1 to 25 of 457 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 ❯

or University with a strong quantitative curriculum is highly valued. Requirements 8+ years of experience building, training, and evaluating Deep Learning and Machine Learning models using tools such as PyTorch , TensorFlow , scikit learn , HuggingFace , or LangChain . Experience in a start up or a cross functional team is a plus Experience in Natural Language Processing (NLP) is a plus Strong 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 ❯

platform scale. Proven track record of leading ML engineering projects from architecture to deployment, including ownership of production-grade systems. Deep expertise in ML frameworks and engineering stacks (TensorFlow, PyTorch, JAX, Ray, MLflow, Kubeflow). Proficiency in Python and at least one backend language (e.g., Java, Scala, Go, C++). Strong understanding of cloud ML infrastructure (AWS SageMaker, GCP Vertex More ❯

Experience with predictive modeling techniques such as regression, classification, clustering, or time-series forecasting. Proficiency in Python and experience with data science libraries (e.g., Pandas, NumPy, scikit-learn, XGBoost, PyTorch, TensorFlow). Strong experience with SQL and data manipulation across large datasets. Familiarity with data visualization tools (e.g., Matplotlib, Seaborn, Plotly, Tableau, or Power BI). Exposure to modern collaborative More ❯

from ideation to delivery, including business scoping and stakeholder management. Strong proficiency in Python (or R), with deep experience using modern data science libraries (e.g., Pandas, NumPy, scikit-learn, PyTorch, TensorFlow, Statsmodels). Solid foundation in SQL and data wrangling across large, complex datasets. Hands-on experience with experimentation platforms, data visualization, and dashboarding tools (e.g., Tableau, Power BI, Plotly 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 ❯

Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related field (or equivalent experience). Technical Skills: Proficiency in Python and deep learning frameworks (e.g., TensorFlow, PyTorch). Strong understanding of NLP techniques, including tokenization, embeddings, transformers, and attention mechanisms. Experience in retraining and fine-tuning LLMs using large-scale datasets. Familiarity with cloud platforms like AWS More ❯

experience delivering AI/ML solutions. Familiarity with OpenAI, Claude Sonnet, Claude Opus, and other AI engines. Strong Python proficiency and familiarity with AI/ML libraries (e.g., TensorFlow, PyTorch, scikit-learn). Understanding of supervised and unsupervised learning techniques. Experience working with cloud platforms (AWS, Azure, or GCP) and container tools (Docker, Kubernetes). Familiarity with CI/CD More ❯

experience delivering AI/ML solutions. Familiarity with OpenAI, Claude Sonnet, Claude Opus, and other AI engines. Strong Python proficiency and familiarity with AI/ML libraries (e.g., TensorFlow, PyTorch, scikit-learn). Understanding of supervised and unsupervised learning techniques. Experience working with cloud platforms (AWS, Azure, or GCP) and container tools (Docker, Kubernetes). Familiarity with CI/CD More ❯

experience delivering AI/ML solutions. Familiarity with OpenAI, Claude Sonnet, Claude Opus, and other AI engines. Strong Python proficiency and familiarity with AI/ML libraries (e.g., TensorFlow, PyTorch, scikit-learn). Understanding of supervised and unsupervised learning techniques. Experience working with cloud platforms (AWS, Azure, or GCP) and container tools (Docker, Kubernetes). Familiarity with CI/CD More ❯

experience delivering AI/ML solutions. Familiarity with OpenAI, Claude Sonnet, Claude Opus, and other AI engines. Strong Python proficiency and familiarity with AI/ML libraries (e.g., TensorFlow, PyTorch, scikit-learn). Understanding of supervised and unsupervised learning techniques. Experience working with cloud platforms (AWS, Azure, or GCP) and container tools (Docker, Kubernetes). Familiarity with CI/CD 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 ❯

GenAI applications. The work you could be doing • Design and deploy machine learning models for fraud detection, credit risk, customer segmentation, and behavioural analytics using scalable frameworks like TensorFlow, PyTorch, and XGBoost. • Engineer robust data pipelines and ML workflows using Apache Spark, Vertex AI, and CI/CD tooling to ensure seamless model delivery and monitoring. • Apply advanced techniques in 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 ❯