Job description

P-1285 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 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. 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 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 $190,900 — $232,800 USD About Databricks Databricks is the Data and AI company. More than 20,000 organizations worldwide — including adidas, AT&T, Bayer, Block, Mastercard, Rivian, Unilever, and 70% of the Fortune 500 — rely on the Databricks Data + AI Platform to build and scale data and AI apps, analytics and agents. Headquartered in San Francisco with 30+ offices around the globe, Databricks offers a unified platform that includes Genie, Lakebase, Agent Bricks, Lakeflow, Lakehouse, and Unity Catalog. To learn more, follow Databricks on LinkedIn , X , YouTube , and Instagram . 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 . 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.

Requirements

Must-haves

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
Our Commitment to Diversity and Inclusion

Nice-to-haves

Bonus: published research or open-source contributions in ML systems, inference optimization, or model serving

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

apimlai

Platform: greenhouse
Effort: quick