Senior Applied ML Engineer: Scalable Systems & Optimization

Databricks; San Francisco (CA); On-site; USD 126,000 - 210,000; Full time

Job summary

Databricks in San Francisco is seeking a Senior Applied ML Engineer on the Applied AI team to use ML, scheduling, and optimization to maximize infrastructure efficiency and performance across the full stack from cluster management to query compilation.

You'll solve high-impact engineering problems to deliver optimized workloads for customers, build ML pipelines and production models, and shape the roadmap for applied ML investments with engineering and product leaders.

Qualifications

• Background in Computer Science and a Master’s degree in ML/Data Science or related field.
• Experience building, training, and deploying ML models in production.
• Familiarity with cloud computing, distributed systems, and data processing frameworks.
• Proficiency in Python, Scala, or Java.

Responsibilities

• Drive serverless growth through optimization techniques to improve scale and efficiency.
• Design end-to-end ML4Sys solutions within a lean team of domain experts.
• Define the roadmap for applied ML investments with engineering and product leaders.
• Architect, train, and deploy state-of-the-art models to improve product performance and cost efficiency.
• Build robust ML pipelines, data processing layers, model serving components, and monitoring.
• Research and implement novel modeling techniques for distributed environments.

Skills

Python

Scala

Java

ML in production

Distributed systems knowledge

Education

Master's degree in ML/Data Science

PhD in AI/Data Science (preferred)

Tools

Cloud computing

Distributed systems

Job description

Databricks in San Francisco is seeking a Senior Applied ML Engineer on the Applied AI team to use ML, scheduling, and optimization to maximize infrastructure efficiency and performance across the full stack from cluster management to query compilation.

You'll solve high-impact engineering problems to deliver optimized workloads for customers, build ML pipelines and production models, and shape the roadmap for applied ML investments with engineering and product leaders.