Staff Machine Learning Engineer - DashPass

About The Team

DashPass is DoorDash’s subscription loyalty program that delivers lower delivery fees and a host of additional benefits and value to a large subscriber base of both paid and sponsored subscriptions. DashPass subscribers enjoy lower delivery fees, faster ETAs, 3rd party partnerships, and special discounts and promotions to get maximum value of their membership.

Several teams are part of the DashPass org including Growth, Habituation, Member Experience, Exclusive Offers, and Partnerships. All of these teams require personalized targeting of offers and promotions to entice active Doordash consumers to sign up for DashPass and actively engage with their subscription in a personalized way, as well as reduce churn by offering personalized incentives to DashPass subscribers to keep using their subscription.

We are forming a new team that will leverage AI and advanced ML to power decision making in real-time – from personalized sign up promotions to progressive reward systems, and to pre-cancel offers that retain subscribers.

DashPass is continuously building new benefits and offerings to drive more value for our subscribers, and personalization is our next big bet to help efficiently grow our subscriber base to 2030 and beyond.

About The Role

We’re looking for a Staff Machine Learning Engineer to drive the design and development of large-scale ML/optimization systems to target personalization efforts across the DashPass Subscriber journey.

You're excited about this opportunity because...

Contribute to Causal inference modeling to measure the incremental impact of DashPass Subscriber acquisition and retention strategies.

Incentive optimization frameworks that personalize progressive rewards to improve spend efficiency.

Budget allocation and forecasting models that identify optimal spend across acquisition, referrals, and retention.

Partner closely with Product, Data Science, and Engineering teams to design experiments, model frameworks, and production ML systems that directly impact DashPass subscriber growth metrics.

Provide technical mentorship and guidance to engineers and cross-functional partners — leading through influence, not management.

Build and deploy 0→1 ML systems that improve subscriber outcomes and marketplace health.

Set best practices for model training, evaluation, deployment, and monitoring

This is a highly impactful IC role for someone who enjoys combining economic intuition, large-scale ML modeling, and system design to solve complex real-world optimization problems.

We’re excited about you because you have…

M.S. or Ph.D. in Computer Science, Machine Learning, Statistics, or a related field.

8+ years of industry experience building production-scale ML systems.

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

Strong understanding of probability theory, statistics, and machine learning fundamentals.

Strong programming skills in Python, Java, or C++, and experience with ML frameworks such as TensorFlow, PyTorch, or XGBoost.

Interest in building and leading a new team that has broad impact across a wide range of problem spaces to support a critical business line.

Proven ability to lead cross-functional initiatives and drive complex technical projects end-to-end.

Excellent communication skills — able to explain technical concepts to product, business, and engineering audiences.

Experience In Subscriptions Growth Or Marketplace Systems Is A Plus.

About The Team

The Storage teams build and operate online stateful systems and abstractions that are reliable, efficient, secure and easy to use for DoorDash Engineering. The teams are responsible for understanding Product Engineering’s evolving needs and developing platform and infrastructure capabilities to serve them. The team currently supports CockroachDB, Cassandra, Kafka and Redis as well as data abstraction services to reduce the complexity of interacting with storage systems for Product Engineers.

About The Role

We’re hiring a

Data Solutions Engineer

with deep expertise in distributed databases, particularly Apache Cassandra, Redis, Kafka, and database agnostic abstractions. In this role, you will design, optimize, and scale distributed data access layers that power DoorDash’s most critical systems, ensuring high availability, low latency, and fault tolerance.

You’ll serve as a hands-on architect and technical partner to product engineering and infrastructure teams, helping translate complex business requirements into resilient and scalable data models. Your work will directly influence the evolution of

Taulu

, DoorDash’s unified storage abstraction layer, by shaping best practices and identifying platform gaps through real world engagements.

This is a

high-impact, cross functional role

that combines deep technical expertise with a customer centric approach. You’ll lead solutioning engagements from design through production, drive the adoption of Taulu modeling best practices, and ensure that our systems meet goals around reliability, cost efficiency, and velocity. You must be located in San Francisco, Sunnyvale, Seattle or New York for this hybrid opportunity.

You’re excited about this opportunity because you will…

Design and implement

highly scalable, fault tolerant distributed database solutions using Taulu, Apache Cassandra, Redis, Kafka, and other paved path storage solutions.

Architect and optimize

multi-region, globally distributed systems to meet our high standards for availability, latency, and throughput.

Lead data modeling, performance tuning, and capacity planning

for large-scale, mission-critical storage workloads.

Partner with product engineering and infrastructure teams

to deeply understand domain specific data needs and guide them in adopting paved path storage solutions.

Serve as the DRI for solutioning engagements

, owning modeling in Taulu from experimentation through launch and scale.

Shape the evolution of Taulu

by identifying abstraction gaps and converting customer feedback into platform improvements.

Apply workload-aware design

patterns, including caching strategies, partitioning, and consistency tuning to improve performance and efficiency.

Drive adoption of operational best practices

across observability, schema design, capacity planning, and cost optimization across storage systems.

Promote clarity and continuity

by contributing to solutioning playbooks, decision logs, and architectural documentation.

We’re excited about you because…

You have 10+ years of experience designing and scaling distributed data systems, with deep expertise in NoSQL technologies like Apache Cassandra, DynamoDB, or ScyllaDB.

You have a strong command of distributed system concepts such as replication, partitioning, tunable consistency, and failure recovery.

You’ve led data modeling efforts for high-throughput, low-latency workloads and understand the real-world trade-offs involved in NoSQL schema design.

You are experienced with caching technologies like Redis or Memcached and know how to layer them effectively over storage systems to optimize for performance and cost.

You have a customer-first mindset, and thrive when working closely with product and platform teams to translate complex requirements into clean, scalable data models.

You are skilled at communicating complex architecture decisions and building alignment across infrastructure and product engineering organizations.

You have a track record of mentoring engineers, influencing data architecture at scale, and fostering best practices in reliability, observability, and data access patterns.

You document decisions, share learnings, and take pride in contributing to reusable playbooks and durable frameworks for others to build upon.

Bonus: You’ve worked on or contributed to open-source distributed databases.

Notice Regarding Use Of Ai And Automated Tools

To streamline our hiring process, DoorDash utilizes an automated recruitment tool called Gem.

How It Works

Gem 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 Gem 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

and applicable state laws. In compliance with New York City Local Law 144, the independent bias audit summary for Gem is publicly available for review at our

Careers Page

.

Notice to Applicants for Jobs Located in NYC or Remote Jobs Associated With Office in NYC Only

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 in NYC. As part of the hiring and/or promotion process, we provide Covey with job requirements and candidate submitted applications. We began using

Covey Scout for Inbound

from August 21, 2023, through December 21, 2023, and resumed using

Covey Scout for Inbound

again on June 29, 2024.

The Covey tool has been reviewed by an independent auditor. Results of the audit may be viewed here:

Covey

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 inclu