Machine Learning Engineer, DoorDash
Conversation AI
$140.1-210k
+ Equity
More information about location
Local food delivery platform
Open for applications
Local food delivery platform
1001+ employees
Open for applications
$140.1-210k
+ Equity
More information about location
1001+ employees
Company mission
To empower local economies by connecting food lovers with great local restaurants
Role
Who you are
- 3+ years of industry experience developing optimization models with business impact, including 1+ year(s) of industry experience serving in a tech lead role
- M.S., or PhD. in Statistics, Computer Science, Math, Operations Research, Physics, Economics, or other quantitative field
- You must be located near one of our engineering hubs which includes: San Francisco, Sunnyvale, Los Angeles, Seattle, and New York
- Deep understanding of natural language processing techniques and procedures for efficiently acquiring and validating human-labeled data
- Good experience in overall big data analysis, system, backed integration with new ML system/solution
- Good understanding of quantitative disciplines such as statistics, machine learning, operations research, and causal inference
- Familiarity with programming languages e.g. python and machine learning libraries e.g. SciKit Learn, Spark MLLib
- Experience productionizing and A/B testing different machine learning models
- Familiarity with advanced causal inferences techniques and contextual bandit algorithms preferred
What the job involves
- As a Machine Learning Engineer you will have the opportunity to identify and prioritize machine learning investments across our conversation AI & personalization ecosystem
- You will leverage our robust data and infrastructure to develop natural language processing and personalization models that impact millions of users across our three audiences
- You will partner with an engineering lead and product manager to set the strategy that moves the business metrics which help us grow our business
- Lead the development of DoorDash's support chatbot & LLM system: Applying LLM, active learning, semi- supervised learning, weak label generation, documentation embedding/retrieval and data augmentation strategies to improve the consumer, dasher, and merchant support experience
- Drive the personalization of DoorDash's issue prediction & resolution policies: Using both personalization, recommendation and dynamic pricing modeling technologies to serve millions of customers on personalized prediction resolution for any issues they might encounter during their journey
- Spearhead the creation of next-generation LLM AI Agent tools: Building Co-pilot system to evolve how millions of users interact with our support system
- Apply stratification, variance reduction, and other advanced experiment design techniques to create A/B tests to efficiently measure the impact of your innovations while minimizing risk to the broader system
Otta's take
Theo Margolius
COO of Otta
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.
Insights
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 values
- We are doers
- We are learners
- We are leaders
- We are one team
Company HQ
Mid-Market, San Francisco, CA
Founders
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.
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