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Staff Software Engineer - GenAI inference

Databricks - San Francisco, California

Posted Oct 8, 2025

Benefits

Parental leave
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Non-birth-parent leave
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Family-building benefits
  • Fertility benefits: Not verified
  • Adoption assistance: Not verified
  • Surrogacy assistance: Not verified
Mental health support
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Relocation assistance
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Childcare support
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Learning budget
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Verification
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Salary
$191K-$233K From the posting source checked Jun 20, 2026
401(k) match
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Market context

U.S. role benchmark (BLS OEWS)
$116,543 U.S. median for this role
Projected growth (BLS Employment Projections)
+9.8% - Much faster than average

82% above the BLS role benchmark for software engineering aggregate.

Matched to SOC 15-1252 - Software Engineering aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Role

Role function
Engineering From the posting source checked Jun 20, 2026
Seniority
Staff Plus From the posting source checked Jun 20, 2026

Schedule

Shift type
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Weekend work
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Company

Company stage
Public-company From the posting source checked Jun 20, 2026
Equity
Offered From the posting source checked Jun 20, 2026

Application

Cover letter
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Assessment
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Deadline
Not stated

Where they hire

State eligibility is not yet verified.

About this role

Staff Software Engineer - GenAI inference San Francisco, California 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

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