Software Engineer, Planning & Controls - Autonomy & Robotics, DoorDash Air

About the Team API Multicloud partners with cloud service providers (CSPs) to bring OpenAI’s frontier models and APIs into the cloud environments where developers and enterprises already build. We own the API end to end across CSP platforms, from API integration and request routing through inference execution, capacity management, and day-to-day operations. Our mission is to make these capabilities available broadly and safely while preserving the reliability and developer experience customers expect from OpenAI. Delivering that experience requires work across the stack: adapting API services and cloud-native integrations, bringing up model-serving environments, validating new releases, and operating the systems that connect customer requests to available capacity. We work directly with CSP engineering teams and partner closely with OpenAI’s inference, API, infrastructure, security, and Safety Systems teams. Together, we turn new model capabilities and partner requirements into dependable production services, with attention to performance, operational readiness, and efficient use of compute. Our work contributes to OpenAI’s AWS partnership , including the launch of Daybreak Red and Daybreak Blue on Amazon Bedrock . We’re building on that foundation to bring OpenAI’s API to additional CSPs. These efforts let customers use OpenAI capabilities within the cloud infrastructure, security controls, and workflows they already rely on. About the Role We’re looking for experienced infrastructure engineers to design, build, and operate the systems that run OpenAI’s API across CSP platforms. You’ll help establish the cloud deployment platform: configuring and launching model-serving environments, planning capacity, improving caching and utilization, and making new releases safe and repeatable. Working closely with inference engineers, you’ll connect advances in model serving to the infrastructure and operational practices needed to run them reliably in production. The role call

About the Role The Cloud Infrastructure team owns the foundational cloud primitives and deployment models that power Perplexity's products, from multi-tenant public cloud to single-tenant and on-premises solutions for enterprise customers. As Perplexity grows its Computer and Enterprise products, this team builds and operates the security, isolation, and compliance layers that customers depend on. We provide the deployment topologies, multi‑region infrastructure, and core services that enable Perplexity to run reliably and efficiently for both consumer traffic and large enterprises. Key Responsibilities Own the roadmap and technical strategy for agent-driven cloud infrastructure management. Design and operate Perplexity’s cloud networking fabric, including VPC architectures, private connectivity, and peering with hyperscalers and neocloud providers to support low-latency, high-throughput AI workloads. Architect and scale compute platforms (Kubernetes/EKS, autoscaling groups, and mixed CPU/GPU fleets) to efficiently serve online request traffic and background workloads across regions. Build and maintain secure, isolated deployment topologies for multi-tenant, single-tenant, and customer‑owned cloud (BYOC) environments, including cross-account networking, identity, and policy guardrails. Implement and evolve multi-region strategies for availability, failover, and data locality, including traffic routing, regional capacity planning, and disaster recovery playbooks. Partner with security to deliver enterprise controls such as BYOK/KMS integrations, network isolation, and auditability required for regulated customers. Develop automation, tooling, and runbooks that make day‑2 operations (provisioning, upgrades, incident response) predictable and repeatable for Perplexity products across all environments. Qualifications Deep experience designing and operating cloud infrastructure on AWS (VPC design, routing, security groups, load balancing, private connectivity). Strong ba

About the Team: Compute Infrastructure builds the platform that turns enormous amounts of compute into a reliable engine for frontier AI. We design, provision, schedule, operate, and optimize the systems that connect accelerators, CPUs, networks, storage, data centers, orchestration software, agent infrastructure, developer tools, and observability into one coherent experience for researchers and product teams. Our work spans the entire stack: capacity planning and cluster lifecycle, bare-metal automation, distributed systems, Kubernetes and scheduling, deep system optimization, high-performance networking, storage, fleet health, reliability, workload profiling, benchmarking, and the developer experience that lets teams use enormous compute systems with confidence. At this scale, small improvements to communication, scheduling, hardware efficiency, or debugging workflows can compound into meaningful research velocity. We are hiring across Compute Infrastructure rather than for a single narrow team, and we use this opening to match strong engineers to the problems where they can have the most leverage. About the Role We are looking for engineers who want to build the compute platform behind OpenAI's research and products. You may not be the strongest in low-level systems, high-performance computing, distributed infrastructure, reliability, CaaS, agent infrastructure, developer platforms, tooling, or the user experience around infrastructure. What matters is that you can reason carefully about complex systems, write durable software, and raise the quality and velocity of the people around you. Depending on your background and interests, you might work close to hardware, close to users, on CaaS and agent infrastructure, or on the control planes and data planes in between. You could help bring new supercomputing capacity online, optimize training workloads from profiler traces and benchmarks, improve NCCL and collective communication behavior, reason about GPUs, NICs, t

About the Team OpenAI's Industrial Compute organization is responsible for planning, delivering, operating, and optimizing the compute infrastructure that powers frontier AI. As OpenAI scales toward becoming an intelligence utility, Industrial Compute coordinates a complex lifecycle spanning infrastructure strategy, capacity planning, provider partnerships, fleet operations, product demand, and financial planning. The organization manages one of the largest and fastest-growing compute footprints in the world, where decisions around capacity allocation, deployment readiness, utilization, reliability, and product demand directly impact product availability, customer experience, and business performance. The Capacity Systems team builds the software platforms, data systems, and automation frameworks that connect these functions into a shared operating model. We transform fragmented planning workflows into scalable systems that enable teams to understand what compute was contracted, delivered, healthy, allocated, and ultimately converted into business and research outcomes. About the Role We are seeking a Capacity Systems Software Engineer to build the platforms and services that power Industrial Compute planning, forecasting, optimization, and operational decision-making. In this role, you will design and develop software systems that connect infrastructure delivery, fleet health, capacity allocation, demand forecasting, deployment readiness, financial planning, and product consumption into a unified system of record. Your work will help OpenAI make better decisions about where compute should be deployed, how capacity should be allocated, and how infrastructure investments translate into business value. You will partner closely with Capacity Planning, Fleet Operations, Infrastructure Engineering, Product, Finance, Supply Chain, and Strategic Sourcing teams to replace spreadsheet-driven workflows with scalable software systems that enable visibility, automation, and dec

About the Team The Search Product Infrastructure team builds the systems that power search experiences across ChatGPT. We partner with teams developing models, operating inference infrastructure, building specialized search experiences, and maintaining search indexes to bring advances in models and retrieval into production. Our work spans search orchestration, model serving, experimentation, and distributed systems, with direct impact on answer quality, responsiveness, reliability, and efficiency at ChatGPT scale. About the Role As a senior engineer on the Search Product Infrastructure team, you will design, build, and operate the systems that connect models with search at ChatGPT scale. You will tackle challenges in search orchestration, inference efficiency, experimentation, and production reliability, making tradeoffs that directly shape answer quality and user experience. Working closely with researchers and partner engineering teams, you will own projects from technical design through launch and iteration, evolving the architecture as models, product capabilities, and demand grow. This role is based in San Francisco, CA. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees. In this role, you will: Design and evolve the services that coordinate search classification, retrieval, ranking, and model inference, working closely with researchers and partner engineering teams to bring new capabilities into production. Improve end-to-end latency, throughput, and infrastructure efficiency through profiling, caching, request routing, and capacity planning, making informed tradeoffs between search quality, reliability, and compute cost. Build experimentation tooling and automation to run reproducible A/B tests, define success metrics, and measure product impact. Use shadow traffic and load testing to validate system behavior, estimate capacity needs, and support safe production rollouts. Own production reliability th

About the Team The Codex Core Agent team builds the kernel of Codex. We own making the agent better, accelerating research, and making those improvements real in production for our users. That means working across the systems that make Codex actually function as an agent in the real world: the production performance envelope around tokens, latency, reliability, cost, and capacity; the core execution loop and interfaces that turn models into useful behavior; the shared infrastructure that enables other teams to build on Codex; and the feedback loops that turn real-world usage into better models and better agent behavior over time. About the Role We’re looking for engineers to build the infrastructure that powers Codex agents in production. This role focuses on the systems that let models safely execute code, interact with tools, complete long-running tasks, and operate reliably and efficiently at scale. You’ll design and operate the infrastructure behind sandboxed execution, orchestration, stateful workflows, app-server and SDK boundaries, and model rollouts. You’ll work at the intersection of distributed systems, developer tooling, and AI, building primitives that make Codex faster, safer, more reliable, and easier for the rest of the organization to build on. What You’ll Do Design and build execution environments for AI agents, including sandboxing, isolation, and reproducibility. Develop systems for agent orchestration across multi-step, tool-using workflows. Build infrastructure for running, testing, and debugging code generated by models. Create state and memory systems that allow agents to persist context across long-running tasks. Optimize tokens, latency, reliability, and cost across Codex’s production fleet. Support model rollouts, capacity planning, and the core tradeoffs between quality, speed, and economics to manage a fleet of frontier agents at scale. Build shared platform capabilities that unblock product teams, partner teams, and open source Codex. Yo