14 hours ago
Seattle, WA, USA +2 moreMid Level
H1B sponsor
Base Salary
$131k - $192k/yr
Responsibilities
- Build infrastructure for real-time GPU endpoints, high-throughput batch inference, and fine-tuning of open-weight models.
- Design scalable systems for model serving, batch inference, GPU autoscaling, utilization, and distributed fine-tuning.
- Improve GPU inference cost, latency, throughput, batching, reliability, fallback behavior, observability, and cost controls.
- Develop and maintain GenAI platform surfaces including the LLM Gateway, Agent Gateway, evaluation infrastructure, guardrails, and cost attribution.
- Operate production systems with monitoring, SLOs, debugging, incident response, playbooks, and performance optimization.
- Partner with ML engineers, product engineers, data scientists, and platform teams to turn GenAI use cases into reusable platform capabilities.
- Contribute to emerging capabilities such as reinforcement learning, agent optimization, post-training, and agentic techniques.
Requirements
- Bachelor’s, master’s, or PhD in Computer Science or equivalent.
- At least 3 years of industry software engineering experience.
- Strong backend engineering fundamentals, especially in Python and distributed systems.
- Experience building production services, APIs, data pipelines, or ML infrastructure at scale.
- Experience operating production systems, including observability, debugging, reliability, incident response, and performance or cost optimization.
- Production experience with LLM inference or fine-tuning of open-weight models, including serving latency, throughput, batching, autoscaling, GPU utilization, or techniques such as SFT, DPO, and LoRA.
- Ability to work across ambiguous, fast-moving technical areas and convert customer use cases into reusable platform capabilities.
- Proficiency using AI coding tools such as Claude Code, Codex, or Cursor throughout the software development lifecycle.
- Preferred experience with LLM inference engines, serving frameworks, distributed or multi-node fine-tuning, GPU performance optimization, Kubernetes, AWS, GCP, Modal, high-throughput batch systems, LLM gateways, model routing, developer platforms, AI agents, MCP servers, evaluation systems, observability, tracing, RAG, search, or vector databases.
Benefits
- 401(k) plan with employer matching.
- Medical, dental, and vision benefits, disability insurance, basic life insurance, and mental health support.
- 16 weeks of paid parental leave, family-forming assistance, wellness benefits, commuter benefits match, paid time off, paid sick leave, and 11 paid holidays.
- Salaried roles receive flexible paid time off or vacation plus 80 hours of paid sick time per year; hourly accrual policies are also provided.
- The role is a salaried position with a localized U.S. base-pay range of $130,600–$192,000.
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.