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Tensorflow Pytorch Jobs (NOW HIRING)

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 ...

Siri InfoSolutions Inc

MLOPS Engineer

Malvern, PA · On-site

$50 - $60/hr

Proficiency with Machine Learning frameworks like TensorFlow, PyTorch, or Scikit-learn. * An in-depth understanding of contemporary software engineering techniques. * Docker, Kubernetes, and/or ...

Winaxis

AI/ML Engineer

Dallas, TX · On-site

$113K - $136K/yr

TensorFlow PyTorch Scikit-learn XGBoost Strong understanding of: Supervised and Unsupervised Learning Deep Learning Neural Networks Natural Language Processing (NLP) Computer Vision Reinforcement ...

Winaxis

AI/ML Engineer

Boston, MA · On-site

$32 - $35/hr

TensorFlow PyTorch Scikit-learn XGBoost Strong understanding of: Supervised and Unsupervised Learning Deep Learning Neural Networks Natural Language Processing (NLP) Computer Vision Reinforcement ...

Winaxis

AI/ML Engineer

Boston, MA · On-site

$124K - $149K/yr

TensorFlow PyTorch Scikit-learn XGBoost Strong understanding of: Supervised and Unsupervised Learning Deep Learning Neural Networks Natural Language Processing (NLP) Computer Vision Reinforcement ...

Winaxis

AI/ML Engineer

Boston, MA · On-site

$30 - $35/hr

TensorFlow PyTorch Scikit-learn XGBoost Strong understanding of: Supervised and Unsupervised Learning Deep Learning Neural Networks Natural Language Processing (NLP) Computer Vision Reinforcement ...

Winaxis

AI/ML Engineer

Dallas, TX · On-site

$113K - $136K/yr

TensorFlow PyTorch Scikit-learn XGBoost Strong understanding of: Supervised and Unsupervised Learning Deep Learning Neural Networks Natural Language Processing (NLP) Computer Vision Reinforcement ...

Saransh Inc

Lead AI/ML Developer

New York, NY · On-site

$64.50 - $84.50/hr

Proficiency in Python with experience using libraries such as TensorFlow, PyTorch, Scikit-learn, or Keras. * Strong understanding of machine learning algorithms, deep learning, and natural language ...

Winaxis

AI/ML Engineer

Boston, MA · On-site

$124K - $149K/yr

TensorFlow PyTorch Scikit-learn XGBoost Strong understanding of: Supervised and Unsupervised Learning Deep Learning Neural Networks Natural Language Processing (NLP) Computer Vision Reinforcement ...

Winaxis

AI/ML Engineer

Dallas, TX · On-site

$113K - $136K/yr

TensorFlow PyTorch Scikit-learn XGBoost Strong understanding of: Supervised and Unsupervised Learning Deep Learning Neural Networks Natural Language Processing (NLP) Computer Vision Reinforcement ...

ACS Consultancy Services

AI/ML Engineer

Albany, NY · Remote

Build and optimize data pipelines and ML workflows using frameworks like TensorFlow, PyTorch, scikit-learn, or XGBoost. * Implement models in production environments using MLOps best practices (e.g ...

Anblicks

Full Stack Engineer( Go Lang)

Dallas, TX · On-site

Proficiency in Python and ML libraries such as TensorFlow, PyTorch, Scikit-learn, etc. * Strong knowledge of CI/CD , and DevOps principles. * Experience with Docker and containerized application ...

Winaxis

AI/ML Engineer

Boston, MA · On-site

$35 - $45/hr

TensorFlow * PyTorch * Scikit-learn * XGBoost * Strong understanding of: * Supervised and Unsupervised Learning * Deep Learning * Neural Networks * Natural Language Processing (NLP) * Computer Vision

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All JobsTensorflow Pytorch Jobs

Tensorflow Pytorch information

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$37.5K

$122.7K

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How much do tensorflow pytorch jobs pay per year?

As of Jul 31, 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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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.

AI Scientist Consumer Risk & Fraud *** Direct End Client ***

Projas Technologies, LLC

Sunnyvale, CA • On-site

Other

Re-posted 2 days ago

Job description

We re seeking a seasoned AI Scientist to lead the development of advanced fraud detection and credit risk models for next-generation financial products. This role combines deep technical expertise with strategic thinking to build scalable, production-ready AI solutions that safeguard money movement systems and lending platforms.

What You ll Do
  • Own the end-to-end lifecycle of fraud risk models from design and development to deployment and monitoring.
  • Build efficient data pipelines for feature engineering, model training, scoring, and reporting using Python and SQL.
  • Apply cutting-edge machine learning techniques (deep learning, tree-based models, NLP, time series, causal inference) to detect fraud patterns.
  • Collaborate with product, engineering, and risk teams to align models with business objectives and compliance standards.
  • Ensure model fairness, interpretability, and regulatory compliance in all deployments.
  • Research and implement innovative AI/ML approaches to improve detection accuracy and scalability.
  • Contribute to MLOps best practices, including automated retraining, monitoring, and version control.
Required Qualifications
  • Advanced degree (MS/PhD) in Computer Science, Data Science, AI, Statistics, or related field.
  • 6+ years of experience in AI/ML model development and deployment.
  • Strong proficiency in Python and SQL.
  • Expertise in fraud risk modeling, credit risk, and financial transaction systems.
  • Hands-on experience with ML frameworks (TensorFlow, PyTorch) and cloud platforms (AWS or Google Cloud Platform).
  • Deep understanding of model calibration, bias correction, and graph-based fraud detection.
  • Proven ability to design scalable pipelines and work in agile environments.
Preferred Skills
  • Experience with Vertex AI, SageMaker, or similar MLOps platforms.
  • Familiarity with workflow orchestration tools (Apache Airflow).
  • Strong background in A/B testing and statistical experimentation.
Why This Role Matters

You ll be solving complex, high-impact problems that protect customers and enable secure financial transactions. If you thrive in fast-paced environments and love applying AI to real-world challenges, this is your opportunity to make a measurable difference.

AI Scientist, Machine Learning Engineer, Fraud Detection, Credit Risk Modeling, Python, SQL, TensorFlow, PyTorch, Deep Learning, NLP, Time Series Analysis, MLOps, Vertex AI, SageMaker, Apache Airflow, Big Data, Financial Risk, Cloud Computing, AWS, Google Cloud Platform, Data Pipelines, Model Deployment, Risk Analytics, Graph Analysis, Fraud Prevention, Fintech AI, Predictive Modeling, Statistical Analysis, CI/CD, Kubernetes, Data Engineering

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