Staff Machine Learning Engineer, DoorDash
New Verticals Search
$176-238k
Salary dependent on location
Local food delivery platform
Job no longer available
Local food delivery platform
1001+ employees
Job no longer available
$176-238k
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
- Expertise in applied ML for Search/NLP/IR/Product Knowledge Graph - both classical and deep learning based. Additional familiarity with explore/exploit/MAB algorithms, LLMs, and causal inference techniques
- Machine learning background in Python; experience with PyTorch or TensorFlow preferred
- Familiarity with Kotlin/Scala
- Ability to communicate technical details to nontechnical stakeholders
- M.S., or PhD. in Statistics, Computer Science, Math, Operations Research, Physics, Economics, or other quantitative fields
- You keep the mission in mind, take ideas and help them grow using data and rigorous testing, show evidence of progress and then double down
- Desire for impact with a growth-minded and collaborative mindset
What the job involves
- As a Staff Machine Learning Scientist, you’ll be conceptualizing, designing, implementing, and validating algorithmic improvements to the search and personalization experiences at the heart of our fast growing grocery and retail delivery business
- In this role, you will leverage our robust data and machine learning infrastructure to implement new ML solutions to make the consumer search experience more relevant, seamless, and delightful across grocery, convenience, and many other retail categories
- You will be expected to demonstrate a strong command of production level machine learning, a passion for solving end-user problems, and collaborate well with multi-disciplinary teams
- Develop production machine learning solutions to build a world class personalized shopping experience for a diverse and expanding retail space
- Partner with engineering and product leaders to help shape the product roadmap leveraging ML
- Mentor junior team members, and lead cross functional pods to generate collective impact
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 10 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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