Staff Software Engineer - GenAI Performance and Kernel

P-1285

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

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

[https://www.databricks.com/sites/default/files/2024-08/us-pay-zone-mapping.pdf].

Local Pay Range

$190,900—$232,800 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

[https://twitter.com/databricks], LinkedIn

[https://www.linkedin.com/company/databricks] and Facebook

[https://www.facebook.com/databricksinc].

Benefits

At Databricks, we strive to provide comprehensive benefits and perks that meet

the needs of all of our employees. For specific details on the benefits offered

in your region click here

[https://docs.google.com/document/d/154un3e8Xav4BceOSlcYFZRGEuQI54xMxVydRwQn54eQ/edit? usp=sharing].

Our Commitment to Diversity and Inclusion

At Databricks, we are committed to fostering a diverse and inclusive culture

where everyone can excel. We take great care to ensure that our hiring practices

are inclusive and meet equal employment opportunity standards. Individuals

looking for employment at Databricks 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, 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.