Staff Software Engineer GenAI inference

P-1285

About This Role

As a staff software engineer for GenAI inference you will lead the architecture development and optimization of the inference engine that powers Databricks Foundation Model API.. Youll bridge research advances and production demands ensuring high throughput low latency and robust scaling. Your work will encompass the full GenAI inference stack: kernels runtimes orchestration memory and integration with frameworks and orchestration systems.

What You Will Do

• Own and drive the architecture design and implementation of the inference engine and collaborate on model-serving stack optimized for large-scale LLMs inference
• Partner closely with researchers to bring new model architectures or features (sparsity activation compression mixture-of-experts) into the engine
• Lead the end-to-end optimization for latency throughput memory efficiency and hardware utilization across GPUs and accelerators
• Define and guide standards to build and maintain instrumentation profiling and tracing tooling to uncover bottlenecks and guide optimizations
• Architect scalable routing batching scheduling memory management and dynamic loading mechanisms for inference workloads
• Ensure reliability reproducibility and fault tolerance in the inference pipelines including A/B launches rollback and model versioning
• Collaborate cross-functionally on Integrating with federated distributed inference infrastructure orchestrate across nodes balance load handle communication overhead
• Drive cross-team collaboration: with platform engineers cloud infrastructure and security/compliance teams
• Represent the team externally through benchmarks whitepapers and open-source contributions

What We Look For

• BS/MS/PhD in Computer Science or a related field
• Strong software engineering background (6 years or equivalent) in performance-critical systems
• Proven track record of owning complex system components and driving architectural decisions end-to-end
• Deep understanding of ML inference internals: attention MLPs recurrent modules quantization sparse operations etc.
• Hands-on experience with CUDA GPU programming and key libraries (cuBLAS cuDNN NCCL etc.)
• Strong background in distributed systems design including RPC frameworks queuing RPC batching sharding memory partitioning
• Demonstrated ability to uncover and solve performance bottlenecks across layers (kernel memory networking scheduler)
• Experience building instrumentation tracing and profiling tools for ML models
• Ability to lead through influence - work closely with ML researchers translate novel model ideas into production systems
• Excellent communication and leadership skills with a proactive and ownership-driven mindset
• Bonus: published research or open-source contributions in ML systems inference optimization or model serving

Required Experience:

Staff IC