276 to 300 of 775 PyTorch Jobs

similarity search, guardrails, model evaluation, experimentation, governance, and observability, etc. Leverage a broad stack of Open Source and SaaS AI technologies such as AWS Ultraclusters, Huggingface, VectorDBs, Nemo Guardrails, PyTorch, and more. Invent and introduce state-of-the-art LLM optimization techniques to improve the performance - scalability, cost, latency, throughput - of large scale production AI systems. Contribute to the technical More ❯

similarity search, guardrails, model evaluation, experimentation, governance, and observability, etc. Leverage a broad stack of Open Source and SaaS AI technologies such as AWS Ultraclusters, Huggingface, VectorDBs, Nemo Guardrails, PyTorch, and more. Invent and introduce state-of-the-art LLM optimization techniques to improve the performance - scalability, cost, latency, throughput - of large scale production AI systems. Contribute to the technical More ❯

similarity search, guardrails, model evaluation, experimentation, governance, and observability, etc. Leverage a broad stack of Open Source and SaaS AI technologies such as AWS Ultraclusters, Huggingface, VectorDBs, Nemo Guardrails, PyTorch, and more. Invent and introduce state-of-the-art LLM optimization techniques to improve the performance - scalability, cost, latency, throughput - of large scale production AI systems. Contribute to the technical More ❯

similarity search, guardrails, model evaluation, experimentation, governance, and observability, etc. Leverage a broad stack of Open Source and SaaS AI technologies such as AWS Ultraclusters, Huggingface, VectorDBs, Nemo Guardrails, PyTorch, and more. Invent and introduce state-of-the-art LLM optimization techniques to improve the performance - scalability, cost, latency, throughput - of large scale production AI systems. Contribute to the technical More ❯

similarity search, guardrails, model evaluation, experimentation, governance, and observability, etc. Leverage a broad stack of Open Source and SaaS AI technologies such as AWS Ultraclusters, Huggingface, VectorDBs, Nemo Guardrails, PyTorch, and more. Invent and introduce state-of-the-art LLM optimization techniques to improve the performance - scalability, cost, latency, throughput - of large scale production AI systems. Contribute to the technical More ❯

similarity search, guardrails, model evaluation, experimentation, governance, and observability, etc. Leverage a broad stack of Open Source and SaaS AI technologies such as AWS Ultraclusters, Huggingface, VectorDBs, Nemo Guardrails, PyTorch, and more. Invent and introduce state-of-the-art LLM optimization techniques to improve the performance - scalability, cost, latency, throughput - of large scale production AI systems. Contribute to the technical More ❯

similarity search, guardrails, model evaluation, experimentation, governance, and observability, etc. Leverage a broad stack of Open Source and SaaS AI technologies such as AWS Ultraclusters, Huggingface, VectorDBs, Nemo Guardrails, PyTorch, and more. Invent and introduce state-of-the-art LLM optimization techniques to improve the performance - scalability, cost, latency, throughput - of large scale production AI systems. Contribute to the technical More ❯

similarity search, guardrails, model evaluation, experimentation, governance, and observability, etc. Leverage a broad stack of Open Source and SaaS AI technologies such as AWS Ultraclusters, Huggingface, VectorDBs, Nemo Guardrails, PyTorch, and more. Invent and introduce state-of-the-art LLM optimization techniques to improve the performance - scalability, cost, latency, throughput - of large scale production AI systems. Contribute to the technical More ❯

intelligence and its application to drug discovery Understanding of ligand- and structure-based drug design. Experience with machine learning and major machine learning frameworks such as scikit-learn or PyTorch Proficiency in a low-level programming language such as C/C++ or Rust. Knowledge of advanced data analysis techniques such as multi-parameter optimization Open source contributions or publications More ❯

AI Engineer will have proven experience developing and deploying machine learning models in a commercial or scientific environment.* Proficient in Python and familiar with key libraries such as TensorFlow, PyTorch, and scikit-learn.* Experience working with cloud platforms such as Azure, AWS, or GCP, ideally within a data engineering or MLOps context.* Strong understanding of data processing, feature engineering, and More ❯

current with emerging research, tools, and trends in Artificial Intelligence. ---------------------------------------- Skills, Knowledge, and Experience Strong proficiency in Python and experience writing scalable, production-ready code. Hands-on experience with PyTorch, Transformer architectures, and Large Language Models (LLMs). Demonstrated success delivering Computer Vision and/or NLP/LLM projects into production. Solid understanding of model deployment, pipelines, and software More ❯

learning architectures. Strong understanding of two-tower neural networks, embedding techniques, and ranking models. Proficiency in Python with familiarity to ML libraries e.g. pandas, numpy, scipy, scikit-learn, tensorflow, pytorch) Familiarity with cloud platforms (GCP, AWS, Azure) and tools like Dataiku. Experience with ML Ops, including model deployment, monitoring, and retraining pipelines. Ability to work cross-functionally with marketing, CRM More ❯

learning architectures. Strong understanding of two-tower neural networks, embedding techniques, and ranking models. Proficiency in Python with familiarity to ML libraries e.g. pandas, numpy, scipy, scikit-learn, tensorflow, pytorch) Familiarity with cloud platforms (GCP, AWS, Azure) and tools like Dataiku. Experience with ML Ops, including model deployment, monitoring, and retraining pipelines. Ability to work cross-functionally with marketing, CRM More ❯

learning architectures. Strong understanding of two-tower neural networks, embedding techniques, and ranking models. Proficiency in Python with familiarity to ML libraries e.g. pandas, numpy, scipy, scikit-learn, tensorflow, pytorch) Familiarity with cloud platforms (GCP, AWS, Azure) and tools like Dataiku. Experience with ML Ops, including model deployment, monitoring, and retraining pipelines. Ability to work cross-functionally with marketing, CRM More ❯

learning architectures. Strong understanding of two-tower neural networks, embedding techniques, and ranking models. Proficiency in Python with familiarity to ML libraries e.g. pandas, numpy, scipy, scikit-learn, tensorflow, pytorch) Familiarity with cloud platforms (GCP, AWS, Azure) and tools like Dataiku. Experience with ML Ops, including model deployment, monitoring, and retraining pipelines. Ability to work cross-functionally with marketing, CRM More ❯

learning architectures. Strong understanding of two-tower neural networks, embedding techniques, and ranking models. Proficiency in Python with familiarity to ML libraries e.g. pandas, numpy, scipy, scikit-learn, tensorflow, pytorch) Familiarity with cloud platforms (GCP, AWS, Azure) and tools like Dataiku. Experience with ML Ops, including model deployment, monitoring, and retraining pipelines. Ability to work cross-functionally with marketing, CRM More ❯

learning architectures. Strong understanding of two-tower neural networks, embedding techniques, and ranking models. Proficiency in Python with familiarity to ML libraries e.g. pandas, numpy, scipy, scikit-learn, tensorflow, pytorch) Familiarity with cloud platforms (GCP, AWS, Azure) and tools like Dataiku. Experience with ML Ops, including model deployment, monitoring, and retraining pipelines. Ability to work cross-functionally with marketing, CRM More ❯

solutions independently. Ideal Experience 5+ years in applied Machine Learning, with significant hands-on NLP project experience. Deep understanding of transformer models and LLM fine-tuning. Proficient in Python, PyTorch or TensorFlow, and scikit-learn. Practical experience deploying ML models in production environments (cloud-native or on-prem). Familiar with data versioning, MLflow, and monitoring frameworks (e.g. Weights & Biases More ❯

Working with Data Scientists to deploy trained machine learning models into production environments Working with a range of models developed using common frameworks such as Scikit-learn, TensorFlow, or PyTorch Experience with software engineering best practices and developing applications in Python. Technical experience of cloud architecture, security, deployment, and open-source tools ideally with one of the 3 major cloud More ❯

Working with Data Scientists to deploy trained machine learning models into production environments Working with a range of models developed using common frameworks such as Scikit-learn, TensorFlow, or PyTorch Experience with software engineering best practices and developing applications in Python. Technical experience of cloud architecture, security, deployment, and open-source tools ideally with one of the 3 major cloud More ❯

Working with Data Scientists to deploy trained machine learning models into production environments Working with a range of models developed using common frameworks such as Scikit-learn, TensorFlow, or PyTorch Experience with software engineering best practices and developing applications in Python. Technical experience of cloud architecture, security, deployment, and open-source tools ideally with one of the 3 major cloud More ❯

Working with Data Scientists to deploy trained machine learning models into production environments Working with a range of models developed using common frameworks such as Scikit-learn, TensorFlow, or PyTorch Experience with software engineering best practices and developing applications in Python. Technical experience of cloud architecture, security, deployment, and open-source tools ideally with one of the 3 major cloud More ❯

with Python. Rust and C++ are nice to have. 3+ years designing and building data-intensive solutions with distributed computing. 2+ years with industry-recognized ML frameworks (scikit-learn, PyTorch, TensorFlow, Spark, Ray). 1+ year productionizing, monitoring, and maintaining models. Bachelor's degree or equivalent work experience. Preferred Qualifications: 1+ years building, scaling, and optimizing ML systems. 1+ years More ❯

functionality of roles WHAT WE NEED FROM YOU: Required: 7+ Years of professional work experience in Python software development Extensive experience with core AI/ML libraries (e.g., TensorFlow, PyTorch, scikit-learn, Pandas, XGBoost) Experience with Python RESTful APIs using Fast API, ensuring effective communication between backend systems and applications Experience with relational and/or NoSQL databases (e.g., SQL More ❯

sequential (e.g. sensor time-series) data and decision-making post PhD Experience in software development for proof of concept in Python Experience with machine and deep learning frameworks: TensorFlow, PyTorch, scikit-learn, etc. Of particular interest are candidates with experience in one or more of the following domains: RF communications and CEMA Electronic or Electromagnetic Warfare (EW) Tracking and sensor More ❯