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Staff Software Engineer - AI/ML at Databricks - ApplyBlast

Staff Software Engineer - AI/ML

Databricks

3 months ago

4.1 on Glassdoor

New York City, New York

Onsite

Full Time

Senior

1 applicant

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About this role

A Staff Machine Learning Engineer at Databricks will join a small, nimble team to build CustomerLake, a Customer Data Platform delivering enterprise-grade ML and AI personalization. This 0-to-1 role involves evaluating LLM approaches, optimizing model behavior in production, and establishing the technical foundation for scalable personalization solutions across the business. The successful candidate will collaborate closely with product and design teams to translate ambiguous customer problems into practical, high-value outcomes.

Responsibilities

Evaluate ML and LLM approaches for customer personalization use cases
Build platform frameworks to optimize customers for real business value metrics
Set technical foundations and best practices for growing ML/AI products
Partner with product and design to convert ambiguous problems into scalable solutions
Inspect individual model traces and tune behavior to improve production quality

Skills

Qualifications

BS in Computer Science or related field10+ years of engineering experience

About Databricks

databricks.com

Headquarters

San Francisco, California, United States

Company Size

5001-10000 employees

Founded

2013

View Profile

Databricks is a unified Data Intelligence Platform that helps organizations build, deploy, and manage data, analytics, and AI at scale. Built on Apache Spark and Delta Lake, it provides a “lakehouse” architecture that combines data engineering, ETL, data warehousing, streaming, and machine learning (including MLflow and feature store) in a single managed service. The platform includes collaborative notebooks, scalable compute across major clouds, and built‑in governance, security, and performance optimizations. Enterprises use Databricks to simplify data pipelines, accelerate model development with a data‑centric approach, and operationalize analytics and AI.