Staff Software Engineer, Machine Learning - Personalization

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.

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

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

Compensation

The successful candidate's starting pay will fall within the pay range listed below and is determined based on job-related factors including, but not limited to, skills, experience, qualifications, work location, and market conditions. Base salary is localized according to an employee's work location. Ranges are market-dependent and may be modified in the future. In addition to base salary, the compensation for this role includes opportunities for equity grants. Talk to your recruiter for more information.

The national base pay ranges for this position within the United States, including Illinois and Colorado:

• I4 $137,100—$201,600 USD
• I5 $167,800—$246,800 USD
• I6 $203,500—$299,300 USD

Benefits

DoorDash cares about you and your overall well-being. That's why we offer a comprehensive benefits package to all regular employees, which includes a 401(k) plan with employer matching, 16 weeks of paid parental leave, wellness benefits, commuter benefits match, paid time off and paid sick leave in compliance with applicable laws (e.g. Colorado Healthy Families and Workplaces Act). DoorDash also offers medical, dental, and vision benefits, 11 paid holidays, disability and basic life insurance, family-forming assistance, and a mental health program, among others. To learn more about our benefits, visit our careers page here.

Paid Time Off Details

• For salaried roles: flexible paid time off/vacation, plus 80 hours of paid sick time per year.
• For hourly roles: vacation accrued at about 1 hour for every 25.97 hours worked (e.g. about 6.7 hours/month if working 40 hours/week; about 3.4 hours/month if working 20 hours/week), and paid sick time accrued at 1 hour for every 30 hours worked (e.g. about 5.8 hours/month if working 40 hours/week; about 2.9 hours/month if working 20 hours/week).