Machine Learning Engineer, Ads & Optimization (Hybrid)

DoorDash USA; Sunnyvale, San Francisco, Seattle, New York (CA, CA, WA, NY); Hybrid; USD 137,000 - 299,000; Full time

Benefits offered by this job

401(k) matching

Paid parental leave

Wellness benefits

Commuter benefits

Paid time off

Health insurance

Dental & vision benefits

Disability & life insurance

Job summary

DoorDash is hiring Software Engineer, Machine Learning to design, implement, and validate ML solutions that power our catalog system and product knowledge graph. This role spans end-to-end ML lifecycle and requires collaboration with multiple teams in Sunnyvale, San Francisco, Seattle, and New York.

You’ll build scalable ML models, pipelines, and data solutions to improve ads efficiency and platform revenue while working with production infrastructure and a strong ML stack.

Qualifications

• M.S. or PhD in a technical field such as CS, math, stats, physics, or equivalent.
• 3+ years ML industry experience with strong fundamentals.
• Experience with data/feature pipelines at scale and ML systems in production.

Responsibilities

• Develop advanced ML models to improve ads efficiency and quality.
• Design optimization algorithms for budget pacing and automated bidding.
• Establish a data-driven framework to understand bid density and market competitiveness.
• Develop data solutions like embeddings and consumer profiles.
• Own end-to-end ML lifecycle from ideation to deployment and monitoring.
• Extend ML infra to support Ads data applications and experimentation.

Skills

ML model development

Pyspark & Snowflake SQL

PyTorch / Keras / scikit-learn

Production ML systems

Education

M.S. in a technical field (CS, math, stats, physics)

Tools

PyTorch

Keras

LightGBM

scikit-learn

Spark ML

PySpark

Snowflake SQL

Job description

DoorDash is hiring Software Engineer, Machine Learning to design, implement, and validate ML solutions that power our catalog system and product knowledge graph. This role spans end-to-end ML lifecycle and requires collaboration with multiple teams in Sunnyvale, San Francisco, Seattle, and New York.

You’ll build scalable ML models, pipelines, and data solutions to improve ads efficiency and platform revenue while working with production infrastructure and a strong ML stack.