Machine Learning Engineer, DoorDash
New Verticals
$140.1-210.1k
+ Equity grants
Local food delivery platform
Job no longer available
Local food delivery platform
1001+ employees
Job no longer available
$140.1-210.1k
+ Equity grants
1001+ employees
Company mission
To empower local economies by connecting food lovers with great local restaurants
Role
Who you are
- We’re looking for someone with a command of production-level machine learning and experience with solving end-user problems who enjoys collaborating with multidisciplinary teams
- 1+ years of industry experience post PhD or 3+ years of industry experience post graduate degree of developing machine learning models with business impact
- Experience with machine learning methods in Recommendation System, Search, Causal Inference, Optimization, Time Series, Natural Language Processing, Large Language Model, Computer Vision
- Machine learning background in Python; experience with PyTorch, TensorFlow, or similar frameworks
- M.S., or PhD. in Statistics, Computer Science, Math, Operations Research, Physics, Economics, or other quantitative fields
- The desire for impact with a growth-minded and collaborative mindset
What the job involves
- You’ll be conceptualizing, designing, implementing, and validating algorithmic improvements to the ranking, fulfillment and catalog system 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 consumer shopping journey delightful, fulfillment system efficient and knowledge graph accurate
- You will report to the engineering manager on our New Verticals ML team
- Develop production machine learning solutions to solve various shopping and dashing problems including recommendation, search, logistic optimization, product knowledge graph building
- Partner with engineering, product, and business strategy leaders to help shape an ML-driven product roadmap and grow a multi-billion dollar retail delivery business
- Find new ways to use diverse data sources, intuitive models, and flexible experimentation to create a world-class shopping and dashing experience
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.
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