In this role, you will:

• Lead sourcing strategies to identify and engage candidates across a variety of engineering disciplines, including software engineering, backend systems, product engineering, and related technical areas.

• Develop and maintain a strong pipeline of passive candidates through proactive outreach, research, and networking.

• Collaborate closely with hiring managers and technical leaders to deeply understand hiring needs and refine sourcing approaches accordingly.

• Leverage advanced sourcing techniques and tools to identify and attract exceptional talent.

• Represent OpenAI at industry events and conferences to promote our mission and connect with potential candidates.

• Maintain accurate and organized candidate data and metrics to inform sourcing strategies and decision-making.

You might thrive in this role if you have:

• 5+ years of experience in technical sourcing, with a focus on engineering or technical roles.

• A proven track record of successfully sourcing candidates across a range of software engineering and technical disciplines.

• A strong understanding of technical concepts and the ability to effectively communicate role requirements to candidates.

• Excellent communication and interpersonal skills, with the ability to build trust with both candidates and internal stakeholders.

• Proficiency with sourcing tools and platforms, including LinkedIn Recruiter and applicant tracking systems.

• The ability to manage multiple priorities and adapt quickly in a fast-paced, evolving environment.

XML job scraping automation by YubHub

In this role, you will work across performance benchmarking, tokenomics, model porting, infrastructure integration, systems tooling, and operational monitoring. You will help connect partner and first-party compute environments into OpenAI's infrastructure stack, ensuring GPU capacity can be onboarded, measured, monitored, and optimized against real workload outcomes.

Key Responsibilities

• Develop systems and tooling to measure, monitor, and improve token throughput across first-party and partner-owned compute environments.
• Support performance benchmarking, tokenomics analysis, and model porting across heterogeneous infrastructure environments.
• Build tooling to integrate external or partner infrastructure into OpenAI's internal compute, observability, and workload management systems.
• Develop and monitor operational metrics including billing, usage, SLAs, utilization, reliability, and throughput.
• Identify bottlenecks across hardware, networking, software, and workload enablement that prevent capacity from becoming productive tokens.
• Partner with compute, infrastructure, networking, finance, and operations teams to translate raw capacity into usable workload-serving capacity.
• Build dashboards, automation, and reporting systems that provide clear visibility into TaaS capacity, performance, and business outcomes.

Qualifications

• Strong software engineering background with experience building systems, tooling, automation, or infrastructure platforms.
• Experience working across compute infrastructure, distributed systems, performance engineering, or production operations.
• Ability to reason about token throughput, utilization, benchmarking, infrastructure efficiency, and workload performance.
• Comfortable integrating external systems or partner environments into internal infrastructure stacks.
• Strong analytical and debugging skills across hardware, networking, software, and operational domains.

Preferred Skills

• Experience with GPU clusters, AI infrastructure, performance benchmarking, or workload optimization.
• Familiarity with model porting, inference/training workloads, token economics, or compute efficiency analysis.
• Experience building monitoring systems for billing, usage, SLAs, utilization, or infrastructure reliability.
• Background in systems engineering, infrastructure software, observability, distributed systems, or platform engineering.

XML job scraping automation by YubHub

In this role, you will own execution across complex compute programs where raw infrastructure capacity must be transformed into operational GPU throughput. You will coordinate across data center delivery, power, networking, hardware deployment, workload enablement, finance, and external partners to ensure capacity becomes productive tokens as quickly and efficiently as possible.

This role is ideal for someone who can bridge physical infrastructure delivery with compute utilization outcomes. Success requires strong systems thinking, elite program leadership, and the ability to drive accountability across internal teams and strategic partners.

Responsibilities:

• Lead Tokens-as-a-Service programs across industrial compute environments, including first-party and partner-owned capacity.
• Convert delivered power, space, and hardware capacity into production-ready token throughput.
• Build integrated execution plans spanning construction, power energization, rack deployment, networking, cluster readiness, and workload onboarding.
• Partner with infrastructure engineering, hardware, networking, finance, supply chain, and operations teams.
• Drive external providers, EPCs, OEMs, utilities, and strategic partners toward aggressive timelines.
• Identify bottlenecks between physical delivery and usable compute output, then lead resolution.
• Build operating cadences, dashboards, forecasts, and executive reporting for TaaS programs.
• Develop repeatable deployment models for future industrial compute expansion.

You might thrive in this role if you:

• Have led hyperscale infrastructure, data center, manufacturing, or industrial deployment programs.
• Understand how power + hardware + networking + software combine into usable compute.
• Can operate equally well with engineers, operators, executives, and external partners.
• Are highly structured in ambiguous, fast-moving environments.
• Know how to escalate intelligently and maintain momentum under pressure.
• Can translate technical execution into business outcomes.

Preferred Skills:

• Experience with GPU clusters, AI infrastructure, hyperscale data centers, or industrial technology programs.
• Background in program management, operations leadership, capacity delivery, or strategic infrastructure execution.
• Familiarity with utilities, construction, commissioning, networking, hardware deployment, or cluster bring-up.
• Experience managing multi-billion-dollar infrastructure or capacity programs.
• Strong executive communication and stakeholder management skills.

XML job scraping automation by YubHub

In this role, you will partner closely with Senior Performance Modeling Engineers and the Performance Modeling Lead to analyze system behavior, run simulations and analytical models, and help evaluate tradeoffs across compute, memory, networking, and storage.

This role is ideal for early-career engineers with 1–2 years of experience in software engineering, systems analysis, or performance modeling who are excited to grow in large-scale infrastructure and hardware/software systems.

Key Responsibilities:

• Support the development and maintenance of performance modeling tools and frameworks
• Assist in building models to evaluate system behavior across compute, memory, networking, and interconnect subsystems
• Help analyze distributed system scaling behavior and identify performance bottlenecks
• Run simulations and analytical models to support architecture and infrastructure decisions
• Partner with senior engineers to evaluate design tradeoffs across hardware and system components
• Interpret modeling outputs and help translate findings into clear recommendations
• Validate models using benchmarking data and real system performance measurements
• Improve modeling workflows, documentation, and usability for broader team adoption
• Collaborate cross-functionally with hardware, infrastructure, and architecture teams

Qualifications:

• 1–2 years of experience in software engineering, systems modeling, performance analysis, or related technical work
• Strong programming skills and experience building technical tools, scripts, or frameworks
• Familiarity with system architecture fundamentals such as compute, memory, and networking
• Ability to reason about system performance, bottlenecks, and scaling behavior
• Strong analytical and problem-solving skills with comfort working in quantitative environments
• Ability to learn quickly and work effectively across technical teams

Preferred Skills:

• Exposure to AI/ML workloads, distributed systems, or large-scale infrastructure
• Experience with simulation tools, benchmarking, profiling, or performance analysis
• Familiarity with data center systems, server architecture, or hardware platforms
• Interest in system architecture and hardware/software co-design
• Internship or early professional experience in performance engineering, infrastructure, or systems design

XML job scraping automation by YubHub

The base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. If the role is non-exempt, overtime pay will be provided consistent with applicable laws. In addition to the salary range listed above, total compensation also includes generous equity, performance-related bonus(es) for eligible employees, and the following benefits.

• Medical, dental, and vision insurance for you and your family, with employer contributions to Health Savings Accounts

• Pre-tax accounts for Health FSA, Dependent Care FSA, and commuter expenses (parking and transit)

• 401(k) retirement plan with employer match

• Paid parental leave (up to 24 weeks for birth parents and 20 weeks for non-birthing parents), plus paid medical and caregiver leave (up to 8 weeks)

• Paid time off: flexible PTO for exempt employees and up to 15 days annually for non-exempt employees

• 13+ paid company holidays, and multiple paid coordinated company office closures throughout the year for focus and recharge, plus paid sick or safe time (1 hour per 30 hours worked, or more, as required by applicable state or local law)

• Mental health and wellness support

• Employer-paid basic life and disability coverage

• Annual learning and development stipend to fuel your professional growth

• Daily meals in our offices, and meal delivery credits as eligible

• Relocation support for eligible employees

• Additional taxable fringe benefits, such as charitable donation matching and wellness stipends, may also be provided.

About the Team

The Scaling team is responsible for the architectural and engineering backbone of OpenAI’s infrastructure. We design and deliver advanced systems that support the deployment and operation of cutting-edge AI models. Our work spans system software, networking, platform architecture, fleet-level monitoring, and performance optimization.

About the Role

We’re hiring an SW Engineer to enable production workloads and end-to-end testing on new platforms. This role will include creating new test harnesses and platform stress benchmarks, porting existing inference and training workloads to new, sometimes early-access, systems/hardware, analyzing performance and bottlenecks, and characterizing the end-to-end behavior of new systems (compute, comms, storage, control plane, and failure modes).

Key Responsibilities

• Port and validate key inference and training workloads on new platforms/SKUs as they arrive; drive correctness, performance, and stability to an internal readiness bar.

• Build a suite of benchmarks and stress tests that capture real E2E behavior of our workloads by exercising all aspects of a system, including CPU, GPU, memory subsystem, frontend, scale-up, and scale-out networking (including WAN traffic, NVlink and RDMA collectives), storage, thermals, and any other relevant parts.

• Deep-dive performance on distributed training/inference:

• Collective performance and tuning (across NCCL/RCCL and internal libraries)

• Overlap of compute/communication, kernel-level bottlenecks, memory bandwidth and scheduling effects

• Create repeatable test harnesses that run in CI / lab environments and produce actionable outputs (pass/fail, performance score, regression detection).

• Partner with systems + fleet bring-up engineers to ensure the platform is not only stable and performant, but also operationally usable and scalable (containerization, K8s integration, telemetry hooks, failure triage loops).

• Work cross-functionally with vendors and internal stakeholders by producing clear bug reports, minimal repros, and prioritized issue lists.

Qualifications

• BS in CS/EE (or equivalent practical experience).

• 5+ years in one or more of: ML systems, performance engineering, distributed systems, or HPC.

• Strong hands-on experience with:

• PyTorch and modern LLM training/inference stacks

• Large-scale distributed training concepts (data/model/pipeline parallel, collective comms)

• Experience with RDMA and debugging/optimizing comms libraries (NCCL or RCCL) and their interaction with hardware/network

• Proficiency in Python plus comfort reading/writing performance-critical code (C++/CUDA/HIP is a plus).

• Strong profiling/debugging skills (e.g., Nsight, rocprof, perf, flamegraphs; ability to reason from traces/counters).

Preferred Skills

• Experience building workload-shaped benchmarks and stress/fault tests that correlate to production behavior (not just synthetic loops or microbenchmarks).

• Familiarity with RDMA networking and transport tuning; understanding of how network topology and congestion impact collectives.

• Experience running and validating workloads in Kubernetes, and bridging “research code” into robust, repeatable infrastructure.

• Hands-on lab experience with early hardware (new NICs, new GPUs/accelerators, early racks).

About OpenAI

OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity.

We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic.

XML job scraping automation by YubHub

You will develop modeling frameworks and methodologies to evaluate system-level tradeoffs and guide key design decisions. Your work will directly influence reference architectures, vendor designs, and long-term infrastructure strategy.

This role sits at the intersection of AI workloads, system architecture, and quantitative modeling, and requires strong technical judgment, ownership, and the ability to translate complex analysis into clear, actionable guidance.

Key Responsibilities:

• Build and own a performance modeling framework/toolchain to evaluate AI systems across multiple levels of abstraction.
• Analyze and quantify architectural tradeoffs across compute, memory, networking, storage, and system topology.
• Develop performance models to guide decisions on:
• scale-up vs. scale-out architectures
• interconnect and network design
• memory hierarchy and system balance.
• Translate modeling outputs into clear recommendations for internal teams and external hardware vendors.
• Influence reference designs and vendor roadmaps through data-driven insights.
• Partner closely with machine learning, systems, and hardware teams to understand workload characteristics and requirements.
• Lead and grow a small team (2–3 engineers), setting technical direction and maintaining high standards for modeling rigor.
• Continuously improve modeling fidelity by validating against real system behavior and measurements.

Qualifications:

• Have experience owning or building performance modeling frameworks used to drive real system design decisions.
• Have deep knowledge of AI/ML workloads, including training and/or inference at scale.
• Understand system-level tradeoffs across compute, memory, and networking in large-scale distributed systems.
• Are comfortable working across abstraction layers,from workload behavior to hardware implementation.
• Have experience using modeling (analytical or simulation) to inform architectural decisions.
• Can operate in ambiguous problem spaces and turn open-ended questions into structured analysis.
• Communicate clearly and influence both internal teams and external partners.

Preferred Skills:

• Experience working with hardware vendors (ODM/JDM, silicon, networking).
• Background in data center infrastructure or hyperscale systems.
• Familiarity with accelerators (GPUs/ASICs) and interconnects (e.g., NVLink, InfiniBand, Ethernet).
• Experience influencing hardware roadmaps or reference architectures.
• Prior experience leading or mentoring engineers.

XML job scraping automation by YubHub

You will work closely with performance modeling and internal architecture teams to evaluate tradeoffs, while owning the end-to-end definition and execution of third-party system designs. This includes identifying gaps in current technologies, driving vendor development, and shaping future infrastructure capabilities.

This role requires strong system intuition, cross-functional leadership, and the ability to operate effectively across internal teams and external ecosystems.

Key Responsibilities:

• Define rack- and cluster-level reference architectures for AI infrastructure deployments.
• Translate workload requirements into clear system design specifications and partner deliverables.
• Collaborate with performance modeling teams to evaluate architectural tradeoffs and system behaviors.
• Align internal stakeholders and external partners on critical system attributes (performance, cost, power, reliability, scalability).
• Identify gaps in current technology offerings and drive vendors (ODM/JDM, silicon, networking) to close those gaps.
• Influence and shape vendor roadmaps to meet future infrastructure needs.
• Track emerging technologies and evaluate their applicability to AI systems.
• Define and lead proof-of-concept (PoC) efforts to validate new architectures and technologies.
• Act as a key interface between OpenAI and external partners, ensuring execution against design intent.

Qualifications:

• Have strong experience in system architecture for large-scale infrastructure or data center environments.
• Understand AI workload characteristics and how they map to system-level design decisions.
• Are comfortable working with performance modeling outputs to inform architectural direction.
• Have experience working with or managing hardware vendors (ODM/JDM, silicon, networking).
• Can drive alignment across multiple stakeholders with competing constraints.
• Have a track record of turning ambiguous requirements into clear, executable system designs.
• Are proactive in identifying gaps and driving solutions across organizational boundaries.

Preferred Skills:

• Experience defining rack- or cluster-level systems for hyperscale or AI workloads.
• Familiarity with accelerators (GPUs/ASICs), interconnects, and data center networking architectures.
• Experience influencing vendor roadmaps and reference designs.
• Background in infrastructure deployment, hardware engineering, or systems integration.
• Experience leading PoCs or early-stage hardware validation efforts.

XML job scraping automation by YubHub

In this role, you will work closely with the Performance Modeling Lead and partner teams to analyze system behavior, run simulations or analytical models, and help quantify tradeoffs across compute, memory, networking, and storage. You will contribute to building modeling frameworks and applying them to real-world questions that impact system design and vendor decisions.

This role is well-suited for engineers with strong software or modeling backgrounds who are interested in developing deeper expertise in system architecture and AI infrastructure.

Key Responsibilities

• Develop and maintain performance modeling tools and frameworks.

• Build models to evaluate system behavior across:
• compute, memory, and interconnect subsystems
• distributed system scaling and bottlenecks.

• Run simulations and analytical models to support architectural tradeoff analysis.

• Collaborate with performance modeling lead and system architects to answer forward-looking design questions.

• Analyze and interpret modeling outputs, translating results into actionable insights.

• Validate models against real system measurements and workload behavior.

• Contribute to improving modeling fidelity, usability, and scalability.

Qualifications

• Strong software engineering or modeling background (e.g., simulation, systems modeling, or performance analysis).

• Familiarity with system architecture fundamentals (compute, memory, networking).

• Experience with programming and building technical tools or frameworks.

• Ability to reason about performance bottlenecks and scaling behavior.

• Strong analytical skills and comfort working with quantitative models.

• Ability to collaborate across teams and learn new system domains quickly.

Preferred Skills

• Exposure to AI/ML workloads or distributed systems.

• Experience with simulation tools, performance modeling, or systems analysis.

• Familiarity with data center infrastructure or large-scale systems.

• Experience working with performance data, benchmarking, or profiling tools.

• Interest in system architecture and hardware/software co-design.

XML job scraping automation by YubHub

San Francisco; Seattle

Employment Type

Full time

Department

Applied AI

Compensation

• $230K – $385K • Offers Equity

The base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. If the role is non-exempt, overtime pay will be provided consistent with applicable laws. In addition to the salary range listed above, total compensation also includes generous equity, performance-related bonus(es) for eligible employees, and the following benefits.

• Medical, dental, and vision insurance for you and your family, with employer contributions to Health Savings Accounts

• Pre-tax accounts for Health FSA, Dependent Care FSA, and commuter expenses (parking and transit)

• 401(k) retirement plan with employer match

• Paid parental leave (up to 24 weeks for birth parents and 20 weeks for non-birthing parents), plus paid medical and caregiver leave (up to 8 weeks)

• Paid time off: flexible PTO for exempt employees and up to 15 days annually for non-exempt employees

• 13+ paid company holidays, and multiple paid coordinated company office closures throughout the year for focus and recharge, plus paid sick or safe time (1 hour per 30 hours worked, or more, as required by applicable state or local law)

• Mental health and wellness support

• Employer-paid basic life and disability coverage

• Annual learning and development stipend to fuel your professional growth

• Daily meals in our offices, and meal delivery credits as eligible

• Relocation support for eligible employees

• Additional taxable fringe benefits, such as charitable donation matching and wellness stipends, may also be provided.

More details about our benefits are available to candidates during the hiring process.

This role is at-will and OpenAI reserves the right to modify base pay and other compensation components at any time based on individual performance, team or company results, or market conditions.

About the Team

The Support Automation team at OpenAI scales the organization by applying cutting-edge AI models to real-world challenges, automating and enhancing work across the organization. From customer operations to engineering, we develop an ecosystem of automation products that empower our colleagues and drive impact. We're passionate about crafting products that serve those around us, blending rapid prototyping with a focus on long-term quality and reliability. By creating reusable solutions, we create patterns that can be applied across diverse domains within OpenAI.

TLDR: this team leverages OpenAI technology to improve OpenAI, and you’ll have the opportunity to leverage the full extent of our tech (both public and pre-released) to accomplish this mission.

About the Role

We’re looking for a Backend Software Engineer with experience working in ML/LLM-heavy domains to help to design and build an evals infrastructure that measures the quality of OpenAI’s support automation. This is a deeply technical and highly cross-functional role where you’ll build robust systems and backend services that serve as the foundation for how knowledge is created, accessed, and applied across OpenAI. The role will especially focus on working closely with Data Science and Research partners to design and build evals at scale.

In this role, you will:

• Design eval pipelines that are reliable, reproducible, and extendable

• Build the infrastructure for continuous eval monitoring frameworks (regression/drift monitoring, building robust golden datasets) along with feedback loops that ultimately strengthen support automation

• Design, build, and maintain backend services and APIs to support intelligent automation and knowledge systems

• Integrate and structure data across internal platforms, transforming it into formats optimized for use by downstream systems and AI workflows.

• Collaborate closely with data, research, and engineering teams to integrate OpenAI models into high-leverage workflows

• Own the full development lifecycle of new backend systems and internal platform capabilities

• Build with scale and maintainability in mind, while rapidly iterating on new ideas

You might be a great fit if you have:

• 4+ years of backend engineering experience at product-driven companies (excluding internships)

• Proficiency in backend technologies. Our tech stack includes Python, FastAPI, and Postgres

• Experience designing and scaling distributed systems, APIs, or data processing pipelines

• Have experience building AI agents or applications, including designing evals and improving performance through prompting or scaffolding

• Are familiar with evaluation methods for LLMs and have worked with patterns like multi-agent workflows, tool use, or long context.

• Experience creating production evals and/or measuring performance of ML/LLM models at scale

• A pragmatic mindset. You’re comfortable shipping iteratively while building toward a long-term vision

About OpenAI

OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity.

XML job scraping automation by YubHub