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DoorDash USA

Senior Software Engineer, Machine Learning Infrastructure - Generative AI

DoorDash USA · San Francisco, CA; Sunnyvale, CA; Seattle, WA

Posted about 3 months ago

or apply directly on DoorDash USA's site. We never take the application ourselves.

Is this posting real?

This role has been open
81 days
DoorDash USA's roles stay open a median of 81 days
Reposted
No
Salary listed
No
0% of DoorDash USA's roles list one
Ghost-job risk at DoorDash USA
high
456 stale, 8 reposted of 462 open
Hiring momentum
682 roles opened in the last 90 days
↑ up vs. the prior 90 days
Last confirmed on the employer's board
2026-10-23

Measured from postings appearing on and disappearing from DoorDash USA's own greenhouse board since 2026-08-03. Full hiring picture for DoorDash USA.

About this role

In this role, you will be part of a small team focused on building production infrastructure for Generative AI at DoorDash. You will lead the design and architecture of the open-weights model platform, focusing on real-time GPU serving, high-throughput batch inference, and model fine-tuning. Your responsibilities will include setting technical direction, mentoring engineers, and collaborating with various teams to enhance the capabilities of the GenAI platform.

Our read on this posting3.2out of 5
benefits
5/5
freshness
1/5
career value
5/5
role clarity
5/5
pay transparency
0/5

Scored from the posting itself — how clearly the role is described, how much it says about pay and benefits, and how recently it was listed. Not a judgement of DoorDash USA as an employer.

What you need

  • 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

Nice to have

  • 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

What you get

  • 401(k) plan with employer matching
  • 16 weeks of paid parental leave
  • wellness benefits
  • commuter benefits match
  • paid time off and paid sick leave
  • medical, dental, and vision benefits

Worth weighing

  • No specific mention of remote work options, though multiple locations are listed
  • The role involves leading in a fast-moving technical area, which may require adaptability to rapid changes
  • The compensation range is broad and depends on various factors, including location and experience
  • The use of automated recruitment tools may affect the hiring process

Summarised from DoorDash USA's posting. Read the full original.

Listed by DoorDash USA on their greenhouse job board, last confirmed open on 2026-10-23. PitchMeAI is not the employer.

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