151 to 175 of 404 PyTorch Jobs

designing, training, and evaluating neural models for speech or language generation. Strong understanding of large language models (LLMs), transformers, and sequence-to-sequence learning. Advanced proficiency in Python and PyTorch, with practical experience in model training and optimisation at scale. Solid software engineering practices: Git, unit testing, and CI/CD pipelines. Proven ability to conduct independent research, troubleshoot complex More ❯

3+ years in Data Science/Analytics (or 2+ with a strong portfolio) delivering models into production. Strong Python (pandas/numpy/scikit-learn; XGBoost/LightGBM; basic PyTorch a plus) and SQL. Solid geospatial skills (PostGIS/GeoPandas/QGIS) and time-series/forecasting know-how. ETL/ELT and data wrangling at scale; comfort with scraping More ❯

designing, training, and evaluating neural models for speech or language generation. Strong understanding of large language models (LLMs), transformers, and sequence-to-sequence learning. Advanced proficiency in Python and PyTorch, with practical experience in model training and optimisation at scale. Solid software engineering practices: Git, unit testing, and CI/CD pipelines. Proven ability to conduct independent research, troubleshoot complex More ❯

designing, training, and evaluating neural models for speech or language generation. Strong understanding of large language models (LLMs), transformers, and sequence-to-sequence learning. Advanced proficiency in Python and PyTorch, with practical experience in model training and optimisation at scale. Solid software engineering practices: Git, unit testing, and CI/CD pipelines. Proven ability to conduct independent research, troubleshoot complex More ❯

our custom-built AWS hardware. More specifically, the AWS Neuron team is developing a deep learning compiler stack that takes neural network descriptions created in frameworks such as TensorFlow, PyTorch, and Jax, and converts them into code suitable for execution. As a Sr. ML Compiler Engineer, you will be responsible for identifying and designing solutions that enable efficient and reliable More ❯

Computer Science, Machine Learning, Artificial Intelligence, or a related field. Deep expertise in training foundation models, including multimodal LLMs and video models, with hands-on experience in frameworks like PyTorch, TensorFlow, or JAX. Strong understanding of scalable training techniques, such as data parallelism, model parallelism, and efficient inference on edge devices (e.g., mobile, IoT). Demonstrated ability to scale teams More ❯

providers, including knowledge of running cost-effective serverless architecture. Experience working with Python, C#, and Angular. Strong interpersonal, communication, and presentation skills applicable to a wide audience. Experience with PyTorch, Scikit-learn, Go, Databricks, JavaScript, and Azure Pipelines is desirable, but not essential. Experience in leading software engineering efforts for AI-enabled SaaS products is desirable, but not essential. Why More ❯

Computing, Computer Vision, or related technical discipline. Good knowledge of machine learning and computer vision algorithms. Familiarity with real-time imaging pipelines. Experience with scientific software packages such as PyTorch, OpenCV, Pandas, SciPy, NumPy, SciKit's, etc. Experience in standard software engineering practices including version control systems and software testing methodologies. Excellent oral and written communication skills. Analytical thinker, attentive More ❯

grade recommendation or personalization systems Strong understanding of collaborative filtering, deep learning for recommendations, reinforcement learning, and causal inference Proficiency in Python and ML frameworks such as TensorFlow or PyTorch Experience with distributed systems like Apache Spark Familiarity with A/B testing and experimentation methodologies Ability to work with large-scale, real-world data with attention to bias, fairness More ❯

and benchmarks, and deploying production-ready solutions) Familiarity in statistical methods for Machine Learning (e.g. Bayesian methods, HMMs, graphical models, dimension reduction, clustering, classification, regression techniques, etc.) Familiarity with PyTorch, PyTorch Lightning, or similar frameworks Strong coding abilities in Python Strong Software development skills and familiarity with GPUs, MLOps, Git, High-performance large-scale ML systems and platforms. Experience with More ❯

in Reinforcement Learning (policy optimisation, reward modelling, RLHF). Hands-on experience training/fine-tuning generative models (LLMs, diffusion, transformers, GANs). Strong knowledge of deep learning frameworks (PyTorch, JAX, TensorFlow). Proficiency in Python and standard ML libraries. Solid foundations in probability, optimisation, and statistics. Experience working with large-scale distributed training on GPUs/TPUs. If this More ❯

in Reinforcement Learning (policy optimisation, reward modelling, RLHF). Hands-on experience training/fine-tuning generative models (LLMs, diffusion, transformers, GANs). Strong knowledge of deep learning frameworks (PyTorch, JAX, TensorFlow). Proficiency in Python and standard ML libraries. Solid foundations in probability, optimisation, and statistics. Experience working with large-scale distributed training on GPUs/TPUs. If this More ❯

in Reinforcement Learning (policy optimisation, reward modelling, RLHF). Hands-on experience training/fine-tuning generative models (LLMs, diffusion, transformers, GANs). Strong knowledge of deep learning frameworks (PyTorch, JAX, TensorFlow). Proficiency in Python and standard ML libraries. Solid foundations in probability, optimisation, and statistics. Experience working with large-scale distributed training on GPUs/TPUs. If this More ❯

in Reinforcement Learning (policy optimisation, reward modelling, RLHF). Hands-on experience training/fine-tuning generative models (LLMs, diffusion, transformers, GANs). Strong knowledge of deep learning frameworks (PyTorch, JAX, TensorFlow). Proficiency in Python and standard ML libraries. Solid foundations in probability, optimisation, and statistics. Experience working with large-scale distributed training on GPUs/TPUs. If this More ❯

in Reinforcement Learning (policy optimisation, reward modelling, RLHF). Hands-on experience training/fine-tuning generative models (LLMs, diffusion, transformers, GANs). Strong knowledge of deep learning frameworks (PyTorch, JAX, TensorFlow). Proficiency in Python and standard ML libraries. Solid foundations in probability, optimisation, and statistics. Experience working with large-scale distributed training on GPUs/TPUs. If this More ❯

Start-up/Scale-up Experience Strong cloud skills (AWS, GCP, Azure) and containerisation (Docker, Kubernetes) Experience in automating deployments and orchestrating cloud environments Nice to have: Python (Jupyter, PyTorch), monitoring tools (Prometheus, Grafana), cloud databases (RDS, Aurora, Spanner), CI/CD tools (CircleCI), and data visualisation experience. This is a unique opportunity to join a visionary team redefining AI More ❯

impact in a senior data science role. Deep understanding of machine learning model lifecycles and optimisation. Skilled in building, training, and deploying models on large datasets. Proficiency in Python, PyTorch, Scikit-learn, and similar ML frameworks. Experience with cloud environments (AWS preferred), containerisation, and modern data infrastructure. Excellent communication skills, with the ability to work effectively across technical and non More ❯

impact in a senior data science role. Deep understanding of machine learning model lifecycles and optimisation. Skilled in building, training, and deploying models on large datasets. Proficiency in Python, PyTorch, Scikit-learn, and similar ML frameworks. Experience with cloud environments (AWS preferred), containerisation, and modern data infrastructure. Excellent communication skills, with the ability to work effectively across technical and non More ❯

etc.) for automation. Hands-on experience with containerization & orchestration (Docker, Kubernetes, Terraform). Skilled in cloud platforms (AWS, GCP, or Azure). Bonus points for: Familiarity with ML frameworks (PyTorch, Jupyter). Knowledge of cloud monitoring tools (Prometheus, Grafana). Experience with cloud-based databases (RDS, Aurora, Redshift, Spanner, etc.). Exposure to CI/CD pipelines (e.g., CircleCI). More ❯

Statistics, Informatics, Physics, Math, Neuroscience or another quantitative field. Scientific publications in top-tier AI and neuroscience conferences or peer reviewed journals Familiarity with deep learning libraries such as Pytorch, Huggingface, Transformers, Accelerator and Diffuser. Hands-on experience in training and fine-tuning generative models like diffusion models or large language models such as GPTs and LLAMAs. Experience with data More ❯

/ML engineers and leading delivery across multiple, concurrent AI workstreams. Strong technical background in AI/ML engineering — with production-level experience in Python, SQL, and frameworks like PyTorch or scikit-learn. Deep expertise in GenAI, LLMs, RAG systems, NLP, recommendation engines, or information retrieval stacks. Strong understanding of the full model lifecycle — from research and experimentation to deployment More ❯

data modeling Containers: Docker; orchestration tools Infra as Code: Terraform, CloudFormation Monitoring: Prometheus, Grafana, ELK Security & Compliance: Data protection and access control Nice-to-Haves AI/ML integration (PyTorch, HF etc.) MLOps (MLflow, Kubeflow, SageMaker) Vector search (Pinecone, pgvector, Weaviate, Qdrant etc.) Video ingestion pipelines & codecs 💡Don’t worry if you haven’t used these exact tools. If you More ❯

data modeling Containers: Docker; orchestration tools Infra as Code: Terraform, CloudFormation Monitoring: Prometheus, Grafana, ELK Security & Compliance: Data protection and access control Nice-to-Haves AI/ML integration (PyTorch, HF etc.) MLOps (MLflow, Kubeflow, SageMaker) Vector search (Pinecone, pgvector, Weaviate, Qdrant etc.) Video ingestion pipelines & codecs 💡Don’t worry if you haven’t used these exact tools. If you More ❯

data modeling Containers: Docker; orchestration tools Infra as Code: Terraform, CloudFormation Monitoring: Prometheus, Grafana, ELK Security & Compliance: Data protection and access control Nice-to-Haves AI/ML integration (PyTorch, HF etc.) MLOps (MLflow, Kubeflow, SageMaker) Vector search (Pinecone, pgvector, Weaviate, Qdrant etc.) Video ingestion pipelines & codecs 💡Don’t worry if you haven’t used these exact tools. If you More ❯

data modeling Containers: Docker; orchestration tools Infra as Code: Terraform, CloudFormation Monitoring: Prometheus, Grafana, ELK Security & Compliance: Data protection and access control Nice-to-Haves AI/ML integration (PyTorch, HF etc.) MLOps (MLflow, Kubeflow, SageMaker) Vector search (Pinecone, pgvector, Weaviate, Qdrant etc.) Video ingestion pipelines & codecs 💡Don’t worry if you haven’t used these exact tools. If you More ❯