Staff Software Engineer - GenAI inference

Cacheflow; San Francisco (CA); On-site; USD 190,900 - 232,800; Full time

Benefits offered by this job

Annual performance bonus

Equity options

Comprehensive health benefits

Job summary

A leading data and AI company is seeking a Staff Software Engineer for GenAI inference to lead the architecture and optimization of the inference engine. The role requires expertise in CUDA, GPU programming, and distributed systems design. Ideal candidates will have a strong software engineering background and a proven ability to collaborate with researchers and drive architectural decisions. Competitive compensation is offered, with a salary range of $190,900 to $232,800 USD.

Qualifications

• 6+ years of experience in performance-critical systems.
• Proven track record of owning complex system components.
• Hands-on experience with CUDA and GPU programming.

Responsibilities

• Own and drive the architecture and implementation of the inference engine.
• Lead the optimization for latency and throughput across GPUs.
• Collaborate with researchers to integrate new model architectures.

Skills

CUDA programming

GPU programming

Distributed systems design

Communication skills

Performance optimization

Education

BS/MS/PhD in Computer Science

Tools

cuBLAS

cuDNN

NCCL

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 10,000 organizations worldwide — including Comcast, Condé Nast, Grammarly, and over 50% of the Fortune 500 — rely on the Databricks Data Intelligence Platform to unify and democratize data, analytics and AI. Databricks is headquartered in San Francisco, with offices around the globe and was founded by the original creators of Lakehouse, Apache Spark™, Delta Lake and MLflow. To learn more, follow Databricks on Twitter, LinkedIn, and Facebook.

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, please visit https://www.mybenefitsnow.com/databricks.

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.