Software Engineer, Machine Learning Infrastructure, Generative AI

DoorDash, Inc

Confirmed live yesterday High trust

Quick summary

Work type
On-site
Location
San Francisco, CASunnyvale, CASeattle, WA
Salary
$137,100–$201,600 / yr
Posted
86 days ago
Freshness
Confirmed live yesterday

Market check

Salary context

Below market

How this pay compares to similar roles

Similar $220k
This role $169k
$122k most similar roles pay here $281k

This role pays less than 79% of similar roles. Most pay $184,625–$254,750 — the shaded band above. At the midpoint, this role pays about $169k versus about $220k for comparable roles.

Based on 240 similar postings.

Employer

About DoorDash, Inc

DoorDash, Inc. is an American company operating online food ordering and food delivery. It trades under the symbol DASH. With a 56% market share, DoorDash is the largest food delivery platform in the United States.

DoorDash, Inc currently has 187 open roles on FindRole.

Listed pay typically runs $144,800–$212,950 across 166 roles with salary data.

Most-posted roles

View all roles at DoorDash, Inc

At a glance

TL;DR · Software Engineer, Machine Learning Infrastructure, Generative AI

Software Engineer, Machine Learning Infrastructure - Generative AI joins the GenAI Platform team to build shared infrastructure for deploying generative AI products and automation. You will develop production systems focused on a unified evals platform, including evaluation SDKs, OpenTelemetry-based trace and score ingestion, LLM-as-judge workflows, agent simulations, and backend services. The role involves building tools for LLM observability, guardrails, cost attribution, and open-weights model serving to ensure high-quality, reliable outputs across various platforms. You will utilize Python and distributed systems to build scalable data pipelines and infrastructure that support rapid experimentation while meeting production standards for latency and reliability. This position addresses the technical challenge of moving generative AI from prototype to production by creating robust measurement primitives, allowing teams to evaluate, trace, and improve LLM and agent systems across a complex ecosystem of models and services.

What you'll do

  • Build infrastructure to move GenAI products from prototype to production across multiple platforms.
  • Develop evaluation SDKs, LLM-as-judge workflows, and agent simulation systems.
  • Design scalable pipelines for OpenTelemetry trace/score ingestion and LLM observability.
  • Create tools for tracking model quality, detecting regressions, and comparing different models.
  • Build backend services for the Agent Gateway, open-weights model serving, and cost attribution.
  • Develop data pipelines to process high-volume telemetry and evaluation signals.
  • Maintain production standards for latency, scale, monitoring, and operational excellence.
  • Translate emerging GenAI capabilities into durable platform primitives for internal and external use.

What we're looking for

  • Hold a B.S., M.S., or PhD. in Computer Science or an equivalent degree.
  • Have 3+ years of industry experience in software engineering.
  • Possess strong backend engineering fundamentals, specifically in Python and distributed systems.
  • Experience building production services, APIs, data pipelines, or ML infrastructure at scale.
  • Experience operating systems in production including observability, debugging, reliability, and performance/cost optimization.
  • Have hands-on experience with evaluation, LLM observability, or measurement systems for ML products.
  • Proficiency in using AI coding tools throughout the full software development lifecycle.

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