Applied Machine Learning Engineer – Causal Inference Recommendation
| Company | DoorDash |
|---|---|
| Location | Seattle, WA, USA, San Francisco, CA, USA, New York, NY, USA, Sunnyvale, CA, USA |
| Salary | $137100 – $299300 |
| Type | Full-Time |
| Degrees | Master’s, PhD |
| Experience Level | Junior, Mid Level |
Requirements
- 1+ years of industry experience post PhD or 3+ years of industry experience post graduate degree of developing machine learning models with business impact
- Expertise in applied ML for Causal Inference and Recommendation Systems – both classical and deep learning based
- M.S., or PhD. in Statistics, Computer Science, Economics, Math, Operations Research, Physics, or other quantitative fields
- Ability to communicate technical details to nontechnical stakeholders
- Strong machine learning background in Python; experience with Spark, PyTorch or TensorFlow preferred
- Familiarity with Kotlin/Scala
Responsibilities
- Develop production machine learning solutions, which is a central intelligence to power multiple teams including Sales Operation, Product, and Marketing
- Partner with engineering and product leaders to help shape the product roadmap leveraging AI/ML
- Own the modeling life cycle end-to-end including feature creation, model development and deployment, experimentation, monitoring and explainability, and model maintenance
- Find new ways to use diverse data sources, and modeling techniques, such as NLP, ranking, personalization, image classification, and entity resolution to touch base with merchants at the right time and provide AI driven world class merchant experience.
Preferred Qualifications
- Experience with Spark, PyTorch or TensorFlow preferred
- The desire for impact with a growth-minded and collaborative mindset