Software Engineer, Machine Learning Infrastructure - Gen AI

DoorDash

San Francisco, CA

Full Time

Mid Level

137k-202k

8 days ago

Job Description

About the Role

The Machine Learning Platform team at DoorDash builds the infrastructure and tools that enable scalable and efficient machine learning across the company. They are responsible for developing and maintaining core ML infrastructure, including data pipelines, model training and serving frameworks, feature stores, and supporting large language model (LLM) deployment to enable real-time retrieval, generation, and personalization. The team works closely with product teams to deliver high-performance, reliable, and scalable machine learning solutions that drive business impact. The role of a Machine Learning Infrastructure Engineer involves designing, building, and optimizing LLMOps infrastructure, with a focus on generative AI, data pipelines, guardrails, model fine-tuning, and AI frameworks, in a hybrid work environment in San Francisco, Sunnyvale, or Seattle.

Key Responsibilities

Design, build, and optimize LLMOps infrastructure such as generative AI gateway, data pipelines for RAG systems, guardrails, batch inference, and model fine-tuning.
Engineer sophisticated data pipelines to power Retrieval-Augmented Generation (RAG) systems, connecting models to real-time data for improved accuracy and relevance.
Develop robust AI guardrails to ensure models operate safely, ethically, and responsibly.
Pioneer development of advanced tool-calling capabilities and AI Agent frameworks to enable models to execute complex tasks as intelligent agents.
Build and optimize scalable systems for batch inference and model fine-tuning to customize state-of-the-art models for specific use cases.
Collaborate with ML and Product Engineers to evolve a reliable, scalable, and highly observable ML platform.

Requirements

Industry experience: 2+ years building ML infrastructure or related disciplines.
Familiarity with large language models (LLMs) and related technologies including prompt engineering, fine-tuning, retrieval-augmented generation (RAG).
Problem-solving skills with the ability to tackle complex technical challenges.
Passion for working at the intersection of machine learning and data engineering.

Nice to Have

Experience with popular vector databases such as Qdrant, Pinecone, or FAISS.
Familiarity with modern LLM frameworks and their interaction with vector search.
Experience with real-time data processing frameworks like Spark, Flink, or Kafka.
Experience with cloud-based infrastructure (AWS, GCP, Azure) for data and model deployment.
Knowledge of retrieval optimization techniques for large-scale systems.
Experience with AI Agent SDKs such as OpenAI Agents, Google ADK, CrewAI, LangGraph.

Qualifications

Educational background or certifications are not explicitly specified, but relevant industry experience is required.

Benefits & Perks

Competitive salary within the range of $137,100-$201,600 USD, determined by skills, experience, and location.
Opportunities for equity grants.
Comprehensive benefits including 401(k) with employer matching, 16 weeks paid parental leave, wellness benefits, commuter benefits, paid time off, and paid sick leave.
Medical, dental, and vision benefits, 11 paid holidays, disability and life insurance, family-forming assistance, mental health programs.
Paid time off: flexible vacation, sick leave, and accrual details based on role type.

Working at DoorDash

DoorDash is committed to empowering local economies and fostering a diverse, inclusive community. They value innovation, quick learning, and impactful decision-making, supporting employee happiness, health, and well-being through comprehensive benefits and perks. The company emphasizes a non-discriminatory environment, encouraging applicants from all backgrounds, including women, non-binary individuals, LGBTQIA+ community, veterans, and others, to apply. They prioritize creating an inclusive workplace where everyone has the opportunity to succeed.

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