Senior Machine Learning Software Engineer, DoorDash
Fraud
$170.6-255.8k
+ Equity grants; Salary dependent on location
Local food delivery platform
Job no longer available
Local food delivery platform
1001+ employees
Job no longer available
$170.6-255.8k
+ Equity grants; Salary dependent on location
1001+ employees
Company mission
To empower local economies by connecting food lovers with great local restaurants
Role
Who you are
- 5+ years of industry experience developing machine learning models with business impact, and shipping ML solutions to production
- Experience building data/feature engineering pipelines at scale using Pyspark and Snowflake SQL
- Experience with building machine learning systems in production by using frameworks such as PyTorch, Keras, lightgbm, scikit-learn, Spark ML, or related
- Experiences in any of the following areas are preferred but not required:
- Online advertising
- Search relevance & ranking
- Recommendation system
- We’re looking for someone with a command of production-level machine learning and experience with solving end-user problems who enjoys collaborating with multi-disciplinary teams
What the job involves
- We are looking for Software Engineers, Machine Learning to build and maintain a large scale 24x7 global infrastructure system that powers DoorDash's 3-sided marketplace of Consumers, Merchants and Dashers
- As a Software Engineer, Machine Learning, you’ll be conceptualizing, designing, implementing, and validating algorithmic improvements to the catalog system and our product knowledge graph at the heart of our fast-growing grocery and retail delivery business
- You will use our robust data and machine learning infrastructure to implement new ML solutions to make our product knowledge graph accurate, standardized, semantically rich, easily discoverable, and extensible
- Will report to an Engineering Manager
- 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 the bid density and market competitiveness would affect advertising value and platform revenue
- Develop new data solutions (eg. embeddings and consumer profiles) to target the relevant audience
- Be responsible for the end-to-end ML lifecycle, including ideation, offline model training, online shadowing/deployment, experimentation, and post-launch monitoring/measurement
- Build and extend the current data/ML infrastructure to empower Ads data applications including data analysis, ML modeling, and experimentation
- Scale our systems and services to fuel the growth of our business
Our take
DoorDash was an early entrant into the last-mile restaurant delivery business, which is now crowded with the likes of Uber Eats, Postmates and GrubHub. They offer restaurants an end-to-end delivery platform, generating new business and access to a network of delivery drivers.
To differentiate itself in the market, DoorDash focused on areas with fewer competitors and found success in this untapped market. Other factors, such as the company's targeting of restaurants which many not typically offer a delivery service, also contribute to its profitability. The company made its stock market debut in 2020 with one of the biggest IPOs of the year.
However, in light of increasing discontent around DoorDash's commission structure from restaurants, competition remains fierce. The business recently responded by publishing a transparent fee structure, but the number of options open to restaurants means DoorDash must fight to keep its customers loyal. Despite this, by focusing on consumer retention, logistics and technology, the company is likely able to maintain its existing growth and revenue.
Freddie
Company Specialist at Welcome to the Jungle
Insights
13% employee growth in 12 months
Company
Funding (last 2 of 11 rounds)
Jun 2020
$400m
SERIES H
Nov 2019
$100m
SERIES G
Total funding: $2.5bn
Company benefits
- Company stock options
- Work from home stipend
- Unlimited paid time off policy
- Work from home opportunities
- Health insurance
Company HQ
Mid-Market, San Francisco, CA
Leadership
Tony Xu
(CEO)Tony studied at UCB and worked as a Matrix Partners Associate while studying for an MBA at Stanford. He combined this experience to co-found DoorDash in January 2013, and has served as CEO since June 2013.
Stanley Tang
(CPO)Previously studied Computer Science at Stanford, before working at Facebook as a Software Engineer.
Salary benchmarks
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