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Vllm Jobs (NOW HIRING)

WEKA

OR

Stay hands-on across the AMG stack (Python, C++, CUDA, vLLM, NIXL/Dynamo, Kubernetes), contributing directly to production systems while providing technical leadership to the team. * Solve Hard ...

WEKA

Tech Lead - AI Inference

Stay hands-on across the AMG stack (Python, C++, CUDA, vLLM, NIXL/Dynamo, Kubernetes), contributing directly to production systems while providing technical leadership to the team. * Solve Hard ...

Accenture Federal Services

AI Engineer

Arlington, VA · On-site

Leverage tools and frameworks such as LangGraph, Semantic Kernel, vLLM, Ollama, and Ray for scalable AI solutions * Integrate with NVIDIA GPU ecosystems and vector databases to enhance AI performance ...

EnCharge AI

LLM Inference Deployment Engineer

$180K - $240K/yr

Utilize inference runtimes such as ONNX Runtime, vLLM for efficient execution. * Optimize batching, caching, and tensor parallelism to improve LLM scalability in real-time applications. * Develop and ...

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Vllm information

How does a VLLM (Very Large Language Model) Engineer typically collaborate with data scientists and product teams during model deployment?

VLLM Engineers work closely with data scientists to understand the specific requirements and fine-tuning needs of large-scale language models. They are often responsible for integrating these models into production systems, ensuring scalability and efficiency. Collaboration with product teams is crucial to align model capabilities with user needs and to troubleshoot real-world application challenges. Frequent communication and agile workflows are common, as updates or optimizations may be needed rapidly based on feedback from both teams.

What is a VLLM and what do they do?

VLLM stands for 'Virtual Large Language Model.' In the context of AI development, VLLM professionals work with optimized inference engines for large language models, enabling faster and more efficient deployment of AI models in production environments. Their responsibilities often include integrating LLMs into applications, optimizing model performance, and ensuring scalability for real-time use cases. They may also collaborate with data scientists and engineers to manage resources and streamline AI workflows.

What is the difference between Vllm vs Data Analyst?

AspectVllmData Analyst
Required CredentialsTypically requires knowledge of machine learning, AI, and programming languages like Python or RRequires skills in statistics, Excel, SQL, and data visualization tools
Work EnvironmentOften in tech companies, research labs, or AI-focused teamsCommonly in business, finance, healthcare, and marketing sectors
Industry UsageEmerging role in AI and machine learning projectsEstablished role in data-driven decision making
Common Search/ComparisonVllm vs Data Analyst

The main difference between Vllm and Data Analyst lies in their focus and skill set. Vllm professionals specialize in AI and machine learning models, often working in tech environments, while Data Analysts focus on interpreting data to inform business decisions. Both roles require analytical skills, but Vllm roles demand programming and AI expertise, whereas Data Analysts emphasize statistical analysis and data visualization.

What are the key skills and qualifications needed to thrive as a Machine Learning Engineer working with vLLM, and why are they important?

To thrive as a Machine Learning Engineer specializing in vLLM (a high-throughput LLM inference library), you need a strong understanding of machine learning principles, deep learning frameworks, and experience with Python programming. Familiarity with tools like PyTorch, CUDA, distributed computing, and cloud platforms, as well as relevant certifications in ML or data engineering, is highly valuable. Strong problem-solving, collaboration, and communication skills are essential for optimizing model performance and integrating with cross-functional teams. These capabilities ensure effective deployment and scaling of large language models, driving innovation and efficiency in AI applications.
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Vllm jobs near you
Infographic showing various Vllm job openings in the United States as of July 2026, with employment types broken down into 1% Internship, 96% Full Time, 1% Part Time, and 2% Contract. Highlights an 84% Physical, 3% Hybrid, and 13% Remote job distribution.
Distributed LLM Inference Engineer

Distributed LLM Inference Engineer

Anyscale

San Francisco, CA • On-site

Full-time

Re-posted 16 days ago

Job description

Job Summary:
Anyscale is on a mission to democratize distributed computing and make it accessible to software developers. The Distributed LLM Inference Engineer will optimize systems for large-scale ML inference, working closely with product teams and the open-source community to deliver high-performance solutions.
Responsibilities:
• Iterate very quickly with product teams to ship the end to end solutions for Batch and Online inference at high scale which will be used by open-source Ray users and customers of Anyscale
• Work across the stack integrating Ray Data and LLM engine providing optimizations achieving low cost solutions for large scale ML inference
• Integrate with Open source software like vLLM, work closely with the community to adopt these techniques in Anyscale solutions, and also contribute improvements to open source
• Follow the latest state-of-the-art in the open source and the research community, implementing and extending best practices
Qualifications:
Required:
• Familiarity with running ML inference at large scale with high throughput and low latency
• Familiarity with deep learning and deep learning frameworks (e.g. PyTorch)
• Solid understanding of distributed systems, ML inference challenges
Preferred:
• ML Systems knowledge
• Experience using Ray
• Work closely with community on LLM engines like vLLM, TensorRT-LLM
• Contributions to deep learning frameworks (PyTorch, TensorFlow)
• Contributions to deep learning compilers (Triton, TVM, MLIR)
• Prior experience working on GPUs / CUDA
Company:
Anyscale accelerates the development and productionization of any AI app, on any cloud, at any scale. Founded in 2019, the company is headquartered in San Francisco, USA, with a team of 201-500 employees. The company is currently Growth Stage.

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