Machine Learning Engineer, DoorDash
Dasher Supply
$140.1-255.8k
+ Equity grants
Local food delivery platform
Open for applications
Local food delivery platform
1001+ employees
Open for applications
$140.1-255.8k
+ Equity grants
1001+ employees
Company mission
To empower local economies by connecting food lovers with great local restaurants
Role
Who you are
- High-energy and confident — 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
- You’re an owner — driven, focused, and quick to take ownership of your work
- Humble — you’re willing to jump in and you’re open to feedback
- Adaptable, resilient, and able to thrive in ambiguity — things change quickly in our fast-paced startup and you’ll need to be able to keep up!
- Growth-minded — you’re eager to expand your skill set and excited to carve out your career path in a hyper-growth setting
- Desire for impact — ready to take on a lot of responsibility and work collaboratively with your team
- 1+ years of industry experience post PhD or 3+ years of industry experience post graduate degree of developing advanced machine learning models with business impact
- M.S., or PhD. in Computer Science, Statistics, Operations Research or other related quantitative fields
- Strong background in machine learning and OSS ML technologies such as Spark, PyTorch, Airflow with hands-on experience in production
- Demonstrated expertise with programming languages e.g. python and machine learning libraries e.g. LightGBM, Spark MLLib, PyTorch, etc
- Deep understanding of complex systems such as Marketplaces, and domain knowledge in two or more of the following: Machine Learning, ML Ops, Causal Inference, and Operations Research
- Experience of shipping production-grade ML models and optimization systems, and designing sophisticated experimentation techniques
- You are located or are planning to relocate to San Francisco, CA, Sunnyvale, CA, or Seattle, WA
What the job involves
- As a Machine Learning Engineer, you will work with our robust data and machine learning infrastructure to deploy MLmodels and optimization programs
- Tackling Doordash’s most challenging business problems, our models power spend allocation across Dasher Acquisition (paid media marketing), Dasher Mobilization (proactive and reactive incentives) and Dasher Pay (base and bonus pay)
- You will work with other data scientists, engineers, and product managers to develop and iterate on models to help us grow our business and provide better service quality for our customers
- Use Causal ML and Optimization techniques to automate spend allocation across Dasher supply levers to ensure our roads are well supplied for a fantastic Dasher and Consumer experience
- Build ML models to better forecast Dasher actions in high dimensional contexts, which will serve as core inputs to our offline and online supply management systems
- Have end-to-end ownership of model ideation, development, testing, deployment, and maintenance
- Get a chance to platformize existing supply levers across new business verticals and geographies
- Be able to measure your business impact through rapid experimentation, making complex tradeoffs to balance different sides of the marketplace
- Work on complex systems, like those described in this blog post written by your future teammates
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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