Staff Machine Learning Engineer - New Verticals Search
About the Team
Come help us build the world's most reliable on-demand, logistics engine for last-mile retail delivery! We're looking for an experienced machine learning engineer to help us develop the cutting edge search and information retrieval models that power DoorDash's growing retail and grocery business.
About the Role
We’re looking for a passionate Applied Machine Learning expert to join our team. 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.
You will report into the engineering manager on our New Verticals Consumer ML team. We expect this role to be hybrid with some time in-office and some time remote (#LI-Hybrid).
You’re excited about this opportunity because you will…
- 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.
You can find out more on our ML blog here
We’re excited about you because you have…
- 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
At DoorDash, our mission to empower local economies shapes how our team members move quickly, learn, and reiterate in order to make impactful decisions that display empathy for our range of users—from Dashers to merchant partners to consumers. We are a technology and logistics company that started with door-to-door delivery, and we are looking for team members who can help us go from a company that is known for delivering food to a company that people turn to for any and all goods.
DoorDash is growing rapidly and changing constantly, which gives our team members the opportunity to share their unique perspectives, solve new challenges, and own their careers. We're committed to supporting employees’ happiness, healthiness, and overall well-being by providing comprehensive benefits and perks including premium healthcare, wellness expense reimbursement, paid parental leave and more.
Our Commitment to Diversity and Inclusion
We’re committed to growing and empowering a more inclusive community within our company, industry, and cities. That’s why we hire and cultivate diverse teams of people from all backgrounds, experiences, and perspectives. We believe that true innovation happens when everyone has room at the table and the tools, resources, and opportunity to excel.
Statement of Non-Discrimination: In keeping with our beliefs and goals, no employee or applicant will face discrimination or harassment based on: race, color, ancestry, national origin, religion, age, gender, marital/domestic partner status, sexual orientation, gender identity or expression, disability status, or veteran status. Above and beyond discrimination and harassment based on “protected categories,” we also strive to prevent other subtler forms of inappropriate behavior (i.e., stereotyping) from ever gaining a foothold in our office. Whether blatant or hidden, barriers to success have no place at DoorDash. We value a diverse workforce – people who identify as women, non-binary or gender non-conforming, LGBTQIA+, American Indian or Native Alaskan, Black or African American, Hispanic or Latinx, Native Hawaiian or Other Pacific Islander, differently-abled, caretakers and parents, and veterans are strongly encouraged to apply. Thank you to the Level Playing Field Institute for this statement of non-discrimination.
Pursuant to the San Francisco Fair Chance Ordinance, Los Angeles Fair Chance Initiative for Hiring Ordinance, and any other state or local hiring regulations, we will consider for employment any qualified applicant, including those with arrest and conviction records, in a manner consistent with the applicable regulation.
If you need any accommodations, please inform your recruiting contact upon initial connection.
The location-specific base salary range for this position is listed below. Compensation in other geographies may vary.
Actual compensation within the pay range will be decided based on factors including, but not limited to, skills, prior relevant experience, and specific work location. For roles that are available to be filled remotely, base salary is localized according to employee work location. Please discuss your intended work location with your recruiter for more information.
DoorDash cares about you and your overall well-being, and that’s why we offer a comprehensive benefits package, for full-time employees, that includes healthcare benefits, a 401(k) plan including an employer match, short-term and long-term disability coverage, basic life insurance, wellbeing benefits, paid time off, paid parental leave, and several paid holidays, among others.
In addition to base salary, the compensation package for this role also includes opportunities for equity grants.