Databricks Inc.
Staff Software Engineer - GenAI Performance and Kernel
Databricks Inc., San Francisco, California, United States, 94199
About This Role
As a staff software engineer for GenAI Performance and Kernel, you will own the design, implementation, optimization, and correctness of the high-performance GPU kernels powering our GenAI inference stack. You will lead development of highly-tuned, low-level compute paths, manage trade-offs between hardware efficiency and generality, and mentor others in kernel-level performance engineering. You will work closely with ML researchers, systems engineers, and product teams to push the state-of-the-art in inference performance at scale. What You Will Do
Lead the design, implementation, benchmarking, and maintenance of core compute kernels (e.g. attention, MLP, softmax, layernorm, memory management) optimized for various hardware backends (GPU, accelerators) Drive the performance roadmap for kernel-level improvements: vectorization, tensorization, tiling, fusion, mixed precision, sparsity, quantization, memory reuse, scheduling, auto-tuning, etc. Integrate kernel optimizations with higher-level ML systems Build and maintain profiling, instrumentation, and verification tooling to detect correctness, performance regressions, numerical issues, and hardware utilization gaps Lead performance investigations and root-cause analysis on inference bottlenecks, e.g. memory bandwidth, cache contention, kernel launch overhead, tensor fragmentation Establish coding patterns, abstractions, and frameworks to modularize kernels for reuse, cross-backend portability, and maintainability Influence system architecture decisions to make kernel improvements more effective (e.g. memory layout, dataflow scheduling, kernel fusion boundaries) Mentor and guide other engineers working on lower-level performance, provide code reviews, help set best practices Collaborate with infrastructure, tooling, and ML teams to roll out kernel-level optimizations into production, and monitor their impact What We Look For
BS/MS/PhD in Computer Science, or a related field Deep hands-on experience writing and tuning compute kernels (CUDA, Triton, OpenCL, LLVM IR, assembly or similar sort) for ML workloads Strong knowledge of GPU/accelerator architecture: warp structure, memory hierarchy (global, shared, register, L1/L2 caches), tensor cores, scheduling, SM occupancy, etc. Experience with advanced optimization techniques: tiling, blocking, software pipelining, vectorization, fusion, loop transformations, auto-tuning Familiarity with ML-specific kernel libraries (cuBLAS, cuDNN, CUTLASS, oneDNN, etc.) or open kernels Strong debugging and profiling skills (Nsight, NVProf, perf, vtune, custom instrumentation) Experience reasoning about numerical stability, mixed precision, quantization, and error propagation Experience in integrating optimized kernels into real-world ML inference systems; exposure to distributed inference pipelines, memory management, and runtime systems Experience building high-performance products leveraging GPU acceleration Excellent communication and leadership skills — able to drive design discussions, mentor colleagues, and make trade-offs visible A track record of shipping performance-critical, high-quality production software Bonus: published in systems/ML performance venues (e.g. MLSys, ASPLOS, ISCA, PPoPP), experience with custom accelerators or FPGA, experience with sparsity or model compression techniques Pay Range Transparency
Local Pay Range: $190,900 — $232,800 USD Databricks is committed to fair and equitable compensation practices. The pay range(s) for this role are the expected salary range for non-commissionable roles or on-target earnings for commissionable roles. Actual compensation packages are based on factors including job-related skills, depth of experience, relevant certifications and training, and location. The total compensation package may 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. About Databricks
Databricks is the data and AI company. More than 10,000 organizations 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 social media. Benefits and Inclusion
Databricks strives to provide comprehensive benefits and perks that meet the needs of all employees. We are committed to fostering a diverse and inclusive culture and to equal employment opportunities for qualified individuals regardless of protected characteristics. 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.
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As a staff software engineer for GenAI Performance and Kernel, you will own the design, implementation, optimization, and correctness of the high-performance GPU kernels powering our GenAI inference stack. You will lead development of highly-tuned, low-level compute paths, manage trade-offs between hardware efficiency and generality, and mentor others in kernel-level performance engineering. You will work closely with ML researchers, systems engineers, and product teams to push the state-of-the-art in inference performance at scale. What You Will Do
Lead the design, implementation, benchmarking, and maintenance of core compute kernels (e.g. attention, MLP, softmax, layernorm, memory management) optimized for various hardware backends (GPU, accelerators) Drive the performance roadmap for kernel-level improvements: vectorization, tensorization, tiling, fusion, mixed precision, sparsity, quantization, memory reuse, scheduling, auto-tuning, etc. Integrate kernel optimizations with higher-level ML systems Build and maintain profiling, instrumentation, and verification tooling to detect correctness, performance regressions, numerical issues, and hardware utilization gaps Lead performance investigations and root-cause analysis on inference bottlenecks, e.g. memory bandwidth, cache contention, kernel launch overhead, tensor fragmentation Establish coding patterns, abstractions, and frameworks to modularize kernels for reuse, cross-backend portability, and maintainability Influence system architecture decisions to make kernel improvements more effective (e.g. memory layout, dataflow scheduling, kernel fusion boundaries) Mentor and guide other engineers working on lower-level performance, provide code reviews, help set best practices Collaborate with infrastructure, tooling, and ML teams to roll out kernel-level optimizations into production, and monitor their impact What We Look For
BS/MS/PhD in Computer Science, or a related field Deep hands-on experience writing and tuning compute kernels (CUDA, Triton, OpenCL, LLVM IR, assembly or similar sort) for ML workloads Strong knowledge of GPU/accelerator architecture: warp structure, memory hierarchy (global, shared, register, L1/L2 caches), tensor cores, scheduling, SM occupancy, etc. Experience with advanced optimization techniques: tiling, blocking, software pipelining, vectorization, fusion, loop transformations, auto-tuning Familiarity with ML-specific kernel libraries (cuBLAS, cuDNN, CUTLASS, oneDNN, etc.) or open kernels Strong debugging and profiling skills (Nsight, NVProf, perf, vtune, custom instrumentation) Experience reasoning about numerical stability, mixed precision, quantization, and error propagation Experience in integrating optimized kernels into real-world ML inference systems; exposure to distributed inference pipelines, memory management, and runtime systems Experience building high-performance products leveraging GPU acceleration Excellent communication and leadership skills — able to drive design discussions, mentor colleagues, and make trade-offs visible A track record of shipping performance-critical, high-quality production software Bonus: published in systems/ML performance venues (e.g. MLSys, ASPLOS, ISCA, PPoPP), experience with custom accelerators or FPGA, experience with sparsity or model compression techniques Pay Range Transparency
Local Pay Range: $190,900 — $232,800 USD Databricks is committed to fair and equitable compensation practices. The pay range(s) for this role are the expected salary range for non-commissionable roles or on-target earnings for commissionable roles. Actual compensation packages are based on factors including job-related skills, depth of experience, relevant certifications and training, and location. The total compensation package may 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. About Databricks
Databricks is the data and AI company. More than 10,000 organizations 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 social media. Benefits and Inclusion
Databricks strives to provide comprehensive benefits and perks that meet the needs of all employees. We are committed to fostering a diverse and inclusive culture and to equal employment opportunities for qualified individuals regardless of protected characteristics. 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.
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