About the Team

The Risk Machine Learning team builds cutting edge models that play central roles in our important anti-fraud systems. We operate at a massive scale, risking billions of events across 20 countries. We are looking for ML experts who can help us push the technology boundaries and make DoorDash the world’s most safe logistics engine!

About the Role

We’re looking for a passionate Technical Leader in Machine Learning Engineering to join our team. As a Staff Machine Learning Engineer, you will be a trusted advisor to both your manager and peers, extending your influence across teams within and beyond your area of expertise. You will play a pivotal role in shaping the team’s roadmaps, adeptly managing ambiguity, identifying opportunities, and translating them into clear objectives. You will be accountable for the formation and delivery of the team’s OKRs.

You will drive significant impact through a combination of direct contributions and by amplifying the capabilities of your team. You’ll be conceptualizing, designing, implementing, and validating algorithmic solutions to prevent, detect and mitigate risks in Fraud as well as Trust and Safety. You are excited about opportunities to introduce step function changes to our risking system. You will demonstrate a command of production level machine learning, experience solving end-user problems, and collaborate well with multi-disciplinary teams.

You will focus on eliminating single points of failure, including yourself, and work to uplift the skills and effectiveness of your team members. You will set and uphold the highest standards of engineering excellence, consistently identifying areas for improvement and fostering a culture of continuous growth.

You will report to the engineering manager on our Risk Machine Learning team in our Operation Excellence organization.

You’re excited about this opportunity because you will…

• Drive the long-term technical ML/AI vision in Fraud, an area that serves as one of the most critical components of the overall DoorDash business
• Develop State of the Art Machine Learning solutions
• Partner with engineering and product leaders to shape the product roadmap
• Mentor 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… 

• Experience leading a team of MLEs in solving hard problems
• 7 years of industry experience developing machine learning models and shipping ML solutions to production.
• Domain expertise in training deep neural nets, such as transformers and multi-task learning models. Proficiency in least one deep learning framework, for example PyTorch, TensorFlow
• M.S., or PhD. in Statistics, Computer Science, Math, Operations Research, Physics, Bioinformatics, Economics, or other quantitative fields
• Familiarity with a JVM language (Kotlin/Scala)
• Familiarity with LLMs and anomaly detection
• (Nice to have) Fraud domain knowledge

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.