Compensation Overview

About the Team

Come help us build the world’s most reliable on-demand, logistics engine for last-mile grocery and retail delivery! We’re looking for an experienced machine learning engineer to help us develop the cutting-edge machine learning models that power DoorDash’s growing grocery and retail business.

About the Role

We’re looking for a passionate Machine Learning Engineer to join our team. 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. 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.

You will report to the engineering manager on our New Verticals ML team. We expect this role to be hybrid with some time in-office and some time remote.

You’re excited about this opportunity because you will…

• 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. 

You can find out more on our ML blog post here

We’re excited about you because you have… 

• 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

About DoorDash

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.

Compensation

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.

We use Covey as part of our hiring and / or promotional process for jobs in NYC and certain features may qualify it as an AEDT. As part of the evaluation process we provide Covey with job requirements and candidate submitted applications. We began using Covey Scout for Inbound on June 20, 2024.

Please see the independent bias audit report covering our use of Covey here.