Distributed LLM Inference Engineer

OpenTalent; San Francisco (CA); On-site; USD 180,000 - 260,000; Full time

Job summary

Anyscale is seeking a Distributed LLM Inference Engineer in San Francisco to push the performance boundaries of large-scale inference. You will collaborate with product teams to deliver end-to-end batch and online inference solutions, leveraging Ray Data and LLM engines.

The role requires familiarity with deep learning frameworks (PyTorch), distributed systems, and GPUs/CUDA, with opportunities to contribute to open source projects like vLLM and TensorRT-LLM.

Qualifications

• 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.

Responsibilities

• Iterate quickly with product teams to ship end-to-end batch and online inference solutions at high scale.
• Work across the stack integrating Ray Data and LLM engine providing optimizations for low-cost large-scale ML inference.
• Integrate with open source software like vLLM and contribute improvements to open source.
• Follow state-of-the-art in open source and research, implementing best practices.

Skills

ML inference at scale

Deep learning

PyTorch familiarity

Distributed systems

Tools

Ray

vLLM

TensorRT-LLM

CUDA

Job description

About Anyscale

At Anyscale, we're on a mission to democratize distributed computing and make it accessible to software developers of all skill levels. We’re commercializing Ray, a popular open-source project that's creating an ecosystem of libraries for scalable machine learning. Companies like OpenAI, Uber, Spotify, Instacart, Cruise, and many more, have Ray in their tech stacks to accelerate the progress of AI applications out into the real world. With Anyscale, we’re building the best place to run Ray, so that any developer or data scientist can scale an ML application from their laptop to the cluster without needing to be a distributed systems expert.

About the Role

As a Distributed LLM Inference Engineer, you will help systems and optimizations that push the boundaries of performance for inference at large scale. This is an incredibly critical role to Anyscale as it allows us to achieve a market leading position for AI infrastructure.

As part of this role, you will

• 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

We'd love to hear from you if you have

• 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

Bonus points!

• 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