Senior Machine Learning Systems Engineer (Hybrid)

Doordashusa; San Francisco, Sunnyvale, New York, Seattle (CA, CA, NY, WA); Hybrid; USD 204,000 - 299,000; Full time

Benefits offered by this job

401(k) Match

Parental Leave

Wellness Benefits

Paid Time Off

Job summary

DoorDash is seeking a Software Engineer, Machine Learning to design, implement, and validate algorithmic improvements for the catalog system and the product knowledge graph. You will use robust data and ML infrastructure to deploy new ML solutions that improve discovery, standardization, and semantic richness of our product knowledge graph.

This hybrid role involves in-office collaboration and will report to an Engineering Manager, with opportunities to build scalable ML systems and drive

Qualifications

• MS/PhD in a technical field (CS/math/stats/physics)
• 8+ years ML industry experience
• Experience with end-to-end ML lifecycle and production
• Experience with data pipelines and scalable ML systems

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 understand bid density and market competitiveness on revenue.
• Develop data solutions (embeddings, consumer profiles) for targeted audiences.
• Own end-to-end ML lifecycle: ideation, training, deployment, testing and monitoring.
• Scale data/ML infrastructure to support Ads data applications and experimentation.
• Grow and improve ML systems to support business growth.

Skills

ML algorithms

Production ML

ML lifecycle

Collaboration

Education

M.S./Ph.D. in CS/Math/Stats/Physics

Tools

PyTorch

Keras

LightGBM

scikit-learn

Spark ML

PySpark

Snowflake SQL

Job description

DoorDash is seeking a Software Engineer, Machine Learning to design, implement, and validate algorithmic improvements for the catalog system and the product knowledge graph. You will use robust data and ML infrastructure to deploy new ML solutions that improve discovery, standardization, and semantic richness of our product knowledge graph.

This hybrid role involves in-office collaboration and will report to an Engineering Manager, with opportunities to build scalable ML systems and drive