Distributed LLM Inference Engineer

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.

Proud to be backed by  Andreessen Horowitz, NEA, and Addition with $250+ million raised to date.

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

Compensation

At Anyscale, we take a market-based approach to compensation. We are data-driven, transparent, and consistent.  As the market data changes over time, the target salary for this role may be adjusted.