Software Engineer, Machine Learning (All Levels / All Teams)
About this role
DoorDash builds a large-scale, continuous infrastructure system that powers a three-sided marketplace for consumers, merchants, and dashers. Engineers in this machine learning group conceptualize, design, implement, and validate algorithmic improvements for catalog systems and product knowledge graphs that support grocery and retail delivery operations. Practitioners manage the full machine learning lifecycle, from ideation and offline model training to online deployment and post-launch monitoring.
Team members develop optimization algorithms for automated bidding and budget pacing while establishing data-driven frameworks to measure market competitiveness. Candidates who possess a command of production-level machine learning, experience solving end-user problems, and a desire to collaborate across multi-disciplinary teams find a strong fit here. The organization operates on a hybrid schedule requiring regular in-office presence.
What you will do
- Develop advanced machine learning models to improve ads efficiency and quality.
- Design and build optimization algorithms for budget pacing and automated bidding to achieve various advertising goals.
- Establish a data-driven framework to understand how bid density and market competitiveness affect advertising value and platform revenue.
- Develop new data solutions such as embeddings and consumer profiles to target relevant audiences.
- Manage the end-to-end machine learning lifecycle including ideation, offline training, deployment, experimentation, and post-launch monitoring.
- Build and extend existing data and machine learning infrastructure to empower ads data applications.
What you need
- Hold a Master of Science or PhD in a technical field such as computer science, mathematics, statistics, or physics.
- Demonstrate at least 3 years of machine learning industry experience with a solid understanding of algorithms and fundamentals.
- Build data and feature engineering pipelines at scale using PySpark and Snowflake SQL.
- Build machine learning systems in production using frameworks like PyTorch, Keras, LightGBM, scikit-learn, or Spark ML.
Nice to have
- Online advertising experience
- Search relevance and ranking experience
- Recommendation system experience
Benefits
- 401(k) plan with employer matching
- 16 weeks of paid parental leave
- Comprehensive medical, dental, and vision insurance
- Flexible paid time off plus 80 hours of paid sick time per year for salaried roles
- Wellness and commuter benefits
- Disability, basic life insurance, and family-forming assistance
Skills and tech stack
Questions candidates ask
What is the remote work policy for this position?
This role is hybrid with some in-office time expected. Work locations include Sunnyvale, San Francisco, Seattle, and New York.
What is the salary range for this job?
The starting pay falls within a base salary range of $137,100 to $201,600 USD depending on the level, location, and qualifications. Additional compensation includes opportunities for equity grants.
What tools and frameworks are used on the team?
Engineers build data pipelines using PySpark and Snowflake SQL. Production machine learning systems utilize frameworks such as PyTorch, Keras, LightGBM, scikit-learn, and Spark ML.
Who does this position report to?
The role reports directly to an Engineering Manager. The position sits within the machine learning engineering organization.