Doordash

Principal Machine Learning Engineer, Ads & Promos Delivery

Rol remoto de Machine Learning Engineering con fit claro de ubicación del candidato.

Publicado6 jul 2026

Países elegibles1 país aceptado

Señal de senioritySenior

Modelo de trabajoRemoto

Ubicaciones aceptadas para candidatos

Estados Unidos

Java LLM Python

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Actualidad de la fuente6 jul 2026

Fit de ubicación1 país aceptado

Match de stackJava, LLM

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Resumen del rol

Principal Machine Learning Engineer, Ads & Promos Delivery

Requisitos y responsabilidades

Contenido del rol extraído en secciones para revisar más rápido.

About the Role

Apply state-of-the-art machine learning and LLM techniques to problems across personalization, query understanding, user and content understanding.
Rigorously evaluate ML and LLM models using a combination of offline analysis and online experimentation, designing metrics and experiments that clearly measure quality, impact, and tradeoffs.
Own the full model lifecycle from research to production, including data analysis, model development, evaluation, offline and online A/B testing, and continuous iteration.
Partner closely with product managers, data scientists, and designers to ensure AI-driven systems deliver meaningful, user-facing improvements.
Stay at the forefront of ML and AI innovation by assessing emerging research and translating promising approaches into scalable, production-ready systems.

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.
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…

5+ years of experience building, deploying, and scaling ML and AI models for large-scale, user-facing or data-intensive products.
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
BS, MS, or PhD in Computer Science, Engineering, or a related field, or equivalent practical experience.
Deep expertise in one or more of the following areas: deep learning, large language models, information retrieval, ranking and relevance, recommendation systems, natural language processing, or content understanding.
Strong programming skills in Python, Java, or C++, with hands-on experience using ML frameworks such as PyTorch, TensorFlow, or XGBoost.
Extensive experience across the full ML lifecycle, including data analysis, feature engineering, iterative model development, rigorous offline and online evaluation, and ongoing monitoring and improvement.
Strong collaborator and communicator who thrives in fast-paced, cross-functional environments.
Product-minded and impact-driven, with a passion for applying cutting-edge ML and AI techniques to real-world problems.

Bonus Points For

Experience designing and deploying LLM-based systems, including prompt engineering and retrieval-augmented generation (RAG) architectures, Generative RecSys.
Experience solving large-scale, user-centric and content-centric personalization problems, including user modeling, retrieval, ranking, and relevance.
Demonstrated contributions to the ML community through open-source projects, publications, or applied research in areas such as ML, NLP, information retrieval, or related fields.

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.

See below for 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).

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