Senior ML Engineer - Systems & Optimization

Menlo Ventures; San Francisco (CA); On-site; USD 16,000 - 21,000; Full time

Job summary

Databricks in San Francisco is seeking a Senior Applied ML Engineer on the Applied AI team to apply ML, scheduling, and optimization to maximize infrastructure efficiency. You will work across the stack—from cluster management to query compilation—solving high-impact engineering problems to deliver optimized, cost-effective workloads for customers.

You will shape the roadmap for applied ML investments, deploy state-of-the-art models, and build scalable ML pipelines and production monitoring to

Qualifications

• Master's degree in Machine Learning, Data Science, or related computational field.
• Strong background in building, training, and deploying ML models in production.
• Practical familiarity with cloud computing, distributed systems, and modern data processing frameworks.
• Proficiency in Python, Scala, or Java.

Responsibilities

• Accelerate Serverless Growth through optimization techniques.
• 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, and production monitoring systems.
• Research and implement novel modeling techniques for distributed environments.

Skills

Python

Scala

Java

ML experience

Education

Master's degree in ML/Data Science

PhD preferred

Job description

Databricks in San Francisco is seeking a Senior Applied ML Engineer on the Applied AI team to apply ML, scheduling, and optimization to maximize infrastructure efficiency. You will work across the stack—from cluster management to query compilation—solving high-impact engineering problems to deliver optimized, cost-effective workloads for customers.

You will shape the roadmap for applied ML investments, deploy state-of-the-art models, and build scalable ML pipelines and production monitoring to