3 months ago
Base Salary
$137k - $202k/yr
Responsibilities
- Lead the design of infrastructure that moves generative AI ideas from prototype to production.
- Own and evolve real-time GPU endpoints, high-throughput batch inference, fine-tuning pipelines, the LLM Gateway, Agent Gateway, evaluation infrastructure, guardrails, and cost attribution.
- Architect scalable systems for model serving, batch inference, GPU autoscaling, fine-tuning, backend services, and observability.
- Improve GPU inference cost, latency, throughput, batching, autoscaling, and utilization while supporting open-weight and closed-source model choices.
- Build production-grade platforms with monitoring, SLOs, operational playbooks, reliability, fallback, and cost controls.
- Partner with ML engineers, product engineers, data scientists, and platform teams across DoorDash, Wolt, and Deliveroo.
- Set technical direction for future generative AI capabilities, including reinforcement learning, agent optimization, post-training, and agentic techniques.
- Mentor engineers and raise the technical bar across the team.
Requirements
- B.S., M.S., or PhD in Computer Science or equivalent experience.
- At least 6 years of industry software engineering experience.
- Deep backend engineering fundamentals, especially Python and distributed systems.
- Experience designing and owning production services, APIs, data pipelines, or ML infrastructure at scale.
- Experience operating production systems, including observability, debugging, reliability, incident response, and performance and cost optimization.
- Hands-on production experience with LLM inference and/or fine-tuning open-weight models, including serving, batching, autoscaling, GPU utilization, SFT, DPO, or LoRA.
- Demonstrated technical leadership across ambiguous technical areas, including design leadership, mentoring, and creating reusable platform capabilities.
- Proficiency using AI coding tools such as Claude Code, Codex, or Cursor across the software development lifecycle.
- Experience with inference engines and serving frameworks such as vLLM, SGLang, or TensorRT-LLM is preferred.
- Experience with distributed or multi-node fine-tuning and training pipelines, GPU performance optimization, Kubernetes, AWS, GCP, Modal, LLM gateways, developer platforms, AI agents, MCP servers, evaluation systems, observability, tracing, RAG, search, or vector databases is preferred.
Benefits
- 401(k) plan with employer matching
- 16 weeks of paid parental leave
- Wellness benefits and expense reimbursement
- Commuter benefits match
- Flexible paid time off/vacation for salaried roles
- Paid sick leave
- Medical, dental, and vision benefits
- 11 paid holidays
- Disability and basic life insurance
- Family-forming assistance and mental health program
- Equity grant opportunities
Tech Stack
Categories
About DoorDash
DoorDash builds a marketplace and logistics platform that connects consumers with local restaurants and retailers for on-demand delivery and pickup, powered by a network of independent Dashers. It earns through delivery fees, merchant commissions, advertising, DashPass subscriptions, and white-label fulfillment via DoorDash Drive. Founded in 2013 and headquartered in San Francisco, the public company operates across the U.S. and selected international markets, and also researches automation through DoorDash Labs.