Senior Software Engineer, Machine Learning Infrastructure - Gen AI Data

DoorDash; San Francisco (CA); Hybrid; USD 167,000 - 247,000; Full time

Job summary

An innovative technology and logistics company is seeking a Senior Software Engineer to join their Machine Learning Platform team. In this dynamic role, you will design and optimize data pipelines that enhance the efficiency and accuracy of large language model applications. Collaborating closely with product teams, you will tackle complex challenges in a fast-paced environment. This hybrid position offers the opportunity to work from the San Francisco HQ 2-3 days a week, contributing to impactful projects that drive business success. Join a forward-thinking organization committed to diversity and inclusion, and help shape the future of machine learning infrastructure.

Benefits

401(k) plan with employer matching

Paid time off

Paid sick leave

16 weeks of paid parental leave

Wellness benefit

Commuter benefit match

Medical, dental, and vision benefits

Disability and life insurance

Family-forming assistance

Mental health program

Qualifications

• 5+ years of experience in ML infrastructure or related disciplines.
• Strong background in building and optimizing data pipelines.

Responsibilities

• Design and build high-performance data pipelines for LLMs.
• Enhance the reliability and scalability of data infrastructure.

Skills

Machine Learning Infrastructure

Data Pipeline Optimization

Real-time Data Processing

Problem Solving

Vector Databases

Education

Bachelor's Degree in Computer Science or related field

Tools

Qdrant

Pinecone

FAISS

Spark

Flink

Kafka

AWS

GCP

Azure

Job description

Senior Software Engineer, Machine Learning Infrastructure - Gen AI Data

The Machine Learning Platform team builds the infrastructure and tools that enable scalable and efficient machine learning across the company. Our team is responsible for developing and maintaining the core ML infrastructure, including data pipelines, model training and serving frameworks, and feature stores. We also support large language model (LLM) deployment, enabling real-time retrieval, generation, and personalization. We work closely with product teams to deliver high-performance, reliable, and scalable machine learning solutions that drive business impact.

About the Role

As a Machine Learning Infrastructure Engineer, you will be responsible for designing, building, and optimizing data pipelines that feed into RAG systems. You will work with diverse data sources, building scalable indexing pipelines and ensuring high-performance data ingestion into vector databases. Your work will enable real-time retrieval and enhance the accuracy and efficiency of LLM-based applications. This is a hybrid role and you will be expected to be in our San Francisco HQ 2-3 days a week.

You’re excited about this opportunity because you will…

• You thrive on zero-to-one projects and are excited about fast development cycles, driving quick iterations and impactful outcomes.
• Design and build high-performance, flexible data pipelines that can quickly adapt to new technologies, techniques, and modeling approaches for LLMs.
• Enhance the reliability, scalability, and observability of our data and inference infrastructure to support LLM-driven applications.
• Work closely with ML Engineers and Product Engineers to evolve the ML platform as per their use cases.
• Improve the reliability, scalability, and observability of our inference infrastructure.

We’re excited about you because…

• You have industry experience: 5+ years of building ML infrastructure or related disciplines.
• You have a strong background in building and optimizing data pipelines in production.
• You understand the nuances of vector databases and their role in RAG systems.
• You are experienced in working with large-scale data infrastructure and real-time processing.
• You are a problem solver — you thrive on tackling complex technical challenges.
• You are passionate about working at the intersection of machine learning and data engineering.

Nice To Haves

• Experience with popular vector databases such as Qdrant, Pinecone, or FAISS.
• Familiarity with modern LLM frameworks and how they interact 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.

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 includes opportunities for equity grants. Talk to your recruiter for more information.

DoorDash cares about you and your overall well-being. That's why we offer a comprehensive benefits package to all regular employees, which includes a 401(k) plan with employer matching, paid time off and paid sick leave in compliance with applicable laws, 16 weeks of paid parental leave, a wellness benefit, and a commuter benefit match.

Additionally, for full-time employees, DoorDash offers medical, dental, and vision benefits, 11 paid holidays, disability and basic life insurance, family-forming assistance, and a mental health program, among others.

The base pay for this position ranges from our lowest geographical market up to our highest geographical market within the United States.

$167,800 — $246,800 USD

About DoorDash

At DoorDash, our mission to empower local economies shapes how our team members move quickly, learn, and reiterate in order to make impactful decisions that display empathy for our range of users—from Dashers to merchant partners to consumers. We are a technology and logistics company that started with door-to-door delivery, and we are looking for team members who can help us go from a company that is known for delivering food to a company that people turn to for any and all goods.

Our Commitment to Diversity and Inclusion

We’re committed to growing and empowering a more inclusive community within our company, industry, and cities. That’s why we hire and cultivate diverse teams of people from all backgrounds, experiences, and perspectives.