Software Engineer, Machine Learning Platform - Gen AI

AI Chopping Block, Inc.; Sunnyvale, Northern (CA, KY); Hybrid; USD 150,000 - 210,000; Full time

Benefits offered by this job

401(k) plan with employer matching

Paid parental leave

Wellness benefits

Commuter benefits match

Medical, dental, vision benefits

Paid time off and paid sick leave

Job summary

DoorDash’s GenAI Platform team builds the shared infrastructure that enables GenAI-powered products across DoorDash, Wolt, and Deliveroo. You will join a small team focusing on real-time GPU serving, high-throughput inference, and model fine-tuning to push cost and latency improvements.

You’ll work across model serving, training pipelines, and observability, partnering with ML engineers and platform teams to translate customer needs into durable platform primitives and robust production systems.

Qualifications

• 3+ years of industry software engineering experience.
• Strong backend and Python/distributed systems fundamentals.
• Experience building production services, APIs, data pipelines, or ML infrastructure at scale.
• Experience operating systems in production, including observability, debugging, reliability, incident response, and cost optimization.
• Hands-on experience with LLM inference and/or fine-tuning of open-weight models in production — serving and/or fine-tuning.
• Ability to work across ambiguous, fast-moving technical areas and turn customer use cases into reusable platform capabilities.
• Proficiency in using AI coding tools in the full software development lifecycle.

Responsibilities

• Build infrastructure enabling GenAI ideas to move from prototype to production.
• Work on open-weights serving stack and related gateways and evals infrastructure.
• Design scalable systems for model serving, batch inference, and GPU autoscaling.
• Push cost/perf frontier of GPU inference with reliability and observability built in.
• Collaborate with ML engineers, product engineers, data scientists, and platform teams to turn GenAI capabilities into platform primitives.
• Shape the future of DoorDash’s centralized GenAI platform including RLHF/RLVR and agent techniques.

Skills

Python

Distributed systems

Backend engineering

Production services

Observability

GPU inference

SFT/LoRA

Kubernetes

Cost optimization

Education

Bachelor's/Master's/PhD in CS

Tools

vLLM

TensorRT-LLM

Kubernetes

AWS

GCP

Modal

Job description

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. A central pillar of that work is running frontier open-weight LLMs and VLMs (such as GLM, Qwen, Kimi, and DeepSeek) ourselves — real-time GPU serving, high-throughput batch inference, and fine-tuning on autoscaling GPUs — delivering large cost and latency wins (for example, a billion embeddings produced roughly 20× cheaper and visual models served roughly 72% cheaper). 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, with a primary focus on our open-weights model platform spanning inference and fine-tuning: real-time GPU serving, high-throughput batch inference, and model fine-tuning. You’ll work across model serving and inference engines, fine-tuning and training pipelines, GPU autoscaling and utilization, batch pipelines, backend services, and observability. This role is ideal for an engineer who enjoys 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.

You’re excited about this opportunity because you will…

• Build the infrastructure that helps DoorDash teams move GenAI ideas from prototype to production, increasing the velocity of business impact from AI across the company.
• Work on 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.
• Design 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 ML engineers, product engineers, data scientists, and platform teams across DoorDash, Wolt, and Deliveroo to turn emerging GenAI capabilities into durable platform primitives.
• Shape 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 PhD. in Computer Science or equivalent
• 3+ years of industry experience in software engineering
• Strong backend engineering fundamentals, especially in Python and distributed systems.
• Experience building 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.
• 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).
• Ability to work across ambiguous, fast‑moving technical areas and turn 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

The successful candidate’s starting pay will fall within the pay range listed below and is determined based on 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.

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

DoorDash cares about you and your overall well-being. That’s why we offer a comprehensive benefits package to all regular employees, which includes a 401(k) plan with employer matching, 16 weeks of paid parental leave, wellness benefits, commuter benefits match, paid time off and paid sick leave in compliance with applicable laws (e.g. Colorado Healthy Families and Workplaces Act). DoorDash also offers medical, dental, and vision benefits, 11 paid holidays, disability and basic life insurance, family‑forming assistance, and a mental health program, among others.

To learn more about our benefits, visit our careers page here.

See below for 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 (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).

About DoorDash

At DoorDash, our mission to empower local economies shapes how our team members move quickly, learn, and reiterate in order to make impactful decisions that display empathy for our range of users—from Dashers to merchant partners to consumers. We are a technology and logistics company that started by enabling door-to-door delivery, and we are looking for team members who can help us go from a company that is known as the place you order food to a company that people turn to for any and all goods.
DoorDash is growing rapidly and changing constantly, which gives our team members the opportunity to share their unique perspectives, solve new challenges, and own their careers. We're committed to supporting employees’ happiness, healthiness, and overall well-being by providing comprehensive benefits and perks including premium healthcare, wellness expense reimbursement, paid parental leave and more.

Our Commitment to Diversity and Inclusion

We’re committed to growing and empowering a more inclusive community within our company, industry, and cities. That’s why we hire and cultivate diverse teams of people from all backgrounds, experiences, and perspectives. We believe that true innovation happens when everyone has room at the table and the tools, resources, and opportunity to excel.

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. Thank you to the Level Playing Field Institute for this statement of non‑discrimination.

Pursuant to the San Francisco Fair Chance Ordinance, Los Angeles Fair Chance Initiative for Hiring Ordinance, and any other state or local hiring regulations, we will consider for employment any qualified applicant, including those with arrest and conviction records, in a manner consistent with the applicable regulation.

If you need any accommodations, please inform your recruiting contact upon initial connection.

Notice to Applicants for Jobs Located in NYC or Remote Jobs Associated With Office in NYC Only