Senior Applied ML Engineer (ML4Sys)

Location: Must be based in San Francisco, California

Salary: $16,000 — $21,000 USD

Company

Databricks is a data and AI company providing a Data Intelligence Platform to unify data, analytics, and AI for over 10,000 organizations worldwide.

What you will do

• Drive scaling and efficiency of serverless compute products through advanced optimization techniques.
• Design and build end-to-end ML4Sys solutions from the ground up within a specialized domain team.
• Define the roadmap for applied ML investments by collaborating with engineering and product leadership.
• Architect, train, and deploy state-of-the-art models to directly improve product performance and cost efficiency.
• Build robust ML pipelines, data processing layers, and production monitoring systems.
• Research and implement novel modeling techniques tailored for computer systems and distributed environments.

Requirements

• Master's degree in Machine Learning, Data Science, Computer Science, or a related computational field.
• Strong background in building, training, and deploying machine learning models in production.
• Practical familiarity with cloud computing, distributed systems, and modern data processing frameworks.
• Proficiency in Python, Scala, or Java.
• Location: Must be based in San Francisco, California

Nice to have

• PhD in AI, Data Science, or a related technical discipline.
• 4+ years of machine learning engineering experience in high-velocity, high-growth environments.
• Deep understanding of computer architecture, distributed computing, database internals, or networking.
• Experience with operations research, forecasting, or Markov decision processes for sequential decision making.
• Proven track record of optimizing large-scale distributed systems via data-driven approaches.

Culture & Benefits

• Comprehensive benefits and perks tailored to the region.
• Eligibility for annual performance bonuses and equity packages.
• Inclusive work environment committed to diversity and equal employment opportunity standards.