Databricks is looking to lead the architecture, development, and optimization of the inference engine that powers their Foundation Model API, bridging research advances and production demands for high throughput, low latency, and robust scaling of GenAI inference.
Requirements
- Strong software engineering background (6+ years or equivalent) in performance-critical systems
- 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
Responsibilities
- 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
Other
- Proven track record of owning complex system components and driving architectural decisions end-to-end
- 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
- BS/MS/PhD in Computer Science, or a related field
- If access to export-controlled technology or source code is required for performance of job duties, it is within Employer's discretion whether to apply for a U.S. government license for such positions, and Employer may decline to proceed with an applicant on this basis alone.
