Production ML Engineer for Scalable Ad Systems

DoorDash, Inc.; New York (NY); Hybrid; USD 137,000 - 299,000; Full time

Benefits offered by this job

Equity grants

401(k) and health benefits

Job summary

DoorDash is seeking a Software Engineer, Machine Learning to design, implement, and validate ML solutions powering our global ads and knowledge graph systems. You will own the end-to-end ML lifecycle, from ideation to deployment, collaborating with cross-functional teams in a hybrid work model.

Ideal candidates have advanced degrees in technical fields and 3+ years of ML in production, with hands-on experience in PyTorch/Keras and scalable data pipelines.

Qualifications

• MS or PhD in a technical field (CS/Math/Stats/Physics).
• 3+ years ML industry experience with fundamentals.
• Experience building data/feature pipelines at scale (PySpark, Snowflake SQL).
• Experience with production ML systems using 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 impact.
• Develop data solutions (embeddings and consumer profiles) to target audiences.
• Oversee end-to-end ML lifecycle: ideation, training, deployment, experiments, monitoring.
• Extend data/ML infrastructure for Ads data applications.
• Scale systems to support business growth.

Skills

ML experience

Production ML

PySpark

Snowflake SQL

PyTorch

Keras

LightGBM

scikit-learn

Spark ML

Online advertising

Search relevance

Recommendation systems

Education

MS/PhD in technical field

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 ML solutions powering our global ads and knowledge graph systems. You will own the end-to-end ML lifecycle, from ideation to deployment, collaborating with cross-functional teams in a hybrid work model.

Ideal candidates have advanced degrees in technical fields and 3+ years of ML in production, with hands-on experience in PyTorch/Keras and scalable data pipelines.