1 to 25 of 651 PyTorch Jobs

field. 7+ years of professional software development experience, with at least 3 years in AI/ML. Strong proficiency in Python , including libraries like NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch . Solid understanding of ML algorithms , NLP , deep learning , and statistical methods. Experience with Docker, Kubernetes , and cloud platforms like AWS/Azure/GCP . Hands-on experience with More ❯

the language) and in building data pipelines and ML models using modern libraries across multiple domains: Data science stack: NumPy, pandas/polars, scikit-learn, XGBoost, LightGBM Deep learning: PyTorch, JAX Statistical programming: NumPyro, PyMC Data skills: Proficient in SQL, with the ability to write efficient, maintainable queries and manage data pipelines for analytics and modeling workflows. ML Ops & deployment More ❯

the language) and in building data pipelines and ML models using modern libraries across multiple domains: Data science stack: NumPy, pandas/polars, scikit-learn, XGBoost, LightGBM Deep learning: PyTorch, JAX Statistical programming: NumPyro, PyMC Data skills: Proficient in SQL, with the ability to write efficient, maintainable queries and manage data pipelines for analytics and modeling workflows. ML Ops & deployment More ❯

software engineering and machine learning/AI engineering roles (building, deploying, operating machine learning models and AI systemsExpertise in programming languages such as Python, and framework experience (e.g., TensorFlow, PyTorch, scikit-learn, KerasProven track record in designing, building, deploying production-grade ML/AI systems in real-world settings.Deep experience in feature engineering, model development, tuning, validation, and model evaluation More ❯

lifecycle. Expertise taking projects from ideation or experimental Jupyter notebooks to full production deployment. Strong programming skills in Python, with familiarity in ML libraries/frameworks such as TensorFlow, PyTorch, and Scikit-learn. Experience with MLOps practices including model drift detection, decay, A/B testing, integration testing, differential testing, Python package building, and code version control. Skilled in data More ❯

Science , or a related technical field. Strong understanding of core ML concepts (supervised/unsupervised learning, neural networks, NLP, etc.). Hands-on experience with frameworks such as TensorFlow , PyTorch , or scikit-learn . Proficiency in Python and familiarity with common data science libraries (NumPy, pandas, etc.). Solid grasp of statistics, linear algebra, and probability. Excellent problem-solving skills More ❯

challenges. Proficiency in designing, implementing, and maintaining MLOps processes in a cloud environment (e.g., Azure, AWS, GCP). Technical Skills: Expertise in Python and its ML ecosystem (e.g., TensorFlow, PyTorch, Scikit-learn, Pandas, NumPy). Strong background in statistical analysis, algorithm design, and software engineering best practices. Experience with Docker and Kubernetes for containerization and orchestration. Proficiency with modern version More ❯

a strong portfolio of high-impact projects in production Expert-level programming skills in Python and SQL, and fluency with leading ML/AI frameworks (e.g., scikit-learn, TensorFlow, PyTorch) Direct experience with GenAI/LLM technologies, including tools like Hugging Face, LangChain, OpenAI APIs, vector databases, and fine-tuning methods Deep knowledge of machine learning algorithms (supervised, unsupervised, deep More ❯

developing and deploying AI/ML solutions, with a strong focus on Microsoft Azure. • Proficiency in Python and experience with relevant AI/ML libraries (e.g., scikit-learn, TensorFlow, PyTorch, Keras). • Demonstrated experience with Azure Machine Learning, including MLOps concepts, model registration, deployment (AKS, ACI), and monitoring. • Hands-on experience with Azure data services such as Azure Data Lake More ❯

developing and deploying AI/ML solutions, with a strong focus on Microsoft Azure. • Proficiency in Python and experience with relevant AI/ML libraries (e.g., scikit-learn, TensorFlow, PyTorch, Keras). • Demonstrated experience with Azure Machine Learning, including MLOps concepts, model registration, deployment (AKS, ACI), and monitoring. • Hands-on experience with Azure data services such as Azure Data Lake More ❯

or related field. - 3+ years in ML, DevOps, or MLOps with hands-on experience deploying ML products. - Python, Go, Rust, or similar programming languages - ML/DL frameworks (TensorFlow, PyTorch, scikit-learn, etc.) - DevOps practices, CI/CD, IaC, Docker, Kubernetes - Version control (Git) and collaborative development tools - Data engineering and ETL workflows - Monitoring/logging tools (Prometheus, Grafana, ELK More ❯

Demonstrated ability to implement, validate, and improve supervised and unsupervised learning models. Excellent knowledge of Excel, Python, SQL, and database systems. Familiarity with machine learning frameworks such as TensorFlow, PyTorch, scikit-learn, or related frameworks. Experience in evaluation metrics, model selection, and validation techniques such as cross-validation, bootstrapping, and assessing the statistical significance of model improvements. Excellent knowledge of More ❯

asyncio for concurrent processing, and building robust automation scripts with proper logging, testing (pytest), and documentation. AI & Machine Learning Frameworks - Deep expertise in AI/ML frameworks including TensorFlow, PyTorch, Scikit-learn, and Hugging Face Transformers. Experience building, training, and deploying machine learning models for classification, regression, clustering, and NLP tasks. Understanding of model evaluation metrics, hyperparameter tuning, feature engineering More ❯

asyncio for concurrent processing, and building robust automation scripts with proper logging, testing (pytest), and documentation. AI & Machine Learning Frameworks - Deep expertise in AI/ML frameworks including TensorFlow, PyTorch, Scikit-learn, and Hugging Face Transformers. Experience building, training, and deploying machine learning models for classification, regression, clustering, and NLP tasks. Understanding of model evaluation metrics, hyperparameter tuning, feature engineering More ❯

experience in data science roles, with proven expertise in: Machine learning (supervised/unsupervised), time series forecasting, deep learning. Programming in Python and SQL; proficiency with ML frameworks (TensorFlow, PyTorch, scikit-learn). Big data technologies and cloud platforms (AWS, Azure, or GCP). Track record of deploying models into production and measuring business impact. Strong problem-solving skills and More ❯

experience in data science roles, with proven expertise in: Machine learning (supervised/unsupervised), time series forecasting, deep learning. Programming in Python and SQL; proficiency with ML frameworks (TensorFlow, PyTorch, scikit-learn). Big data technologies and cloud platforms (AWS, Azure, or GCP). Track record of deploying models into production and measuring business impact. Strong problem-solving skills and More ❯

Science, Engineering, Mathematics, or a related discipline Demonstrated project experience (academic research, dissertation work, or personal projects) applying machine learning or AI techniques Strong programming skills in Python (e.g. PyTorch, TensorFlow, scikit-learn, pandas, NumPy) Familiarity with cloud platforms (AWS, Azure, or GCP) and basic distributed systems concepts Strong problem-solving mindset with a passion for building practical, scalable AI More ❯

Science, Engineering, Mathematics, or a related discipline Demonstrated project experience (academic research, dissertation work, or personal projects) applying machine learning or AI techniques Strong programming skills in Python (e.g. PyTorch, TensorFlow, scikit-learn, pandas, NumPy) Familiarity with cloud platforms (AWS, Azure, or GCP) and basic distributed systems concepts Strong problem-solving mindset with a passion for building practical, scalable AI More ❯

Functions, etc. Proven expertise in MLOps implementation for deploying, monitoring, and managing ML models in production environments. Proficiency in Python and experience with ML frameworks and libraries (e.g., TensorFlow, PyTorch, Scikit-learn, Pandas, NumPy Strong understanding of cloud technologies and AI/ML platforms, particularly AWS SageMaker. Solid grasp of software engineering principles including design patterns, testing, CI/CD More ❯

multi-agent systems using AutoGen, CrewAI , or similar frameworks. Solid understanding of vector embeddings , similarity search, and experience with vector databases. Proficiency in Python and core ML libraries (e.g., PyTorch, TensorFlow, Scikit-learn, Hugging Face). More ❯

for sensitive environments. Collaborate across engineering, security, and product teams to deliver at pace and scale. The toolkit you’ll use 🌳 Data Science & Engineering: Python (NumPy, Pandas, scikit-learn, PyTorch/TensorFlow), SQL, NoSQL, Spark, big data ecosystems Visualisation & APIs: REST/JSON, Postman, Flask/FastAPI, Power BI/Tableau, D3.js DevOps & Cloud: CI/CD, Docker, AWS (S3 More ❯

for sensitive environments. Collaborate across engineering, security, and product teams to deliver at pace and scale. The toolkit you’ll use 🌳 Data Science & Engineering: Python (NumPy, Pandas, scikit-learn, PyTorch/TensorFlow), SQL, NoSQL, Spark, big data ecosystems Visualisation & APIs: REST/JSON, Postman, Flask/FastAPI, Power BI/Tableau, D3.js DevOps & Cloud: CI/CD, Docker, AWS (S3 More ❯

model deployment and monitoring tools, version control, and orchestration frameworks (e.g., Docker, Kubernetes, MLflow, CI/CD pipelines). Experience with Python and common ML libraries (scikit-learn, TensorFlow, PyTorch, XGBoost) and data-processing tools (SQL, Spark). Typical Education & Experience Experienced (Level 3) Education/experience typically acquired through advanced education (e.g. Associate) and typically 3 or more years More ❯

model deployment and monitoring tools, version control, and orchestration frameworks (e.g., Docker, Kubernetes, MLflow, CI/CD pipelines). Experience with Python and common ML libraries (scikit-learn, TensorFlow, PyTorch, XGBoost) and data-processing tools (SQL, Spark). Typical Education & Experience Experienced (Level 3) Education/experience typically acquired through advanced education (e.g. Associate) and typically 3 or more years More ❯

associated concepts, such as transformer architecture and retrieval augmented generation. Strong programming ability with demonstrated experience in Python and one or more associated machine learning frameworks, such as TensorFlow, PyTorch, or SKLearn. Knowledge of and experience working with open-source AI models. Demonstrated ability to perform the essential duties of the position with or without accommodation. Authorization to work in More ❯