Create reusable prompt templates and libraries to standardize interactions across multiple LLM applications and use cases
  • Implement prompt testing frameworks to quantitatively evaluate and iteratively improve prompt effectiveness
  • Establish prompt versioning systems and governance to maintain consistency and quality across applications
  • Apply model customization techniques like knowledge distillation, quantization, and pruning to reduce memory footprint and inference costs
  • Tackle memory constraints using techniques such as sharded data parallelism, GPU offloading, or CPU+GPU hybrid approaches
  • Build robust retrieval-augmented generation (RAG) pipelines with vector databases, embedding pipelines, and optimized chunking strategies
  • Design advanced prompting strategies including chain-of-thought reasoning, conversation orchestration, and agent-based approaches
  • Collaborate with the MLOps engineer to ensure models are efficiently deployed, monitored, and retrained as needed

Expected Skillset:

  • Deep Learning & NLP: Proficiency with PyTorch/TensorFlow, Hugging Face Transformers, DSPy, and advanced LLM training techniques
  • GPU/Hardware Knowledge: Experience with multi-GPU training, memory optimization, and parallelization strategies
  • LLMOps: Familiarity with workflows for maintaining LLM-based applications in production and monitoring model performance
  • Technical Adaptability: Ability to interpret research papers and implement emerging techniques (without necessarily requiring PhD-level mathematics)
  • Domain Adaptation: Skills in creating data pipelines for fine-tuning models with utility-specific content