Staff Software Engineer - AI Research Infrastructure (AI/distributed systems): Developing and operating the research infrastructure that schedules, orchestrates, and observes large-scale training and inference workloads across thousands of GPUs with an accent on reliable experiment pipelines, developer tooling, and secure resource management. Focus on designing cluster scheduling and job orchestration systems, enabling rapid research iteration, and scaling complex AI infrastructure from prototypes to stable services.
Location: New York City, New York, or San Francisco, California
Salary: $199,000–$270,000 USD annually
Company
Databricks is a data and AI company building a unified platform for data, analytics, AI applications, and agents.
What you will do
Design and build infrastructure for large-scale experiments, data processing, and model training across GPU fleets, HPC clusters, and cloud systems.
Develop services for job submission, scheduling, orchestration, monitoring, and observability of training and inference workloads.
Create experiment management, CI/testing, and developer tooling that improves research iteration speed.
Turn experimental workloads into reliable, repeatable pipelines in partnership with research scientists, ML engineers, and platform teams.
Shape the long-term roadmap for research computation and how models are trained, evaluated, and shipped.
Mentor engineers working on compute, infrastructure, and AI systems.
Requirements
BS, MS, or PhD in Computer Science or a related field.
5+ years of software engineering experience, including substantial experience with large-scale distributed systems or infrastructure.
Deep experience building and operating distributed systems, data pipelines, or large-scale backend services, ideally involving GPUs, clusters, or major cloud providers.
Proficiency in one or more systems programming languages, such as C++, Rust, Go, Java, or Scala.
Experience with cluster schedulers, resource managers, or job orchestration systems such as Kubernetes, Slurm, Ray, or comparable internal systems.
Understanding of modern ML training and inference workflows, including distributed training, model parallelism, fine-tuning, and evaluation.
Culture & Benefits
Work with research scientists, ML engineers, and platform teams to advance AI infrastructure.
Benefits and perks are provided according to the employee’s region.
Inclusive hiring practices and an environment designed to support diverse employees.
Compensation may also include an annual performance bonus, equity, and regional benefits.
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