Staff Machine Learning Engineer, DoorDash
MerchantMerchant Tax Categorization Service
$201.9-302.9k
+ Equity
Local food delivery platform
Job no longer available
Local food delivery platform
1001+ employees
Job no longer available
$201.9-302.9k
+ Equity
1001+ employees
Company mission
To empower local economies by connecting food lovers with great local restaurants
Role
Who you are
- We’re looking for a passionate staff level Applied Machine Learning expert to join our team
- 6+ years of industry experience leading and developing advanced machine learning models with business impact, and shipping ML solutions to production
- Expertise in applied ML for deep learning, NLP, LLM, and multi-modality models
- M.S., or PhD. in Statistics, Computer Science, Economics, Math, Physics, or other quantitative fields
- Ability to communicate technical details to nontechnical stakeholders
- Strong machine learning background in Python; experience with Spark, PyTorch or TensorFlow preferred
- Familiarity with Kotlin/Scala
- 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
- The desire for impact with a growth-minded and collaborative mindset
What the job involves
- We're looking for an experienced machine learning engineer to help us develop the AI/ML to power DoorDash growing Merchants
- In this role, you will utilize our robust data and machine learning infrastructure to develop a comprehensive AI/ML service to create accurate tax categorization for billions of restaurants and non-restaurants items at DoorDash
- You will be expected to lead and build a production-level AI/ML solution, collaborate cross-functionally, and push the boundaries of LLM capabilities and lead the AI product strategy for the team to execute
- Lead and develop cutting-edge ML-driven tax catalog solutions, utilizing Generative AI to efficiently manage and organize tax categorization information
- Design ML products to solve large scale categorization problems transaction level for DoorDash
- Lead with engineering and product leaders to shape the product roadmap leveraging AI/ML
- Own the modeling life cycle end-to-end including feature creation, model development and deployment, experimentation, monitoring and explainability, and model maintenance
- Being exposed to new opportunities where AI/ML can be used as a lever that benefits new business, new markets, and new regions
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.
Salary benchmarks
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