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Tensorflow Pytorch Jobs in Columbus, OH (NOW HIRING)

Vertiv

AI Architect

Westerville, OH · On-site

$60.75 - $80/hr

... g., TensorFlow, PyTorch, Keras) • Certification or training in artificial intelligence, machine learning, or data science. • Experience in working with big data technologies, such as Hadoop ...

Maximus

AI/ML Software Developer

Columbus, OH · Remote

... TensorFlow, PyTorch, scikit-learn). - 3 years of experience with programming languages commonly used in AI/ML development, such as Python, and supporting languages (e.g., SQL, Java, C++). - 3 years ...

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Vertiv Co

AI Architect

Westerville, OH

$60.75 - $80/hr

Proficiency in programming languages such as Python, Java, or C++, and familiarity with popular AI libraries and frameworks (e.g., TensorFlow, PyTorch, Keras) * Certification or training in ...

Vertiv Group Corp.

AI Architect

Westerville, OH · On-site

$60.75 - $80/hr

Proficiency in programming languages such as Python, Java, or C++, and familiarity with popular AI libraries and frameworks (e.g., TensorFlow, PyTorch, Keras) * Certification or training in ...

McKesson

AI Full stack Software Engineer

Columbus, OH · Remote

$110.30K - $183.80K/yr

Proficiency in Python and AI related python libraries such asopenai,tensorflow,pytorch * AI System Evaluation & Optimization: Proficient in sensitivity metrics (precision, recall, F1-score, AUC-ROC ...

CoverMyMeds

AI Full stack Software Engineer

Columbus, OH · Remote

$110.30K - $183.80K/yr

Proficiency in Python and AI related python libraries such asopenai,tensorflow,pytorch * AI System Evaluation & Optimization: Proficient in sensitivity metrics (precision, recall, F1-score, AUC-ROC ...

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How much do tensorflow pytorch jobs pay per year?

As of May 30, 2026, the average yearly pay for tensorflow pytorch in Columbus, OH is $118,553.00, according to ZipRecruiter salary data. Most workers in this role earn between $95,100.00 and $131,400.00 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.

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 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.

What are popular job titles related to Tensorflow Pytorch jobs in Columbus, OH? For Tensorflow Pytorch jobs in Columbus, OH, the most frequently searched job titles are:
What job categories do people searching Tensorflow Pytorch jobs in Columbus, OH look for? The top searched job categories for Tensorflow Pytorch jobs in Columbus, OH are:
What cities near Columbus, OH are hiring for Tensorflow Pytorch jobs? Cities near Columbus, OH with the most Tensorflow Pytorch job openings:
Tensorflow Pytorch jobs near you

Risk Program Senior Associate

JPMorganChase

Columbus, OH • On-site

Full-time

Posted yesterday

Job description

Job Summary:
JPMorganChase is a leading financial services firm dedicated to helping customers achieve their financial goals. The Risk Program Senior Associate will focus on developing machine learning models for credit decision and fraud modeling, ensuring ethical standards and regulatory compliance while collaborating across teams.
Responsibilities:
• Model Development: Design and develop machine learning models to drive impactful decisions across credit decisions and fraud modeling, covering the entire customer lifecycle, including acquisition, account management, transaction authorization, and collections.
• Advanced Machine Learning Techniques: Apply state-of-the-art machine learning methodologies — including deep learning architecture, transformer-based models, and LLMs — on big data platforms to tackle complex business challenges.
• Explainability & Fairness: Develop and maintain tools and frameworks that enhance AI/ML model explainability and fairness, ensuring transparency and ethical use of models.
• Strategic Collaboration: Work closely with senior management to develop and implement ambitious, innovative modeling solutions, ensuring their successful deployment into production environments.
• Cross-Functional Partnership: Collaborate with diverse teams, including risk, technology, model governance, and research, throughout the entire modeling lifecycle—from development and review to deployment and operational use.
Qualifications:
Required:
• Ph.D. or Master’s degree from a reputable institution in a quantitative discipline such as Computer Science, Mathematics, Statistics, Econometrics, or Engineering.
• 2 years of experience with data analysis in Python.
• Proven track record in designing, building, and deploying high-quality machine learning models in production environments, demonstrating a strong ability to translate theoretical concepts into practical applications.
• In-depth knowledge of advanced machine learning algorithms, including logistic regression, XGBoost, Deep Neural Networks (CNN and RNN), clustering, and recommendation systems, with expertise in model design, hyperparameter tuning, and responsible deployment practices.
• Demonstrated experience in model interpretability and explainability for complex models such as XGBoost and GBM; experience extending these methods to deep learning architectures (CNNs, RNNs, transformers) is a strong plus.
• Familiarity with large language models (LLMs) and their applications, including experience in fine-tuning, prompt engineering, and responsible deployment with appropriate safeguards, monitoring, and auditability.
• Proficiency in Python, TensorFlow, PyTorch, Spark, or Scala, coupled with experience in big data technologies such as Hadoop, AWS, and Hive, and familiarity with MLOps tooling that supports model monitoring, drift detection, and end-to-end auditability.
Preferred:
• Strong expertise, interest, and track record of performing cutting-edge research on Explainable AI (XAI) and LLM.
• Demonstrated expertise in data wrangling and model building on a distributed Spark computation environment (with stability, scalability and efficiency). GPU experience is desired.
• Strong ownership and execution; proven experience in implementing models in production.
Company:
With a history tracing its roots to 1799 in New York City, JPMorganChase is one of the world's oldest, largest, and best-known financial institutions—carrying forth the innovative spirit of our heritage firms in global operations across 100 markets. Founded in 2000, the company is headquartered in New York, USA, with a team of 10001+ employees. The company is currently Late Stage.

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