Senior Software Engineer, Machine Learning Infrastructure - Generative AI

DoorDash US
San Francisco, Sunnyvale, Seattle
Workplace: OnsiteFull timeUSD 137,100 - 299,300 annuallyFunction: Software EngineeringExperience: 6+ yearsSkills: ["Technical leadership","Mentoring","Production reliability"]

Build and lead the design of DoorDash’s open-weights GenAI platform infrastructure, spanning real-time GPU serving, high-throughput batch inference, and fine-tuning. Own the serving and training pipelines, GPU autoscaling/utilization, backend services, and observability while pushing cost/latency improvements for production LLM workloads. Partner with ML, product, data science, and platform teams across DoorDash, Wolt, and Deliveroo to turn emerging GenAI capabilities into reusable platform primitives.

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FursaFursa
DoorDash US
DoorDash US
2 months ago

Senior Software Engineer, Machine Learning Infrastructure - Generative AI

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI
Last checked: 3 hours agoStatus: Live

Job Summary

Build and lead the design of DoorDash’s open-weights GenAI platform infrastructure, spanning real-time GPU serving, high-throughput batch inference, and fine-tuning. Own the serving and training pipelines, GPU autoscaling/utilization, backend services, and observability while pushing cost/latency improvements for production LLM workloads. Partner with ML, product, data science, and platform teams across DoorDash, Wolt, and Deliveroo to turn emerging GenAI capabilities into reusable platform primitives.
Location: San Francisco, Sunnyvale, Seattle
Workplace: Onsite
Employment Type: Full time
Job Function: Software Engineering

Key Responsibilities

  • •Lead design and architecture of an open-weights model platform spanning inference and fine-tuning, including real-time GPU serving, high-throughput batch inference, and training/fine-tuning pipelines.
  • •Set technical direction across serving/inference engines, GPU autoscaling/utilization, backend services, and observability for production systems.
  • •Architect scalable, high-performance systems that power real customer and internal automation use cases.
  • •Drive cost and latency improvements for GPU inference, including batching/throughput and reliability, fallback, and cost controls.
  • •Partner with and raise the technical bar for ML, product, data science, and platform teams, mentoring engineers as needed and guiding future platform directions (e.g., RLHF/RLVR, agent optimization).

Pay and Benefits

Salary: USD 137,100 - 299,300 annually
Equity and Bonus:Equity
Perks:401kPaid ParentalHealth InsuranceDentalVisionPaid LeaveWellness StipendCommuter BenefitsPaid HolidaysDisability InsuranceLife Insurance

Key Requirements

  • •B.S., M.S., or PhD. in Computer Science (or equivalent).
  • •6+ years of industry experience in software engineering.
  • •Strong backend engineering fundamentals, especially in Python and distributed systems.
  • •Proven track record designing and owning production services, APIs, data pipelines, or ML infrastructure at scale.
  • •Hands-on experience operating production systems with observability, reliability, incident response, and performance/cost optimization, plus LLM inference and/or fine-tuning with open-weight models (SFT/DPO/LoRA).
Experience:6+ years
Education:
Skills:Technical leadershipMentoringProduction reliability
Languages:English
Tech Stack:PythonDistributed systemsGenAILLMVLMGPU servingBatch inferenceFine-tuningSFTDPOLoRARLHFRLVRLLM GatewayAgent GatewayE2vals infrastructureGuardrailsCost attributionObservabilityDebugging

Company Brief

DoorDash US
On-demand logistics platform connecting consumers with local restaurants, grocers, and retailers for food delivery, pickup, and convenience services. Operates a marketplace and last-mile delivery network while offering tools and analytics for merchants and couriers.
Industry: Online Marketplaces
Company Size: Enterprise (1,001+ employees)
Revenue: USD 1B+
Growth: Public Company
Valuation: Public Company (Market Cap in USD)
Funding: IPO / Publicly Listed
Headquarters: San Francisco, United States
Founded: 2013
WebsiteLinkedIn