Senior Software Engineer, Machine Learning Infrastructure - Generative AI

DoorDash; Seattle (WA); In loco; USD 137.100 - 201.600; Tempo pieno

Vantaggi offerti da questo lavoro

401(k) plan with employer matching

16 weeks of paid parental leave

Comprehensive wellness benefits

Descrizione del lavoro

DoorDash is seeking an experienced software engineer to join their Generative AI team. This role involves designing and leading the development of production infrastructure for AI models. You will set technical direction across various systems, ensuring high performance and scalability.

The ideal candidate will have a strong background in backend engineering and experience in AI applications. The position offers a competitive salary and comprehensive benefits including 401(k) matching and paid parental leave.

Competenze

• 6+ years of industry experience in software engineering.
• Experience with LLM inference and production systems.
• Deep hands-on experience with fine-tuning of open-weight models.

Mansioni

• Lead the design of production infrastructure for Generative AI.
• Set technical direction across model serving and inference engines.
• Build scalable and high-performance systems for model serving.

Conoscenze

Backend engineering fundamentals

Python

Distributed systems

Technical leadership

AI coding tools

Formazione

B.S., M.S., or Ph.D. in Computer Science or equivalent

Strumenti

Kubernetes

AWS/GCP

Descrizione del lavoro

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. We run frontier open‑weight LLMs and VLMs (GLM, Qwen, Kimi, DeepSeek) on real‑time GPU serving, high‑throughput batch inference, and autoscaling fine‑tuning, delivering large cost and latency wins. We also own core platform surfaces including the LLM Gateway, Agent Gateway, evals infrastructure, guardrails, and cost attribution.

About The Role

You will join a small, high‑leverage team building production infrastructure for Generative AI at DoorDash, leading the design and architecture of our open‑weight model platform spanning inference and fine‑tuning. You’ll set technical direction across model serving and inference engines, fine‑tuning and training pipelines, GPU autoscaling and utilization, batch pipelines, backend services, and observability, and mentor engineers as you go.

You’re Excited About This Opportunity Because You Will…

• Lead the design of infrastructure that helps DoorDash teams move GenAI ideas from prototype to production, increasing the velocity of business impact from AI across the company.
• Own and evolve our open‑weights serving stack — real‑time GPU endpoints, high‑throughput batch inference, and fine‑tuning (SFT/DPO/LoRA) — alongside the LLM Gateway, Agent Gateway, evals infrastructure, guardrails, and cost attribution.
• Architect scalable, high‑performance systems for model serving, batch inference, GPU autoscaling, and fine‑tuning that power real customer and internal automation use cases.
• Push the cost and latency frontier of GPU inference, turning batch jobs that took days into hours and cutting inference cost by multiples, while giving product teams a clean choice across open‑weight and closed‑source models with reliability, fallback, 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 – and raise the technical bar for – ML engineers, product engineers, data scientists, and platform teams across DoorDash, Wolt, and Deliveroo to turn emerging GenAI capabilities into durable platform primitives.
• Set technical direction for the future of DoorDash’s centralized GenAI platform, including emerging directions such as reinforcement learning (RLHF/RLVR), agent optimization, and other post‑training and agentic techniques, enabling the next generation of AI‑powered products, agents, automation, and personalization.

We’re Excited About You Because…

• B.S., M.S., or Ph.D. in Computer Science or equivalent.
• 6+ years of industry experience in software engineering.
• Deep backend engineering fundamentals, especially in Python and distributed systems.
• Track record of designing and owning 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.
• Deep hands‑on experience with LLM inference and/or fine‑tuning of open‑weight models in production – serving (latency, throughput, batching, autoscaling, GPU utilization) and/or fine‑tuning (SFT/DPO/LoRA).
• Demonstrated technical leadership: leading design across ambiguous, fast‑moving technical areas, mentoring engineers, and turning customer use cases into reusable platform capabilities.
• 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

• Experience with LLM inference engines and serving frameworks (vLLM, SGLang, TensorRT‑LLM) in production.
• Experience with distributed/multi‑node fine‑tuning and training pipelines (SFT, DPO/RLHF, LoRA), including data preparation and evaluation.
• GPU performance work – multi‑node/distributed inference, KV‑cache/memory optimization, quantization (FP8/INT8/AWQ/GPTQ), or cold‑start/throughput tuning.
• Experience with Kubernetes, cloud infrastructure (AWS/GCP), GPUs, serverless/elastic GPU platforms (Modal), or high‑throughput batch systems.
• Experience with LLM gateways, model routing, vendor abstraction, or cost attribution.
• Experience building developer platforms, internal platforms, or self‑serve infrastructure.
• Experience building and deploying AI agents or MCP servers in production.
• Experience with eval systems, LLM observability, tracing, RAG, search, or vector databases.

Compensation

The successful candidate’s starting pay will fall within the pay range listed below, determined by 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.

• I4: $137,100—$201,600 USD
• I5: $167,800—$246,800 USD
• I6: $203,500—$299,300 USD

In addition to base salary, compensation includes opportunities for equity grants.

Benefits

DoorDash offers a comprehensive benefits package that includes a 401(k) plan with employer matching, 16 weeks of paid parental leave, wellness benefits, commuter benefits match, paid time off and sick leave in compliance with applicable laws (e.g., Colorado Healthy Families and Workplaces Act), medical, dental, and vision benefits, 11 paid holidays, disability and basic life insurance, family‑forming assistance, and a mental health program.

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 (≈6.7 hours/month if working 40 hours/week; ≈3.4 hours/month if working 20 hours/week), and paid sick time accrued at 1 hour for every 30 hours worked (≈5.8 hours/month if working 40 hours/week; ≈2.9 hours/month if working 20 hours/week).

EEO Statement

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.