Staff Software Engineer - GenAI inference

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 the 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.

P-1285

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 Pay Range Transparency Local Pay Range: $190,900—$232,800 USD. Compensation may include eligibility for annual performance bonus, equity, and benefits. Benefits Comprehensive benefits and perks are offered. Our Commitment to Diversity and Inclusion We are committed to fostering a diverse and inclusive culture and have hiring practices that meet equal employment opportunity standards. Disparate qualities are considered without regard to age, color, disability, ethnicity, family or marital status, gender identity or expression, language, national origin, physical and mental ability, political affiliation, race, religion, sexual orientation, socio‑economic status, veteran status, and other protected characteristics. Compliance If access to export‑controlled technology or source code is required for performance of job duties, the Employer may decline to proceed with an applicant on this basis alone. #J-18808-Ljbffr Databricks