15 of 15 Ray Jobs

infrastructure, model deployment, distributed compute, GPU optimization, and MLOps architecture. Willingness to learn new parts of the ML tech stack – Python, PyTorch, Docker, Kubernetes, Ray, Weights & Biases, Prefect, BigQuery, Postgres, GCP, CUDA, and model serving frameworks. Fluency in life sciences or drug discovery is a plus. Working Location & Compensation: This ...

Excitement to learn parts of our ML tech stack that you might not already know. Our current ML infrastructure includes: Python, PyTorch, Docker, Kubernetes, Ray, Weights & Biases, Prefect, BigQuery, Postgres, GCP, CUDA, and various model serving frameworks. Fluency in life sciences or drug discovery is a plus but not required ...

tests for data quality, coverage, stability, and incremental lift over existing signals. Experience with LLMs and agentic AI frameworks/infrastructure (e.g., LangChain, LangGraph, Ray) is strongly preferred; ability to design or extend agentic workflows for analytics and data quality use cases is a plus. Demonstrated ability to proactively deliver ...

experience with cloud-native ML infrastructure platforms Knowledge of vector databases (Pinecone, Weaviate, Qdrant) and embedding models Experience with model serving frameworks (vLLM, TensorRT, Ray) Experience with A/B testing and experimentation frameworks for AI features Contributions to open-source ML projects or research publications Experience with model observability ...

orchestration tools (Temporal, Airflow, Kubeflow, or similar). Cloud platform expertise (AWS required, Azure & GCP beneficial). Experience with data processing frameworks (Spark, Athena, Ray, or similar). Experience with systems having ML Engineering and MLOps aspects. Proven track record of leading architectural transformations in growing companies. Excellence in technical ...

including 2D and 3D geometry Experience with cloud services and architectures (e.g. AWS, Azure) Proficiency with modern deep learning libraries and frameworks (PyTorch, HuggingFace, Ray) Excellent written documentation skills to document code, architectures, and experiments Preferred Qualifications Experience working with distributed systems Knowledge of the design, manufacturing, AEC, or media ...

scale. The day‐to‐day work combines data preprocessing, model training (PyTorch, TensorFlow), evaluation against business or research metrics, and production deployment (FastAPI, Triton, Ray) with monitoring. The role increasingly overlaps with MLOps (model deployment, monitoring, retraining infrastructure) and applied ML research, taking new techniques from papers to production. Generative ...

experience, with a strong record of shipping production systems in Python (and ideally C++). Deep experience with ML training infrastructure at scale (e.g. Ray, Slurm) including GPU orchestration, feature stores, experiment tracking, and model serving. Experience with deep learning model frameworks ...

experience, with a strong record of shipping production systems in Python (and ideally C++). Deep experience with ML training infrastructure at scale (e.g. Ray, Slurm) including GPU orchestration, feature stores, experiment tracking, and model serving. Experience with deep learning model frameworks ...

agentic applications. Your depth of expertise in technologies including Golang and Python programming languages, popular distributed compute frameworks including Spark/Dask/Ray/Flink, container (e.g., Kubernetes) and serverless (e.g., AWS Lambda) runtime environments, and ML+AI workload patterns will provide an amplifying technical element that is paramount … build control and data plane implementations required to realize a highly available, multi-tenant, large scale and a secure machine learning platform Develop Ray and Spark distributed compute engine solutions to accelerate diverse workloads from LLM pre-training and reinforcement learning to large-scale data processing, while maximizing compute unit ...

pipelines for video indexing and processing (face detection, quality assessment, tracking) Improve training performance across single and multi-node setups using PyTorch and Ray Build evaluation and experimentation systems (Parquet/Iceberg) for model output analysis Own model versioning, lifecycle management, and promotion to production Optimise inference pipelines using Triton … pipeline performance trade-offs: I/O, compute, batching, memory layout Hands-on PyTorch experience: training pipelines, data loading, preprocessing Practical distributed systems experience (Ray, DDP, or similar) Experience handling TB-scale or high-throughput data pipelines Familiarity with columnar formats: Arrow, Parquet, Iceberg Nice to Have Exposure to video ...

large-scale inference and data pipelines, focusing on KV cache management, heterogeneous memory scheduling, and high-throughput inference serving using frameworks like vLLM, Ray Serve, and modern PyTorch Distributed systems. Scalable Model Serving Infrastructure : Develop and evaluate frameworks that enable efficient multi-tenant, low-latency, and fault-tolerant AI serving … computing, or large-scale AI infrastructure are also welcome At least 2 years of experience with LLM inference/serving framework optimization (vLLM/Ray Serve/TensorRT-LLM/PyTorch) Hands-on experience with distributed KV cache optimization Familiarity with GPU and how they execute LLMs Strong knowledge ...

depth profiling and performance tuning of inference pipelines, focusing on KV cache management. Develop low-latency, fault-tolerant AI serving frameworks using vLLM, Ray Serve, and PyTorch Distributed. Research and prototype novel techniques for cache sharing, data locality, and resource orchestration. Translate innovative designs into publishable contributions at top-tier … systems, or related field. Strong knowledge of Distributed Systems, OS internals, and Machine Learning systems architecture. Hands-on experience with LLM serving frameworks (vLLM, Ray Serve, TensorRT-LLM, or TGI). Proficiency in C/C++ for systems development and Python for research prototyping. Solid grounding in distributed algorithms, load ...

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 ...

modern ML framework (e.g. PyTorch, JAX), and ability to learn others quickly. Experience with distributed training and inference frameworks (e.g. DeepSpeed, FSDP, Megatron, ZeRO, Ray). Strong software engineering fundamentals – you write robust, maintainable, production‐grade systems. Experience with GPU optimization, including memory efficiency, quantization, and mixed precision. Comfort owning … with RLHF pipelines (PPO, DPO, ORPO). Experience training or deploying multimodal or diffusion models. Experience with large‐scale data processing (Apache Arrow, Spark, Ray). How We Work The best products today in the world were built by small, world class teams. We are a high talent density ...