Software Engineer, Machine Learning Infrastructure - Generative AI

DoorDash’s GenAI Platform team sits within Machine Learning Platform and builds the shared infrastructure that helps DoorDash, Wolt, and Deliveroo teams safely bring GenAI-powered products, agents, automation, and personalization to production. Our mission is to increase the velocity of business impact from GenAI. A central pillar of that work is our evaluation platform — the unified evals backbone that lets teams measure, trace, and trust the quality of LLM and agent systems across the company, powering trace/score ingestion, LLM-as-judge workflows, agent simulations, and LLM observability for the tens of millions of daily requests flowing through our LLM Gateway. We also own core platform surfaces including the Agent Gateway, open-weights model serving and batch inference, guardrails, and cost attribution.

You will join a small, high-leverage team building production infrastructure for Generative AI at DoorDash, with a primary focus on our evals and LLM observability platform: the systems that let teams evaluate, trace, and continuously improve the quality of LLM and agent products. You’ll work across evaluation frameworks and SDKs, OpenTelemetry-based trace/score ingestion, LLM-as-judge and offline/online eval pipelines, agent simulations, data pipelines, backend services, and observability. This role is ideal for an engineer who enjoys building reliable measurement and quality primitives in a fast-moving technical area where product needs, model capabilities, vendor ecosystems, and evaluation methodologies are evolving quickly.

• Build the infrastructure that helps DoorDash teams move GenAI ideas from prototype to production, increasing the velocity of business impact from AI across the company.
• Work on our unified evals platform — evaluation SDKs, OpenTelemetry trace/score ingestion, LLM-as-judge, offline and online eval pipelines, and agent simulations — alongside the LLM Gateway, Agent Gateway, open-weights model serving, guardrails, and cost attribution.
• Design scalable systems for evaluation workflows, trace/score ingestion, LLM observability, and agent simulation that power real customer and internal automation use cases
• Raise the quality bar for GenAI at DoorDash — giving product teams trustworthy, low-friction ways to measure model and agent quality, catch regressions, and compare across open-weight and closed-source models with observability and cost controls built in.
• Build platforms that support rapid experimentation while meeting production standards for latency, scale, monitoring, SLOs, playbooks, and operational excellence.
• Partner closely with ML engineers, product engineers, data scientists, and platform teams across DoorDash, Wolt, and Deliveroo to turn emerging GenAI capabilities into durable platform primitives.
• Shape the future of DoorDash’s centralized GenAI platform — closing the loop from evaluation and agent observability to agent optimization, where eval signals and traces drive automated evaluation, agent simulation, and post-training techniques (e.g., reward modeling and RLHF/RLVR evaluation) — enabling the next generation of AI-powered products, agents, automation, and personalization.

• B.S., M.S., or PhD. in Computer Science or equivalent
• 3+ years of industry experience in software engineering
• Strong backend engineering fundamentals, especially 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, incident response, and performance/cost optimization.
• Hands-on experience with evaluation, LLM observability, or measurement systems for ML/LLM products in production — eval pipelines, tracing/scoring, offline/online quality metrics, or experimentation.
• 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

• Depth in evaluation methodology — LLM-as-judge design and calibration, judge/eval drift detection, human-in-the-loop labeling, or eval harness design for agents and multi-step systems
• Experience with LLM observability and tracing (e.g., OpenTelemetry, trace/score ingestion) and building 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 (e.g., SQL, columnar/OLAP) 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 (AWS/GCP), or high-throughput batch systems
• Experience with RAG, search, vector databases, or open-weights LLM inference and fine-tuning

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 by enabling door-to-door delivery, and we are looking for team members who can help us go from a company that is known as the place you order food to a company that people turn to for any and all goods.

DoorDash is growing rapidly and changing constantly, which gives our team members the opportunity to share their unique perspectives, solve new challenges, and own their careers. We're committed to supporting employees’ happiness, healthiness, and overall well-being by providing comprehensive benefits and perks including premium healthcare, wellness expense reimbursement, paid parental leave and more.

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. We believe that true innovation happens when everyone has room at the table and the tools, resources, and opportunity to excel.

Statement of Non-Discrimination: In keeping with our beliefs and goals, no employee or applicant will face discrimination or harassment based on: race, color, ancestry, national origin, religion, age, gender, marital/domestic partner status, sexual orientation, gender identity or expression, disability status, or veteran status. Above and beyond discrimination and harassment based on “protected categories,” we also strive to prevent other subtler forms of inappropriate behavior (i.e., stereotyping) from ever gaining a foothold in our office. Whether blatant or hidden, barriers to success have no place at DoorDash. We value a diverse workforce – people who identify as women, non-binary or gender non-conforming, LGBTQIA+, American Indian or Native Alaskan, Black or African American, Hispanic or Latinx, Native Hawaiian or Other Pacific Islander, differently-abled, caretakers and parents, and veterans are strongly encouraged to apply. Thank you to the Level Playing Field Institute for this statement of non-discrimination.

Pursuant to the San Francisco Fair Chance Ordinance, Los Angeles Fair Chance Initiative for Hiring Ordinance, and any other state or local hiring regulations, we will consider for employment any qualified applicant, including those with arrest and conviction records, in a manner consistent with the applicable regulation.

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