Software Engineer, Machine Learning Infrastructure - Generative AI at DoorDash USA

Department: 313 Infrastructure EngineeringEducation: education_optional

Compensation

$137,100 – $201,600/yr

Location: San Francisco, California · Sunnyvale, California · +1
Type: Full-time
Level: mid
Posted: Jun 17, 2026

Description

DoorDash is hiring a GenAI Platform Engineer to build production infrastructure for its evaluation and LLM observability platform. The role focuses on evaluation SDKs, OpenTelemetry trace and score ingestion, LLM-as-judge and offline/online evaluation pipelines, agent simulations, data pipelines, backend services, and observability. The engineer will partner with ML, product, data science, and platform teams to create reliable measurement and quality primitives, support rapid experimentation, and improve model and agent quality across DoorDash, Wolt, and Deliveroo. The position requires a computer science degree or equivalent, at least three years of software engineering experience, strong Python and distributed systems skills, and production experience with services, APIs, data pipelines, ML infrastructure, observability, and evaluation systems.

Required Skills

Python
Distributed systems
OpenTelemetry
Data pipelines
LLM observability
Agent simulations
Kubernetes
AWS/GCP

Additional Information

Minimum Education: Bachelor of Science, Master of Science, or PhD in Computer Science, or equivalent
Required Previous Experiences: At least 3 years of industry software engineering experience
Experience building production services, APIs, data pipelines, or ML infrastructure at scale
Experience operating systems in production, including observability, debugging, reliability, incident response, and performance or cost optimization
Hands-on experience with evaluation, LLM observability, or measurement systems for ML/LLM products in production
Preferred Qualifications: Depth in evaluation methodology, including LLM-as-judge design and calibration, drift detection, human-in-the-loop labeling, or eval harness design
Experience with LLM observability and tracing, including OpenTelemetry, trace and score ingestion, and instrumentation SDKs
Experience building and deploying AI agents or MCP servers in production, including agent evaluation or simulation
Experience with data pipelines, streaming ingestion, and analytical stores for high-volume telemetry
Experience with LLM gateways, model routing, vendor abstraction, or cost attribution
Experience building developer platforms, internal platforms, or self-serve infrastructure
Experience with Kubernetes, cloud infrastructure, or high-throughput batch systems
Experience with RAG, search, vector databases, or open-weights LLM inference and fine-tuning
Translation Company Name: DoorDash USA