Senior Applied ML Engineer - ML4Sys
Databricks2 months ago
Base Salary
$16k - $21k/yr
Responsibilities
- Drive scaling and efficiency improvements for Databricks serverless compute products through advanced optimization techniques.
- Design end-to-end ML4Sys solutions from the ground up.
- Define the roadmap for applied ML investments in collaboration with engineering and product leaders.
- Architect, train, and deploy models that improve product performance and cost efficiency.
- Build ML pipelines, data processing layers, model-serving components, and production monitoring systems.
- Research and implement modeling techniques for computer systems and distributed environments.
Requirements
- A background in Computer Science and a master's degree in Machine Learning, Data Science, or a related computational field are required.
- Strong experience building, training, and deploying machine-learning models in production is required.
- Practical familiarity with cloud computing, distributed systems, and modern data processing frameworks is required.
- Proficiency in Python, Scala, or Java is required.
- A PhD in AI, Data Science, or a related technical discipline is preferred.
- 4+ years of machine-learning engineering experience in a high-velocity, high-growth environment is preferred.
- Strong knowledge of computer architecture, distributed computing, cloud compute, database internals, or networking is preferred.
- Experience with operations research, forecasting, Markov decision processes, or other optimization algorithms for sequential decision-making is preferred.
- A proven record of optimizing large-scale distributed systems or cloud infrastructure through data-driven approaches is preferred.
Benefits
- Comprehensive employee benefits and perks, with details varying by region.
Tech Stack
Categories
Data EngineeringML Engineering
About Databricks
Databricks builds a cloud-based data and AI platform centered on the lakehouse architecture, combining data engineering, analytics, and machine learning with Apache Spark, Delta Lake, and MLflow. It sells subscriptions and cloud services to enterprises that need to unify data pipelines and develop large-scale AI and analytics. Founded in 2013 by the creators of Apache Spark and headquartered in San Francisco, the company is privately held and serves organizations across many industries.