Software Engineer, Machine Learning Infrastructure - Generative AI

About the Team 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. About the Role 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. You’re excited about this opportunity because you will… 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. We’re excited about you because… 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 Nice To Haves 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 Notice Regarding Use of AI and Automated Tools: To streamline our hiring process, DoorDash utilizes an automated recruitment tool called Gem. How it works: Gem assists our recruiting team by evaluating job related qualifications and characteristics in connection with hiring. The tool is designed and used to support - rather than replace - human decision-making; trained personnel make final decisions with meaningful human review and oversight, and DoorDash does not use Gem or other AI-enabled tool in a manner that has the effect of subjecting applicants or employees to discrimination based on any protected characteristic or proxy or for engaging in any protected activity under applicable law. Data Retention, Privacy & Bias Audit: Data collected during this process is retained in accordance with our Candidate Privacy Policy and applicable state laws. In compliance with New York City Local Law 144, the independent bias audit summary for Gem is publicly available for review at our Careers Page . 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, 16 weeks of paid parental leave, wellness benefits, commuter benefits match, paid time off and paid sick leave in compliance with applicable laws (e.g. Colorado Healthy Families and Workplaces Act). DoorDash also offers medical, dental, and vision benefits, 11 paid holidays, disability and basic life insurance, family-forming assistance, and a mental health program, among others. To learn more about our benefits, visit our careers page here . See below for paid time off details: For salaried roles: flexible paid time off/vacation, plus 80 hours of paid sick time per year. For hourly roles: vacation accrued at about 1 hour for every 25.97 hours worked (e.g. about 6.7 hours/month if working 40 hours/week; about 3.4 hours/month if working 20 hours/week), and paid sick time accrued at 1 hour for every 30 hours worked (e.g. about 5.8 hours/month if working 40 hours/week; about 2.9 hours/month if working 20 hours/week). The national base pay ranges for this position within the United States, including Illinois and Colorado. I4 $137,100 — $201,600 USD I5 $167,800 — $246,800 USD I6 $203,500 — $299,300 USD About DoorDash At DoorDash, our mission to empower local economies shapes how our team members