Staff Software Engineer - GenAI inference

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

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

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..

You’ll 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.

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