Staff Machine Learning Engineer, Personalization

The posting, as DoorDash wrote it

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 modern growth and personalization 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 Engineer, you’ll be conceptualizing, designing, implementing, and validating algorithmic improvements to the growth and personalization experiences 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 the consumer search experience more relevant, seamless, and delightful across grocery, convenience, and many other retail categories. You will demonstrate a strong command of production level machine learning, experience with solving end-user problems, and collaborate well with multi-disciplinary teams.

You will report into the engineering manager on our Personalization 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 applying ML
• Mentor junior team members, and lead cross functional pods to create collective impact

You can find out more on our ML blog here

We’re excited about you because you have…

• 8+ years of industry experience developing machine learning models with business impact, and shipping ML solutions to production.
• Proficiency in using AI coding tools (e.g., Claude Code, Codex, Cursor) in the full software development lifecycle, including designing, generating code, testing, monitoring and releasing software
• M.S., or PhD. in Statistics, Computer Science, Math, Operations Research, Physics, Economics, or other quantitative field
• Expertise in applied ML for Causal Inference and Recommendation Systems - both classical and deep learning based. Additional familiarity with explore/exploit/MAB algorithms & LLMs is a plus.
• Machine learning background in Python; experience with PyTorch or TensorFlow preferred.
• Ability to communicate technical details to nontechnical stakeholders
• 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

Data Retention, Privacy & Bias Audit: Data collected during this process is retained in accordance with our Candidate Privacy Policy and applicable state laws. In compliance with New York City Local Law 144, the independent bias audit summary for Gem is publicly available fo
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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.

Notice Regarding Use of AI and Automated Tools: To streamline our hiring process, DoorDash utilizes an automated recruitment tool called Metaview.

How it works: Metaview assists our recruiting team by evaluating job related qualifications and characteristics in connection with hiring. The tool is designed and used to support - rather than replace - human decision-making; trained personnel make final decisions with meaningful human review and oversight, and DoorDash does not use Metaview or other AI-enabled tool in a manner that has the effect of subjecting applicants or employees to discrimination based on any protected characteristic or proxy or for engaging in any protected activity under applicable law.

Data Retention, Privacy & Bias Audit: Data collected during this process is retained in accordance with our Candidate Privacy Policy https://help.doordash.com/en-us/legal/article/ax-privacy-notice and applicable state laws. In compliance with New York City Local Law 144, the independent bias audit summary for Metaview is publicly available for review at https://trust.warden-ai.com/metaview/application-review .