ML Engineer, Ads & Knowledge Graph Systems

Xapply; California, Northern (MO, KY); Hybrid; USD 137,000 - 299,000; Full time

Benefits offered by this job

401(k) matching

Paid parental leave

Wellness benefits

Commuter benefits match

Paid time off

Job summary

DoorDash is seeking a Software Engineer, Machine Learning to build and maintain a large-scale, 24x7 global infrastructure powering our grocery and retail delivery ecosystem. You will conceptualize, design, implement, and validate algorithmic improvements to the catalog system and knowledge graph with a production ML focus.

This hybrid role reports to an Engineering Manager and requires collaboration with cross-functional teams to deliver impactful ML solutions across ads, targeting, and revenue

Qualifications

• MS or PhD in a technical field such as CS, math, stats, physics or equivalent.
• 3+ years ML industry experience with solid ML fundamentals.
• Experience building data/feature pipelines at scale using PySpark and Snowflake SQL.
• Experience deploying ML systems in production with frameworks like PyTorch, Keras, lightgbm, scikit-learn, Spark ML.

Responsibilities

• Develop advanced ML models to improve ads efficiency and quality.
• Design optimization algorithms for budget pacing and automated bidding.
• Establish data-driven framework to assess bid density vs. market competitiveness.
• Develop data solutions (embeddings, consumer profiles) for targeted audiences.
• Own end-to-end ML lifecycle from ideation to deployment and monitoring.
• Extend data/ML infrastructure to support Ads data applications.
• Scale systems and services to support business growth.

Skills

ML algorithms

Pyspark

Snowflake SQL

PyTorch

Production ML

Education

MS or PhD in a technical field

Tools

PyTorch

Keras

LightGBM

scikit-learn

Spark ML

Job description

DoorDash is seeking a Software Engineer, Machine Learning to build and maintain a large-scale, 24x7 global infrastructure powering our grocery and retail delivery ecosystem. You will conceptualize, design, implement, and validate algorithmic improvements to the catalog system and knowledge graph with a production ML focus.

This hybrid role reports to an Engineering Manager and requires collaboration with cross-functional teams to deliver impactful ML solutions across ads, targeting, and revenue