20 of 20 Remote/Hybrid vLLM Jobs

Foundry and other AWS, Azure or GCP services . Work with self-hosted inference and model-serving technologies where appropriate, including tools such as vLLM, SGLang and Ollama . Design evaluation, observability and monitoring frameworks to assess model performance, system health, reliability and business impact. Optimise AI solutions for performance ...

optimise models using cloud services like AWS Bedrock and Azure AI Foundry, or self-host them on GPU/CPU hardware using tools like vLLM, SGLang, and Ollama.Solution Evaluation: Implement frameworks and approaches to evaluate model performance against business objectives, both pre-deployment and on an ongoing basis as part ...

optimise models using cloud services like AWS Bedrock and Azure AI Foundry, or self-host them on GPU/CPU hardware using tools like vLLM, SGLang, and Ollama.Solution Evaluation: Implement frameworks and approaches to evaluate model performance against business objectives, both pre-deployment and on an ongoing basis as part ...

optimise models using cloud services like AWS Bedrock and Azure AI Foundry, or self-host them on GPU/CPU hardware using tools like vLLM, SGLang, and Ollama.Solution Evaluation: Implement frameworks and approaches to evaluate model performance against business objectives, both pre-deployment and on an ongoing basis as part ...

optimise models using cloud services like AWS Bedrock and Azure AI Foundry, or self-host them on GPU/CPU hardware using tools like vLLM, SGLang, and Ollama.Solution Evaluation: Implement frameworks and approaches to evaluate model performance against business objectives, both pre-deployment and on an ongoing basis as part ...

optimise models using cloud services like AWS Bedrock and Azure AI Foundry, or self-host them on GPU/CPU hardware using tools like vLLM, SGLang, and Ollama.Solution Evaluation: Implement frameworks and approaches to evaluate model performance against business objectives, both pre-deployment and on an ongoing basis as part ...

haves Fintech/trading/market-data domain experience or experience as a trading-platform user. Open-source contributions to AI tooling (LangChain, LlamaIndex, vLLM, DSPy, or similar). Production RAG and vector-database experience; experience with code-generation, transpilation, or developer-experience tooling. Logistics Location: Europe (EU/ ...

purely technical preference.Technical Expertise:Deep proficiency in Python and modern software development practices.Hands-on experience with Distributed Training infrastructure (Multi-node GPU training, Kubernetes, vLLM).Familiarity with Deep Learning frameworks (PyTorch).Experience with MLOps tools and experiment tracking (e.g., ClearML, MLFlow, Weights & Biases).Research Fluency: Ability to read technical research ...

Hands-on experience with LLM training cycles, parameter-efficient fine-tuning (PEFT), and sophisticated prompt engineering.Inference Stack: Experience with high-performance inference servers (e.g., vLLM, TGI, or Triton) and an understanding of how to optimize models for GPU deployment.Infrastructure: Comfortable working in Linux-based environments and proficient in managing containerized ...

experience with LLM training cycles, parameter-efficient fine-tuning (PEFT), and sophisticated prompt engineering. Inference Stack: Experience with high-performance inference servers (e.g., vLLM, TGI, or Triton ) and an understanding of how to optimize models for GPU deployment. Infrastructure: Comfortable working in Linux-based environments and proficient in managing containerized ...

Deep understanding of how models are trained, how they work internally, and their inherent limitations.LLM Serving stack: Experience with the LLM serving stack (e.g. vLLM) for open weight models for bringing the latest models to internal users.ML Literacy: Experience evaluating probabilistic ML systems and managing model "tool use", contexts ...

production-quality code and data pipelines for ML systemsProficiency in modern AI development frameworks including: PyTorch, Jax , HuggingFace Transformers, LLM APIs (litellm etc) and vLLM for building and deploying large-scale AI applicationsUnderstanding of LLM training methodologies including instruction fine-tuning, preference optimization, and reinforcement learning approachesStrong software engineering skills ...

rail-optimised and fat-tree topologies. Working knowledge of the AI software and orchestration stack: CUDA, cuDNN, NCCL, ROCm, Triton Inference Server, NIM, vLLM, TensorRT-LLM, Slurm, Kubernetes (with GPU Operator), Kubeflow, Run:ai, MLflow and NVIDIA AI Enterprise. Familiarity with datacenter facilities engineering for AI workloads: high-density power ...

rail-optimised and fat-tree topologies. Working knowledge of the AI software and orchestration stack: CUDA, cuDNN, NCCL, ROCm, Triton Inference Server, NIM, vLLM, TensorRT-LLM, Slurm, Kubernetes (with GPU Operator), Kubeflow, Run:ai, MLflow and NVIDIA AI Enterprise. Familiarity with datacenter facilities engineering for AI workloads: high-density power ...

data, including classification, extraction, embeddings, re-rankers, clustering, and search. Experience in instruction fine-tuning and serving language models, familiarity with frameworks such as vLLM, DeepSpeed, or similar tools A solid grounding in classical ML and statistics, and the judgement to choose simpler methods when they’re the right solution. ...

without degrading their reasoning capabilities. Experience with machine translation, multilingual NLP, or language quality estimation. Familiarity with inference and serving at scale (e.g. via vLLM, SGLang, TensorRT‐LLM, etc) and long‐context modelling. Publications at top‐tier venues. What we offer Diverse and internationally distributed team : joining our team means ...

compatible object storage - including performance-killing cases (small-object overhead, range-request patterns, eventual consistency, multipart tuning). Comfort with model-serving runtimes (vLLM, SGLang etc) and the formats they consume (safetensors, GGUF, sharded checkpoints). An end-to-end view: comfortable reasoning about NIC, switch, filesystem, cache, container runtime ...

product management in development tools and software including CLIs, SDKs and APIs.Knowledge of machine learning frameworks, runtimes, infrastructure such as PyTorch, ONNX, ExecuTorch, Llama.cpp, vLLM and LiteRT.Understanding of model deployment to edge, embedded or heterogeneous computing, with an understanding of trade-offs between accuracy, performance and power consumption.You should also ...

management in development tools and software including CLIs, SDKs and APIs. Knowledge of machine learning frameworks, runtimes, infrastructure such as PyTorch, ONNX, ExecuTorch, Llama.cpp, vLLM and LiteRT. Understanding of model deployment to edge, embedded or heterogeneous computing, with an understanding of trade-offs between accuracy, performance and power consumption. ...

user and a working results. Bonus Points These aren't requirements, but they'd make you stand out: Experience with inference serving stacks (vLLM, SGLang, TensorRT-LLM, Triton). A track record of contributing to or maintaining open source developer tools and ML ecosystem projects Experience writing technical documentation ...