Senior Applied ML Engineer, Scalable Serverless Systems

Databricks; California (MO); On-site; USD 166,000 - 210,000; Full time

Job summary

Databricks is seeking a Senior Applied ML Engineer on the Applied AI team to utilize machine learning, scheduling, and optimization to maximize infrastructure efficiency and performance. Your work spans from cluster management to query compilation, solving high-impact engineering problems to deliver optimized workloads for customers.

This role demands strong CS and ML foundations, production ML experience, cloud and distributed systems knowledge, and proficiency in Python, Scala, or Java.

Qualifications

• Education: Master’s in ML/Data Science or related field.
• ML Experience: building, training, and deploying models in production.
• Infrastructure Knowledge: cloud computing, distributed systems, data processing frameworks.
• Core Coding: Python, Scala, or Java.

Responsibilities

• Accelerate serverless growth through optimization of compute products.
• Build end-to-end ML4Sys solutions within a lean team.
• Define the roadmap for applied ML investments with engineering/product leaders.
• Architect, train, and deploy state-of-the-art models to improve performance and cost.
• Scale ML pipelines and production monitoring for large workloads.
• Research novel modeling techniques for distributed environments.

Skills

Python

Scala

Java

Education

Master's degree in ML/Data Science

PhD (preferred)

Tools

Cloud platforms

Distributed data processing

Job description

Databricks is seeking a Senior Applied ML Engineer on the Applied AI team to utilize machine learning, scheduling, and optimization to maximize infrastructure efficiency and performance. Your work spans from cluster management to query compilation, solving high-impact engineering problems to deliver optimized workloads for customers.

This role demands strong CS and ML foundations, production ML experience, cloud and distributed systems knowledge, and proficiency in Python, Scala, or Java.