PyTorch AI Jobs

Discover the latest remote and onsite PyTorch AI roles across top active AI companies. Updated hourly.

Check out 362 new PyTorch AI roles opportunities posted on The Homebase

Data Scientist (Python & SQL) - Freelance AI Trainer

New
Top rated
Mindrift
Part-time
Full-time
Posted

As a Data Science AI Trainer at Mindrift, your responsibilities include designing original computational data science problems simulating real-world analytical workflows across various industries such as telecom, finance, government, e-commerce, and healthcare. You create problems requiring Python programming skills with libraries including pandas, numpy, scipy, sklearn, statsmodels, matplotlib, and seaborn. You ensure these problems are computationally intensive and not solvable by manual means within reasonable timeframes, develop problems that require complex reasoning chains in areas like data processing, statistical analysis, feature engineering, predictive modeling, and insight extraction. You create deterministic problems with reproducible answers, avoiding stochastic elements or requiring fixed random seeds, and base the problems on real business challenges like customer analytics, risk assessment, fraud detection, forecasting, optimization, and operational efficiency. You design end-to-end problems covering the entire data science pipeline from data ingestion, cleaning, exploratory data analysis, modeling, validation to deployment considerations and incorporate big data processing scenarios that require scalable computational methods. Part of your role also involves verifying solutions using Python with standard data science libraries and statistical methods and clearly documenting problem statements with realistic business contexts along with verified correct answers.

$34 / hour
Undisclosed
HOUR

(USD)

Portugal
Maybe global
Remote
Python
Pandas
NumPy
Scikit-learn
TensorFlow

Data Scientist (Python & SQL) - Freelance AI Trainer

New
Top rated
Mindrift
Part-time
Full-time
Posted

As a Data Science AI Trainer, responsibilities include designing original computational data science problems that simulate real-world analytical workflows across various industries such as telecom, finance, government, e-commerce, and healthcare. You will create problems requiring Python programming using libraries like pandas, numpy, scipy, sklearn, statsmodels, matplotlib, and seaborn. The problems must be computationally intensive and not solvable manually within reasonable timeframes. You will develop problems requiring non-trivial reasoning in data processing, statistical analysis, feature engineering, predictive modeling, and insight extraction. Problems should be deterministic with reproducible answers, avoiding stochastic elements or requiring fixed random seeds. These problems are based on real business challenges including customer analytics, risk assessment, fraud detection, forecasting, optimization, and operational efficiency. You will design end-to-end problems spanning the full data science pipeline from data ingestion to deployment considerations and incorporate big data processing scenarios needing scalable computational approaches. Verification of solutions using Python and standard data science libraries is required. Documentation of clear problem statements with realistic business contexts and providing verified correct answers is also part of the role.

$55 / hour
Undisclosed
HOUR

(USD)

Singapore
Maybe global
Remote
Python
Pandas
NumPy
Scikit-learn
TensorFlow

Data Scientist (Python & SQL) - Freelance AI Trainer

New
Top rated
Mindrift
Part-time
Full-time
Posted

As a Data Science AI Trainer, you will design original computational data science problems that simulate real-world analytical workflows across various industries such as telecom, finance, government, e-commerce, and healthcare. You will create problems that require Python programming to solve using libraries like pandas, numpy, scipy, sklearn, statsmodels, matplotlib, and seaborn, ensuring these problems are computationally intensive and cannot be solved manually within reasonable timeframes. Your tasks include developing problems requiring non-trivial reasoning chains in data processing, statistical analysis, feature engineering, predictive modeling, and insight extraction. You will create deterministic problems with reproducible answers by avoiding stochastic elements or using fixed random seeds, base these problems on real business challenges such as customer analytics, risk assessment, fraud detection, forecasting, optimization, and operational efficiency, and design end-to-end problems spanning the complete data science pipeline from data ingestion to deployment considerations. Additionally, you will incorporate big data processing scenarios that require scalable computational approaches, verify solutions using Python with standard data science libraries and statistical methods, and document problem statements clearly with realistic business contexts and provide verified correct answers.

$32 / hour
Undisclosed
HOUR

(USD)

South Africa
Maybe global
Remote
Python
Pandas
NumPy
Scikit-learn
SQL

Data Scientist (Python & SQL) - Freelance AI Trainer

New
Top rated
Mindrift
Part-time
Full-time
Posted

As a Data Science AI Trainer, responsibilities include designing original computational data science problems that simulate real-world analytical workflows across industries such as telecom, finance, government, e-commerce, and healthcare. Tasks involve creating problems that require Python programming to solve using libraries like pandas, numpy, scipy, sklearn, statsmodels, matplotlib, and seaborn. Problems must be computationally intensive and not solvable manually within reasonable timeframes. The role requires developing problems that involve non-trivial reasoning chains in data processing, statistical analysis, feature engineering, predictive modeling, and insight extraction. Problems must be deterministic with reproducible answers, avoiding stochastic elements or requiring fixed random seeds. Design problems should be based on real business challenges such as customer analytics, risk assessment, fraud detection, forecasting, optimization, and operational efficiency. The trainer must design end-to-end problems spanning the complete data science pipeline including data ingestion, cleaning, exploratory data analysis, modeling, validation, and deployment considerations. Incorporation of big data processing scenarios requiring scalable computational approaches is needed. Additionally, solutions must be verified using Python with standard data science libraries and statistical methods. Problem statements should be clearly documented with realistic business contexts and verified correct answers must be provided.

$15 / hour
Undisclosed
HOUR

(USD)

India
Maybe global
Remote
Python
Pandas
NumPy
Scikit-learn
SQL

Data Scientist (Python & SQL) - Freelance AI Trainer

New
Top rated
Mindrift
Part-time
Full-time
Posted

As a Data Science AI Trainer at Mindrift, you will design original computational data science problems simulating real-world analytical workflows across various industries such as telecom, finance, government, e-commerce, and healthcare. You will create problems requiring Python programming using libraries like pandas, numpy, scipy, sklearn, statsmodels, matplotlib, and seaborn. The problems must be computationally intensive and unsolvable manually within reasonable timeframes. You will develop problems involving non-trivial reasoning in data processing, statistical analysis, feature engineering, predictive modeling, and insight extraction, ensuring they are deterministic with reproducible answers and based on real business challenges like customer analytics, risk assessment, fraud detection, forecasting, optimization, and operational efficiency. You will design problems covering the entire data science pipeline from data ingestion to deployment considerations, including big data processing and scalable computational approaches. You will verify solutions using Python and document problem statements with realistic business contexts and provide verified answers.

$58 / hour
Undisclosed
HOUR

(USD)

Ireland
Maybe global
Remote
Python
Pandas
NumPy
Scikit-learn
TensorFlow

Data Scientist (Python & SQL) - Freelance AI Trainer

New
Top rated
Mindrift
Part-time
Full-time
Posted

As a Data Science AI Trainer at Mindrift, you will design original computational data science problems that simulate real-world analytical workflows in various industries such as telecom, finance, government, e-commerce, and healthcare. You will create problems that require Python programming using libraries like pandas, numpy, scipy, sklearn, statsmodels, matplotlib, and seaborn, ensuring these problems are computationally intensive and cannot be solved manually within reasonable timeframes. Your tasks include developing problems that require non-trivial reasoning chains involving data processing, statistical analysis, feature engineering, predictive modeling, and insight extraction. You will create deterministic problems with reproducible answers based on real business challenges, including customer analytics, risk assessment, fraud detection, forecasting, optimization, and operational efficiency. You will design end-to-end problems spanning the entire data science pipeline from data ingestion to deployment considerations, incorporating big data processing scenarios that require scalable computational approaches. You will verify solutions using Python with standard data science libraries and statistical methods, and you will document problem statements clearly with realistic business contexts while providing verified correct answers.

$55 / hour
Undisclosed
HOUR

(USD)

Canada
Maybe global
Remote
Python
Pandas
NumPy
Scikit-learn
SQL

Data Scientist (Python & SQL) - Freelance AI Trainer

New
Top rated
Mindrift
Part-time
Full-time
Posted

As a Data Science AI Trainer at Mindrift, you will design original computational data science problems that simulate real-world analytical workflows across various industries such as telecom, finance, government, e-commerce, and healthcare. You will create problems requiring Python programming using libraries like pandas, numpy, scipy, sklearn, statsmodels, matplotlib, and seaborn, ensuring these problems are computationally intensive and cannot be solved manually within reasonable timeframes. Your responsibilities include developing problems that require non-trivial reasoning in data processing, statistical analysis, feature engineering, predictive modeling, and insight extraction, while ensuring deterministic problems with reproducible answers by avoiding stochastic elements or requiring fixed random seeds. You will base problems on real business challenges including customer analytics, risk assessment, fraud detection, forecasting, optimization, and operational efficiency. Additionally, you will design end-to-end problems covering the complete data science pipeline from data ingestion, cleaning, exploratory data analysis, modeling, validation, to deployment considerations, and incorporate big data processing scenarios requiring scalable computational approaches. Verification of solutions using Python with standard data science libraries and statistical methods, as well as clear documentation of problem statements with realistic business contexts and verified correct answers, are also part of your duties.

$58 / hour
Undisclosed
HOUR

(USD)

Germany
Maybe global
Remote
Python
Pandas
NumPy
Scikit-learn
SQL

Data Scientist (Python & SQL) - Freelance AI Trainer

New
Top rated
Mindrift
Part-time
Full-time
Posted

As a Data Science AI Trainer, you will design original computational data science problems that simulate real-world analytical workflows across various industries such as telecom, finance, government, e-commerce, and healthcare. You will create problems that require Python programming to solve, using libraries like pandas, numpy, scipy, sklearn, statsmodels, matplotlib, and seaborn. Your tasks include ensuring problems are computationally intensive and cannot be solved manually within reasonable timeframes, developing problems requiring non-trivial reasoning chains in data processing, statistical analysis, feature engineering, predictive modeling, and insight extraction. You will create deterministic problems with reproducible answers, base problems on real business challenges such as customer analytics, risk assessment, fraud detection, forecasting, optimization, and operational efficiency. You will design end-to-end problems covering the complete data science pipeline from data ingestion to deployment considerations, incorporate big data processing scenarios requiring scalable computational approaches, verify solutions using Python with standard data science libraries and statistical methods, and document problem statements clearly with realistic business contexts and verified correct answers.

$34 / hour
Undisclosed
HOUR

(USD)

Italy
Maybe global
Remote
Python
Pandas
NumPy
Scikit-learn
SQL

Senior Python Systems Developer - Functional Testing Project

New
Top rated
Mindrift
Part-time
Full-time
Posted

Create functional black box tests for large codebases in various source languages, create and manage Docker environments to ensure 100% reproducible builds and test execution across different platforms, monitor code coverage and configure automated scoring criteria to meet industry benchmark-level standards, and leverage LLMs such as Roo Code and Claude to accelerate development cycles, automate repetitive tasks, and improve overall code quality.

$50 / hour
Undisclosed
HOUR

(USD)

Germany
Maybe global
Remote
Python
Docker
Linux
Go
C++

Data Scientist (Python & SQL) - Freelance AI Trainer

New
Top rated
Mindrift
Part-time
Full-time
Posted

As a Data Science AI Trainer, responsibilities include designing original computational data science problems that simulate real-world analytical workflows across various industries such as telecom, finance, government, e-commerce, and healthcare. The role involves creating problems requiring Python programming using libraries like pandas, numpy, scipy, sklearn, statsmodels, matplotlib, and seaborn, and ensuring these problems are computationally intensive and cannot be solved manually within reasonable timeframes. The trainer develops problems requiring non-trivial reasoning chains in data processing, statistical analysis, feature engineering, predictive modeling, and insight extraction, creates deterministic problems with reproducible answers, and bases them on real business challenges including customer analytics, risk assessment, fraud detection, forecasting, optimization, and operational efficiency. Responsibilities also include designing end-to-end problems spanning the complete data science pipeline, incorporating big data processing scenarios requiring scalable computational approaches, verifying solutions using Python with standard data science libraries and statistical methods, and documenting problem statements clearly with realistic business contexts and verified correct answers.

$58 / hour
Undisclosed
HOUR

(USD)

France
Maybe global
Remote
Python
Pandas
NumPy
Scikit-learn
TensorFlow

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[{"question":"What are PyTorch AI jobs?","answer":"PyTorch AI jobs focus on building, training, and deploying deep learning models for applications like computer vision, natural language processing, and generative AI. These positions involve creating custom neural networks, research prototyping with dynamic computation graphs, and transitioning models to production using tools like TorchScript and TorchServe. These roles typically exist in research labs, tech companies, and AI-driven startups."},{"question":"What roles commonly require PyTorch skills?","answer":"Roles that commonly require PyTorch skills include AI researchers, machine learning engineers, data scientists, and deep learning specialists. These professionals develop custom neural networks, implement computer vision solutions, create NLP models, and design predictive analytics systems. They often work on research prototyping and transitioning models to production environments through REST APIs or cloud platforms."},{"question":"What skills are typically required alongside PyTorch?","answer":"Python programming is essential as the framework is deeply integrated with the language. Professionals also need strong foundations in deep learning concepts, familiarity with neural network architectures like CNNs and RNNs, and experience with NumPy. Additional valuable skills include GPU programming with CUDA, distributed training techniques, cloud platforms integration, and knowledge of deployment tools like TorchServe and ONNX Runtime."},{"question":"What experience level do PyTorch AI jobs usually require?","answer":"PyTorch AI jobs span from entry-level to senior positions. Entry roles typically require fundamental Python and deep learning knowledge. Mid-level positions demand practical experience building and deploying models using the framework. Senior roles require extensive experience with complex architectures, distributed training, production deployment, and often specialization in areas like computer vision or NLP."},{"question":"What is the salary range for PyTorch AI jobs?","answer":"Salaries for PyTorch AI jobs vary based on location, experience level, industry, and specific role. Machine learning engineers and AI researchers using this framework typically earn competitive compensation reflecting their specialized skills. Roles involving advanced model development for computer vision, NLP, or generative AI, especially in major tech hubs, command premium compensation packages."},{"question":"Are PyTorch AI jobs in demand?","answer":"PyTorch AI jobs are in high demand across both academia and industry. The framework has gained widespread adoption for cutting-edge research and commercial applications. Many companies seek specialists who can prototype and deploy deep learning models using its dynamic computation graphs. Major cloud providers like Azure, AWS, and Google Cloud have integrated support, further increasing demand for these skills in production environments."},{"question":"What is the difference between PyTorch and TensorFlow in AI roles?","answer":"PyTorch uses dynamic computation graphs allowing for flexible, iterative development and easier debugging, making it popular in research. TensorFlow traditionally used static graphs optimized for production deployment. AI roles focused on research prototyping often prefer PyTorch for its pythonic interface, while production-focused teams might use TensorFlow. However, both frameworks now support both dynamic and static approaches, with the gap narrowing as they evolve."}]