Staff Software Engineer - GenAI inference
$191k - $233k • Remote • San Francisco, California
Posted 26d ago
Job Location
San Francisco, California
Tech Stack
Remote Work Policy
Fully remote
Categories
LLM Engineer
About the job
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.
Responsibilities
- Own and drive the architecture, design, and implementation of the inference engine and collaborate on a model-serving stack optimized for large-scale LLMs inference.
- Partner with researchers to integrate new model architectures or features into the engine.
- Lead end-to-end optimization for latency, throughput, memory efficiency, and hardware utilization across GPUs and accelerators.
- Define and guide standards for building and maintaining instrumentation, profiling, and tracing tooling.
- Architect scalable routing, batching, scheduling, memory management, and dynamic loading mechanisms for inference workloads.
- Ensure reliability, reproducibility, and fault tolerance in inference pipelines, including A/B launches, rollback, and model versioning.
- Collaborate cross-functionally on integrating with federated, distributed inference infrastructure.
- Drive cross-team collaboration with platform engineers, cloud infrastructure, and security/compliance teams.
- Represent the team externally through benchmarks, whitepapers, and open-source contributions.
Requirements
- BS/MS/PhD in Computer Science, or a related field.
- 6+ years of software engineering experience in performance-critical systems.
- Proven track record of owning complex system components and driving architectural decisions.
- 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 (RPC frameworks, queuing, 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 and work closely with ML researchers.
- 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.
Benefits
- Eligibility for annual performance bonus
- Equity
- Comprehensive benefits and perks