Machine Learning Engineer - Delivery Excellence

$119,100 - $175,100/Yr

Doordash - San Francisco, CA

Full-time - Mid Level

San Francisco, CA

Couriers and Messengers

About the position

The Machine Learning Engineer role at DoorDash focuses on enhancing the delivery service quality for its three-sided marketplace of consumers, merchants, and dashers. This position involves leveraging robust data and machine learning infrastructure to develop models that impact millions of users and tackle significant business challenges. The role is integral to the Delivery Excellence team, which is a key area of investment for the company, aiming to solve complex problems at scale and improve overall service quality.

Responsibilities

• Build statistical and ML models that run in production to enhance consumer experience by reducing cancellations, pickup waiting times, delivery lateness, missing and incorrect items, and non-fulfilled orders.
• Own the modeling life cycle end-to-end including feature creation, model development and prototyping, experimentation, monitoring and explainability, and model maintenance.
• Identify new opportunities where delivery quality can be leveraged for demand shaping, search ranking, customer segmentation, etc.
• Mentor and uplevel a talented team of ML Engineers.

Requirements

• 1+ years of industry experience post PhD or 3+ years of industry experience post graduate degree in developing machine learning models with business impact.
• M.S. or PhD in Machine Learning, Statistics, Computer Science, Applied Mathematics, or other related quantitative fields.
• Demonstrated expertise with programming languages such as Python, SciKit Learn, Lightgbm, Spark MLLib, PyTorch, TensorFlow, etc.
• Deep understanding of complex systems such as Marketplaces, and domain knowledge in two or more of the following: Machine Learning, Causal Inference, Operations Research, Forecasting, and Experimentation.
• Experience of shipping production-grade ML models and optimization systems, and designing sophisticated experimentation techniques.

Nice-to-haves

• Experience in a fast-paced startup environment.
• Familiarity with data-driven decision-making processes.

Benefits

• 401(k) plan with employer match
• Paid time off
• Paid parental leave
• Wellness benefits
• Paid holidays
• Medical, dental, and vision benefits
• Disability and basic life insurance
• Family-forming assistance
• Commuter benefit match
• Mental health program