1 to 25 of 30 Permanent Ray Jobs

Python3, C++, Rust, SQL Frameworks: PyTorch, TensorFlow, Docker Databases: Postgres, Elasticsearch, DynamoDB, RDS Cloud: Kubernetes, Helm, EKS, Terraform, AWS Data Engineering: Apache Arrow, Dremio, Ray Miscellaneous: Git, Jupyterhub, Apache Superset, Plotly Dash Qualifications: Bachelor's degree in Computer Science, Math, Engineering, or related field (Master's preferred) with ...

Comfortable collaborating with cross-functional technical partners. Curious, proactive, and eager to learn. Nice to Have: Experience with big data tools (e.g., Apache Spark, Ray). Familiarity with cloud infrastructure (AWS, Terraform, Kubernetes). Knowledge of ML frameworks (Huggingface, PyTorch, TensorFlow, Keras). We use Covey as part ...

certification within the first 90 days of employment Highly preferred: Experience with ML model serving and inference platforms such as Triton Inference Server, KServe, Ray Serve, vLLM, or similar technologies Experience with secure and compliant deployment practices in regulated or government environments Experience with Kubernetes-based ML platforms such ...

driven features. Cloud and data platform proficiency: The ability to utilize cloud environments such as GCP, AWS, or Azure. Familiarity with solutions like Databricks, Ray, or KubeFlow is a plus. Data engineering knowledge: Skills in handling and managing large datasets including, data cleaning, preprocessing, and storage. Good understanding of batch ...

Experience training and iterating on machine learning models (e.g., for ranking, prediction, or personalization). Experience with ML and distributed compute frameworks such as Ray or similar tools. Experience partnering with data science or ML teams to productionize models (designing feature stores, ensuring offline/online parity, advanced model deployment ...

spent 3+ years as software engineer, with a wide generalist skillset. You've worked with distributed systems, and previously administered a big data toolset (Ray, Spark, Airflow, etc.). You have hands-on experience shipping scalable data solutions in the cloud (e.g AWS, GCP, Azure, etc.), across multiple data stores ...

processing, and inference. Cloud platform proficiency: The ability to utilize cloud environments such as GCP, AWS, or Azure. Familiarity with ML serving solutions like Ray, Databricks, KubeFlow, or W&B is a plus. ML knowledge: Deep understanding of various DNN architectures, track record of building, debugging, and fine-tuning models. ...

knowledge of distributed systems, operating systems, machine learning systems architecture, Inference serving, and AI Infrastructure. · Hands-on experience with LLM serving frameworks (e.g., vLLM, Ray Serve, TensorRT-LLM, TGI) and distributed KV cache optimization. · Proficiency in C/C++, with additional experience in Python for research prototyping. · Solid grounding ...

optimized Python. You know how to profile code and squeeze every ounce of performance out of NVIDIA GPUs. Distributed Systems & Scaling. Experience with Kubernetes, Ray, custom load balancing, multi-GPU/multi-node inference, and reliably handling thousands of concurrent connections. Public work. Non-trivial systems programming projects, open-source ...

Learning pipelines or integrating AI models into production engineering systems.- Experience with large-scale data processing systems and orchestration framework (e.g., Apache Spark, Airflow, Ray, Kafka).- Open-minded and collaborative team player with strong willingness to help others.The US yearly salary range for this role ...

Learning pipelines or integrating AI models into production engineering systems.- Experience with large-scale data processing systems and orchestration framework (e.g., Apache Spark, Airflow, Ray, Kafka).- Open-minded and collaborative team player with strong willingness to help others.The US yearly salary range for this role ...

Learning pipelines or integrating AI models into production engineering systems.- Experience with large-scale data processing systems and orchestration framework (e.g., Apache Spark, Airflow, Ray, Kafka).- Open-minded and collaborative team player with strong willingness to help others.The US yearly salary range for this role ...

Master's in CS, EE, or related field (or equivalent practical experience). Preferred: Contributions to inference frameworks (vLLM, Triton, TensorRT-LLM, Ray Serve, TorchServe). Experience with CUDA kernels, NCCL/SHARP, RDMA/NUMA, or GPU interconnect topologies. Leading multi-team initiatives or partnering with customers on mission ...

track record improving tail latency (P95/P99) and service reliability through metrics-driven work. Preferred: Contributions to inference frameworks (vLLM, Triton, TensorRT-LLM, Ray Serve, TorchServe). Experience with CUDA kernels, NCCL/SHARP, RDMA/NUMA, or GPU interconnect topologies. Leading multi-team initiatives or partnering with customers ...

about twenty machine learning scientists. The team is supported by an ML Ops team that provides state-of-the-art tooling (including AWS, Encord, Ray, PyTorch Lightning and Weights & Biases). The Applied Science team works closely with product engineering to deploy models to serve our worldwide customer base. Position ...

About the role: Implement well-scoped features and fixes in Python/Go/C++ for model-serving services (e.g., Triton, vLLM, TensorRT-LLM, Ray Serve). Write tests, code comments, and short design docs; participate in code reviews. Add basic metrics and dashboards; assist with alarms and runbooks. Follow ...

improve service reliability and performance using metrics (P95/P99 latency, throughput, error budgets). Preferred Familiarity with orchestration and workflow technologies such as Ray, Kubeflow, Kueue, Istio, Knative, or Argo Workflows Experience with distributed workloads, GPU-based applications, or ML pipelines. Knowledge of scheduling concepts like quota enforcement ...

Qualifications 4+ years of experience Proficient in Python or C++ Familiarity with any of the training frameworks and libraries like PyTorch, Lightning, Hugging Face, Ray, JAX, etc. Familiarity with any of the GPU-accelerated inference on Nvidia hardware like CUDA, TensorRT, and/or XLA Bonus Qualifications Familiarity with Bazel ...

cross-team architecture. Bachelor's or Master's degree in CS, EE, or related field. Preferred Familiarity with orchestration and workflow technologies such as Ray, Kubeflow, Kueue, Istio, Knative, or Argo Workflows Deep expertise in Slurm/Kubernetes internals. Experience with distributed workloads, GPU-based applications, or ML pipelines. Knowledge ...

mentorship. Qualifications Strong experience with training frameworks like PyTorch, leveraging GPUs efficiently for distributed model training. Experience with GPU-accelerated inference using TensorRT, Ray Serve, or similar frameworks. Experience using profiling tools like NVIDIA's Nsight or PyTorch's Profiler for identifying model training and serving bottlenecks. Proficient in Python ...

mentorship. Qualifications Strong experience with training frameworks like PyTorch, leveraging GPUs efficiently for distributed model training. Experience with GPU-accelerated inference using TensorRT, Ray Serve, or similar frameworks. Experience using profiling tools like NVIDIA's Nsight or PyTorch's Profiler for identifying model training and serving bottlenecks. Proficient in Python ...

mentorship. Qualifications Strong experience with training frameworks like PyTorch, leveraging GPUs efficiently for distributed model training. Experience with GPU-accelerated inference using TensorRT, Ray Serve, or similar frameworks. Experience using profiling tools like NVIDIA's Nsight or PyTorch's Profiler for identifying model training and serving bottlenecks. Proficient in Python ...

such as Terraform, Pulumi, or Crossplane. Workload Scheduling: Hands-on experience building or managing large-scale orchestrators for compute-heavy workloads (e.g., Kubernetes, KubeRay, Ray, Slurm, or Volcano). Distributed Data Processing: Proficiency in at least one distributed processing framework, such as Apache Spark or Apache Beam, for large-scale … context of high-performance computing. Bonus Points Active contributor to open-source projects in the MLOps or Cloud-Native ecosystem (e.g., CNCF, Ray, or Kubeflow communities). Experience with high-performance storage systems (e.g., Lustre, Ceph, or specialized NVMe caching) for ML data loading. Knowledge of cost-optimization strategies ...

workloads in industrial contexts. You are responsible for developing and operating core infrastructure components such as scheduling and resource management systems (e.g., SLURM, Ray, Run:ai), ensuring efficient utilization of shared GPU resources. Using modern tooling, you build and maintain automated, reproducible infrastructure (e.g., Docker, Kubernetes, Terraform, Ansible, CI/… InfiniBand, NCCL), combined with experience in cloud environments (AWS, Azure) alongside on prem infrastructure. Practical experience with resource scheduling and workload orchestration (e.g., SLURM, Ray, NVIDIA Run:ai). Strong experience in infrastructure automation (e.g., Docker, Kubernetes, Terraform, Ansible, CI/CD) and proficiency in Python for infrastructure and system ...

forecasting models and budget management tools for capacity planning Qualifications Experience optimizing large-scale distributed systems for cost and efficiency Experience with Ray.io, particularly Ray Core and Ray Data Experience with Kubernetes, particularly for heterogeneous workloads and cost optimization Experience with cloud cost management on AWS (Cost Explorer) or similar ...