15 of 15 Ray Jobs

Kubernetes, and Terraform Strong scripting skills in Python or Bash Familiar with ML lifecycle tools, model monitoring, and versioning Exposure to tools like KServe, Ray Serve, Triton, or vLLM a big plus Bonus Points: Experience with observability frameworks like Prometheus or OpenTelemetry Knowledge of ML libraries: TensorFlow, PyTorch, HuggingFace Exposure ...

autonomous systems. Technical Self-Assessment (1-10) Candidates will be asked to rate their knowledge of: Languages: Python, Java, Scala Compute/Distributed: RAY , Spark, Databricks, Hadoop Orchestration/Cloud: Kafka, Airflow, Prefect, AWS Storage/Warehouse: Clickhouse, Snowflake, Redshift, Greenplum Note: Experience with Petabyte/Exabyte scale ...

best practices. • The ability to design systems for scale, reliability and cost‐efficiency. Nice to have skills include AWS, Kubernetes, GCP BigQuery, GPU or Ray experience, and exposure to AdTech or privacy‐first data environments. How to Apply If this sounds like the right next step for you, apply today ...

/CD , Git-based workflows, and automation Strong problem-solving skills and a collaborative mindset Bonus Terraform or other IaC tools Slurm, Kueue, Ray, Spark, or similar systems GPU tooling (CUDA, Nvidia operators, schedulers) Experience supporting ML training or data science teams ...

best practices. * The ability to design systems for scale, reliability and cost-efficiency. Nice to have skills include AWS, Kubernetes, GCP BigQuery, GPU or Ray experience, and exposure to AdTech or privacy-first data environments. How to Apply If this sounds like the right next step for you, apply today ...

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 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 ...

runtime environments; Hands-on experience with Serverless architectures and cloud-native optimization technologies such as containers, Kubernetes, service orchestration, and autoscaling vLLM, SGLang, Ray Serve, etc.); understand common optimization concepts such as continuous batching, KV-Cache reuse, parallelism, and compression/quantization/distillation Proficient in using Profiling/Tracing ...

runtime environments; Hands-on experience with Serverless architectures and cloud-native optimization technologies such as containers, Kubernetes, service orchestration, and autoscaling vLLM, SGLang, Ray Serve, etc.); understand common optimization concepts such as continuous batching, KV-Cache reuse, parallelism, and compression/quantization/distillation Proficient in using Profiling/Tracing ...

runtime environments; Hands-on experience with Serverless architectures and cloud-native optimization technologies such as containers, Kubernetes, service orchestration, and autoscaling vLLM, SGLang, Ray Serve, etc.); understand common optimization concepts such as continuous batching, KV-Cache reuse, parallelism, and compression/quantization/distillation Proficient in using Profiling/Tracing ...

runtime environments; Hands-on experience with Serverless architectures and cloud-native optimization technologies such as containers, Kubernetes, service orchestration, and autoscaling vLLM, SGLang, Ray Serve, etc.); understand common optimization concepts such as continuous batching, KV-Cache reuse, parallelism, and compression/quantization/distillation Proficient in using Profiling/Tracing ...

runtime environments; Hands-on experience with Serverless architectures and cloud-native optimization technologies such as containers, Kubernetes, service orchestration, and autoscaling vLLM, SGLang, Ray Serve, etc.); understand common optimization concepts such as continuous batching, KV-Cache reuse, parallelism, and compression/quantization/distillation Proficient in using Profiling/Tracing ...

/PhD in Mathematics, Physics, Computer Science, or a related field Strong hands-on experience building LLM-based systems with modern ML tooling (PyTorch, Ray, JAX) Experience designing agentic systems (planning, memory, tool use, multi-step reasoning) Strong understanding of transformers and foundation model behavior Comfortable owning whatever needs doing ...

Mathematics, Physics, Computer Science, or a related field Strong hands-on experience building, training, and scaling foundation models with modern ML tooling (PyTorch, Ray, JAX) Strong background in graph neural networks and transformer architectures Demonstrated ability to turn research ideas into robust, scalable implementations Comfortable owning whatever needs doing, from ...

depth profiling of large-scale inference pipelines, specifically focusing on KV cache management and heterogeneous memory scheduling. AI Serving: Optimising high-throughput frameworks (vLLM, Ray Serve, PyTorch Distributed) to ensure low-latency, multi-tenant performance. Research Leadership: Contributing to top-tier venues (OSDI, NSDI, EuroSys, MLSys) and driving those innovations … Stack: Strong proficiency in C/C++ for systems work, with Python for rapid prototyping. Expertise: Hands-on experience with LLM serving frameworks ( vLLM, Ray Serve, TensorRT-LLM ) and distributed algorithms. Mindset: A solid grounding in systems research methodology and performance profiling tools. The "Value Add" (Desired): A PhD focused ...