Staff Software Engineer - GenAI inference
Posted by employer 1 year ago
First seen on Joblaze 6 months ago
Last verified on the company career page 2 hours ago
Skills & Technologies
Requirements
- Experience
- 6+ years
- Education
- Bachelor's degree
Not disclosed in this posting: work arrangement, visa sponsorship.
Joblaze summary
In the role of Staff Software Engineer for GenAI inference at Databricks, the individual will lead the design and optimization of an inference engine that supports large-scale model APIs. Key skills include a strong background in performance-critical systems, expertise in ML inference internals, and hands-on experience with GPU programming and distributed systems. This position is ideal for seasoned engineers with a proven track record in complex system architecture and a collaborative mindset, particularly those who can bridge the gap between research and production.
Joblaze insights
- Listed about 6 months ago — first seen on Joblaze April 14, 2026. Last confirmed on Databricks's careers page October 28, 2026.
- Salary band is in line with the typical range for AI/ML roles (median ~$190,000).
- Starts above 19% of 54 comparable staff ai/ml roles in United States that list AI/ML we track (median $246,701 across 22 companies). See AI/ML salary trends
- AI/ML appears in 34.4% of 282 comparable staff ai/ml roles in United States; GPU programming appears in 0.4% of 282 comparable staff ai/ml roles in United States.
Quick facts
- What's the salary range?
- Databricks lists $190,900–$232,800 for this role.
- How much experience is required?
- At least 6 years of relevant experience for this Staff Software Engineer - GenAI inference role.
- What's the tech stack?
- Joblaze extracted these technologies from the posting: AI/ML, CUBLAS, CUDA, GPU programming, NCCL, RPC frameworks.
- What seniority level is this role?
- Databricks targets staff-level candidates for this position.
- Is this full-time or contract?
- Full-time for this Staff Software Engineer - GenAI inference role at Databricks.
From the original posting
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.
About Databricks
BenefitsStandard company text repeated across Databricks's postings is omitted here.