4 of 4 Remote/Hybrid Ray Jobs

learning research, AI systems, or MLOps-focused engineering. Familiarity with reinforcement learning, simulation environments, or control systems. Experience using distributed computing frameworks such as Ray and managing GPU workloads across multiple nodes. Knowledge of platforms and tools such as PyTorch, SciPy, scikit-learn, NumPy, pandas, MLflow, Docker, Kubernetes, and cloud ...

Qualifications Advanced degree (MSc/PhD) in Machine Learning, Computer Science, or related field Experience scaling machine learning training and data pipelines (e.g., using Ray or similar frameworks) Experience with cloud services and architectures (e.g. AWS, Azure) Experience with LLMs or generative AI systems (fine-tuning, prompting, evaluation) Knowledge ...

infrastructure details. Nice to Have Experience managing physical data centres, co‐location facilities, or hybrid infrastructure environments. Working knowledge of ML orchestration frameworks (e.g., Ray, Slurm, Kubeflow). Background in media pipelines, VFX tooling, or media compliance standards (MPA, ISO 27001). Prior experience working in a hybrid startup/ ...

learning frameworks such as PyTorch and scientific computing libraries such as SciPy. Experience working with scalable experimentation environments and infrastructure such as distributed computing, Ray, Kubernetes, Docker, or cloud platforms like GCP. Strong research background demonstrated through publications or contributions in reinforcement learning, control systems, artificial intelligence, or related fields. ...