Senior Software Engineer, Machine Learning Infrastructure - Generative AI

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-weights model platform spanning inference and fine-tuning: real-time GPU serving, high-throughput batch inference, and model 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.

This role is ideal for a senior engineer who enjoys owning ambiguous, high-impact systems and pushing the cost/performance frontier of GPU inference and fine-tuning in a fast-moving technical area where product needs, model capabilities, vendor ecosystems, and cost/performance tradeoffs are evolving quickly.

Responsibilities

• 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 Deliverado 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.

Requirements

• B.S., M.S., or PhD 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 (e.g., 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 (e.g., 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.

Pay

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

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

Benefits

• 401(k) plan with employer matching.
• 16 weeks of paid parental leave.
• Wellness benefits and commuter benefits match.
• Paid time off and paid 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.

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).