Machine Learning Engineer
Build and deploy cutting-edge Agentic AI and LLM systems to transform Airbnb's customer service experience. Requires PhD or equivalent experience and production ML/AI deployment expertise.
Distributed LLM Inference Engineer
Build and optimize distributed LLM inference systems at scale using Ray, integrating with engines like vLLM to deliver high-throughput, low-latency batch and online inference solutions.
170k – 247kSan Francisco, CAPalo Alto, CACaliforniaML EngineeringHybrid
About the role
Responsibilities
- Iterate quickly with product teams to ship end-to-end solutions for batch and online inference at high scale for Ray users and Anyscale customers
- Work across the stack integrating Ray Data and LLM engines to provide optimizations for low-cost, large-scale ML inference
- Integrate with open-source software like vLLM, work with the community to adopt techniques in Anyscale solutions, and contribute improvements to open source
- Follow state-of-the-art developments in open source and research, implementing and extending best practices
Requirements
- 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 and ML inference challenges
Nice-to-Haves
- ML Systems knowledge
- Experience using Ray
- Work 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 & Benefits
- Market-based compensation approach
- Equity (stock options)
- Healthcare plans with 99% premiums covered for employees and dependents
- 401k Retirement Plan
- Education & Wellbeing Stipend
- Paid Parental Leave
- Fertility Benefits
- Paid Time Off
- Commute reimbursement
- 100% of in-office meals covered
Skills
PyTorchRayvLLMTensorrt-LlmDistributed SystemsMl InferenceCUDATritonTvmMlirTensorFlow
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