Software Engineer - GenAI inference

P-1284 About This Role As a software engineer for GenAI inference, you will help design, develop, and optimize the inference engine that powers Databricks’ Foundation Model API. You’ll work at the intersection of research and production, ensuring our large language model (LLM) serving systems are fast, scalable, and efficient. Your work will touch the full GenAI inference stack — from kernels and runtimes to orchestration and memory management. What You Will Do Contribute to the design and implementation of the inference engine, and collaborate on model-serving stack optimized for large-scale LLMs inference Collaborate with researchers to bring new model architectures or features (sparsity, activation compression, mixture-of-experts) into the engine Optimize for latency, throughput, memory efficiency, and hardware utilization across GPUs, and accelerators Build and maintain instrumentation, profiling, and tracing tooling to uncover bottlenecks and guide optimizations Develop and enhance scalable routing, batching, scheduling, memory management, and dynamic loading mechanisms for inference workloads Support reliability, reproducibility, and fault tolerance in the inference pipelines, including A/B launches, rollback, and model versioning Integrate with federated, distributed inference infrastructure – orchestrate across nodes, balance load, handle communication overhead Collaborate cross-functionally: with platform engineers, cloud infrastructure, and security/compliance teams Document and share learnings, contributing to internal best practices and open-source efforts when possible What We Look For BS/MS/PhD in Computer Science, or a related field Strong software engineering background (3+ years or equivalent) in performance-critical systems Solid 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.) Comfortable designing and operating distributed systems, 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 work closely with ML researchers, translate novel model ideas into production systems Ownership mindset and eagerness to dive deep into complex system challenges Bonus: published research or open-source contributions in ML systems, inference optimization, or model serving Pay Range Transparency Databricks is committed to fair and equitable compensation practices. The pay range(s) for this role is listed below and represents the expected salary range for non-commissionable roles or on-target earnings for commissionable roles. Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to job-related skills, depth of experience, relevant certifications and training, and specific work location. Based on the factors above, Databricks anticipates utilizing the full width of the range. The total compensation package for this position may also include eligibility for annual performance bonus, equity, and the benefits listed above. For more information regarding which range your location is in visit our page here . Local Pay Range $142,200 $204,600 USD About Databricks Databricks is the data and AI company. More than 10,000 organizations worldwide — including Comcast, Condé Nast, Grammarly, and over 50% of the Fortune 500 — rely on the Databricks Data Intelligence Platform to unify and democratize data, analytics and AI. Databricks is headquartered in San Francisco, with offices around the globe and was founded by the original creators of Lakehouse, Apache Spark™, Delta Lake and MLflow. To learn more, follow Databricks on Twitter , LinkedIn and Facebook ... (truncated, view full listing at source)