Machine Learning Engineer, Marketplace Optimization

About The Team

The mission of the Marketplace Optimization team is to ensure we maintain a healthy Ads Marketplace across all our verticals for both search (query context) and discovery experiences while fulfilling the requirements of all players in this marketplace.

Marketplace Optimization is a critical part of the Ads Delivery funnel with a broad charter responsible for Bidding, Auction Design, Budget Pacing, Forecasting, and Ads Experimentation. Our work directly shapes advertiser experience, consumer experience, and marketplace balance. We leverage artificial intelligence and advanced ML, deep learning techniques to power decision-making in real time — from optimizing ad auctions to generating the most efficient bids and pacing budgets dynamically. These models sit at the heart of DoorDash’s ad delivery and play a pivotal role in improving the efficiency, fairness, and scalability of our marketplace.

The opportunity is massive as DoorDash expands into new verticals like Grocery and Retail while building unique innovative ad products to leverage the closed loop marketplace.

About The Role

We’re looking for a Machine Learning Engineer to help design, build, optimize and scale large-scale ML systems within the Ads Delivery funnel.

Design, build, and deploy ML models and pipelines for pacing, bidding, auction and targeting optimization.

Collaborate with Data Science and Product teams to develop and evaluate new algorithms through rigorous experimentation.

Improve and scale existing ML infrastructure and data pipelines in partnership with Platform and Infra teams.

Write high-quality, maintainable code and participate in system design and peer reviews.

Learn from senior engineers and contribute to technical discussions that shape the team’s roadmap.

Partner with Data Science and Marketing to design and execute lift tests; collaborate with Platform teams on budget A/B testing and evaluation framework.

This is a high-impact role for someone who enjoys combining economic intuition, large-scale ML modeling, and applied engineering to solve complex real-world optimization problems.

You’re excited about this opportunity because you will…

Own impactful ML systems: Build and improve models that directly have a large impact on top and bottom line financials.

Drive experimentation: Rapidly test hypotheses via robust sequential experiments; measure and explain your models’ impact on marketplace KPIs

Optimize at scale: Work with one of the largest delivery datasets, building optimization pipelines that consider budget, fairness, assignment rates, and more

Collaborate cross-functionally: Partner with engineering, analytics, product, and operations to iterate quickly, moving models from prototype to production

Shape the future: We're one of the fastest growing Ads platforms in the world and we're looking to take that even further!

We’re excited about you because you have…

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

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

Industry experience building or maintaining machine learning systems in production.

Solid understanding of machine learning fundamentals, statistics, and data modeling.

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

Excellent communication and collaboration skills — comfortable working with cross-functional partners in Product, DS, and Engineering.

Curiosity and a growth mindset — motivated to learn, iterate quickly, and take ownership of impactful projects.

Familiarity with auction systems, bidding, forecasting, or budget optimization (or other experience in ads or marketplaces) is a plus.

Familiarity with experimentation science, including experience designing lift tests; marketplace incrementality experience 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

T