Software Engineer - GenAI inference

Benefits

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Market context

Median wage (BLS OEWS)

$116,543 national median

Projected growth (BLS Employment Projections)

+9.8% - Much faster than average

49% above the BLS national median 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.

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Weekend work

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About this role

Software Engineer - GenAI inference San Francisco, California 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

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