Model Designer Jobs

About the Team Training Runtime designs the core distributed machine-learning training runtime that powers everything from early research experiments to frontier-scale model runs. With a dual mandate to accelerate researchers and enable frontier scale, we’re building a unified, modular runtime that meets researchers where they are and moves with them up the scaling curve. Our work focuses on three pillars: high-performance, asynchronous, zero-copy tensor and optimizer-state-aware data movement; performant, high-uptime, fault-tolerant training frameworks (training loop, state management, resilient checkpointing, deterministic orchestration, and observability); and distributed process management for long-lived, job-specific and user-provided processes. We integrate proven large-scale capabilities into a composable, developer-facing runtime so teams can iterate quickly and run reliably at any scale, partnering closely with model-stack, research, and platform teams. Success for us is measured by raising both training throughput (how fast models train) and researcher throughput (how fast ideas become experiments and products). About the Role As a Training: ML Framework Engineer, you will work on improving the training throughput for our internal training framework, while enabling researchers to experiment with new ideas. This requires good engineering (for example designing, implementing, and optimizing state-of-the-art AI models), writing bug-free machine learning code (surprisingly difficult!), and acquiring deep knowledge of the performance of supercomputers. In all the projects this role pursues, the ultimate goal is to push the field forward. We’re looking for people who love optimizing performance, understanding distributed systems, and who cannot stand having bugs in their code. Since our training framework is used for large runs with massive numbers of GPUs, performance improvements here will have a large impact. This role is based in San Francisco, CA. We use a

Overview: The Data Acquisition team within the Foundations organization at OpenAI is responsible for all aspects of data collection to support our model training operations. Our team manages web crawling and GPTBot services and works closely with Data Processing, Architecture, and Scaling teams. We are looking for a skilled Full-Stack Engineer to join our Data Acquisition team to build and optimize the interfaces and tools that power our data infrastructure. Responsibilities: Develop and maintain full-stack applications that support data acquisition, including internal tools and dashboards. Collaborate closely with cross-functional teams, including Data Processing, Architecture, and Scaling, to ensure seamless data ingestion and workflow management. Design and implement APIs to facilitate data interactions between internal services and external data sources. Enhance user experience by developing intuitive web-based interfaces for managing and monitoring data pipelines. Optimize backend services for performance, scalability, and security in a distributed computing environment. Work with legal and compliance teams to ensure our data acquisition processes adhere to privacy regulations and best practices. Deploy and maintain infrastructure using Kubernetes and Infrastructure-as-Code (IaC) methodologies. Analyze system performance, conduct experiments, and improve data workflows to maximize efficiency. Qualifications: BS/MS/PhD in Computer Science or a related field. 4+ years of industry experience in full-stack development. Proficiency in frontend frameworks (React, Vue, or similar) and backend technologies such as Python, Node.js, or Go. Strong expertise in RESTful APIs, GraphQL, and database design (SQL and NoSQL). Experience building data-intensive applications that handle large-scale datasets. Familiarity with cloud platforms (AWS, GCP, or Azure) and container orchestration (Kubernetes, Docker). Prior experience with web crawling and large-scale data processing is a

About the Team OpenAI's Training team is responsible for producing the large language models that power our research, our products, and ultimately bring us closer to AGI. Achieving this goal requires combining deep research into improving our current architecture, datasets and optimization techniques, alongside long-term bets aimed at improving the efficiency and capability of future generations of models. We are responsible for integrating these techniques and producing model artifacts used by the rest of the company, and ensuring that these models are world-class in every respect. Recent examples of artifacts with major contributions from our team include GPT4-Turbo, GPT-4o and o1-mini. About the Role As a member of the architecture team, you will push the frontier of architecture development for OpenAI's flagship models, enhancing intelligence, efficiency, and adding new capabilities. Ideal candidates have a deep understanding of LLM architectures, a sophisticated understanding of model inference, and a hands-on empirical approach. A good fit for this role will be equally happy coming up with a creative breakthrough, investing in strengthening a baseline, designing an eval, debugging a thorny regression, or tracking down a bottleneck. This role is based in San Francisco. 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, prototype and scale up new architectures to improve model intelligence Execute and analyze experiments autonomously and collaboratively Study, debug, and optimize both model performance and computational performance Contribute to training and inference infrastructure You might thrive in this role if you: Have experience landing contributions to major LLM training runs Can thoroughly evaluate and improve deep learning architectures in a self-directed fashion Are motivated by safely deploying LLMs in the real world Are well-versed in the state of the art tran

AI Systems Engineer - Codex Core Agents About The Team The Codex Core Agents team builds the agent harness that turns model capability into real-world action. We own the systems around the model: prompting and interpreting model outputs, executing actions safely in real environments, and feeding production experience back into better models and better agent behavior. This team sits close to research and works across the stack: harness, model interaction, inference, sandboxed execution, orchestration, evals, production reliability, and the performance envelope around tokens, latency, cost, capacity, and quality. The harness is open source and increasingly part of how models are trained and evaluated, making this one of the highest-leverage layers in Codex. About The Role We’re looking for engineers to build the AI systems that make Codex agents dependable in production. The ideal candidate is an agent-systems builder: hands-on across low-level systems and ML workflows, able to debug Codex behavior end to end across the harness, model behavior, inference/runtime stack, GPU fleet, and product surface. You’ll work with research, infrastructure, and product to design agent harness capabilities, run experiments and ablations across the model + system prompt + harness stack, build frameworks for assessing production agent performance, and turn messy failures into durable improvements. What You’ll Do Design and build the core agent harness and execution loop that lets Codex agents interpret model outputs, use tools, execute code, and complete long-horizon tasks safely. Build sandboxing, isolation, orchestration, state, and workflow infrastructure for agents operating in real development environments. Develop evaluation, experimentation, and debugging systems that distinguish harness issues, model behavior, inference/runtime issues, and product failures. Run ablations across prompts, model-facing interfaces, context construction, tool-use strategies, and harness behavior to

About the Team Training Runtime designs the core distributed machine-learning training runtime that powers everything from early research experiments to frontier-scale model runs. With a dual mandate to accelerate researchers and enable frontier scale, we’re building a unified, modular runtime that meets researchers where they are and moves with them up the scaling curve. Our work focuses on three pillars: high-performance, asynchronous, zero-copy tensor and optimizer-state-aware data movement; performant, high-uptime, fault-tolerant training frameworks (training loop, state management, resilient checkpointing, deterministic orchestration, and observability); and distributed process management for long-lived, job-specific and user-provided processes. We integrate proven large-scale capabilities into a composable, developer-facing runtime so teams can iterate quickly and run reliably at any scale, partnering closely with model-stack, research, and platform teams. Success for us is measured by raising both training throughput (how fast models train) and researcher throughput (how fast ideas become experiments and products). About the Role As a Training Performance Engineer, you’ll drive efficiency improvements across our distributed training stack. You’ll analyze large-scale training runs, identify utilization gaps, and design optimizations that push the boundaries of throughput and uptime. This role blends deep systems understanding with practical performance engineering — analyzing GPU kernel performance, collective communication throughput, investigating I/O bottlenecks, and sharding our models so we can train them at massive scale. You’ll help ensure that our clusters are running at peak performance, enabling OpenAI to train larger, more capable models with the same compute budget. This role is based in San Francisco, CA. We use a hybrid work model of three days in the office per week and offer relocation assistance to new employees. In this role, you will: Profil

About the Team Training Runtime designs the core distributed runtime that powers everything from early research experiments to frontier-scale model runs. We work on building robust, scalable, high performance components to support our distributed training workloads. Our priorities are to maximize the productivity of our researchers and our hardware, with the goal of accelerating progress towards AGI. Within Training Runtime, the Process Management team develops the distributed OS responsible for launching, coordinating, and supervising the large numbers of processes that make up modern training workloads. Our runtime sits beneath training frameworks and on top of research infrastructure, ensuring jobs run reliably across massive clusters while maintaining performance, stability, and observability. Success for us is measured by both system reliability and researcher velocity - enabling ideas to scale from experiments to production training runs. About the Role As a Training Runtime: Process Management Engineer , you will work on the software that ties thousands of computers together and exposes them as a unified system. This system has to serve individual researchers running multiple parallel experiments, as well as our largest training runs spanning 100’s of thousands and even millions of machines and accelerators. This requires easy to use, introspectable systems that can promote a fast debugging and development cycle, as well as relentless optimization for scale while maintaining stability and performance throughout. You will work primarily in Rust , building high-performance asynchronous systems with a strong emphasis on performance, correctness, and scalability. Working at this scale and at the frontier of AI development poses novel challenges. Out-of-the-box approaches often don’t work. The problems you will be working on are highly ambiguous and require strong design judgment as well as proficient execution to advance the state of our infrastructure. We’re loo

About the Team The Cooperative AI team is scaling OpenAI with OpenAI. We are building a model-powered scaled automated workforce and knowledge system that evolves and learns alongside a human workforce. By leveraging OpenAI’s state-of-the-art models and technologies, some already in production, others still in the lab, we develop systems that reason and work autonomously for a wide variety of operational work. We leverage real workloads for critical systems across finance, sales, customer support, integrity, product insights, internal operations, and more in order to drive insights into product and industry. We partner closely with internal teams and external customers globally, operating in a hyper-fast feedback loop where many of our users are just a few steps away. This proximity allows us to iterate quickly, validate impact in real time, and accelerate industry impacting learnings and systems builds. We are a highly multidisciplinary, self-contained team focused on transforming the workplace via smart systems, knowledge, scalable and reliable primitives that apply world-class AI capabilities across domains. Our mission is to learn fast and transform how humans collaborate with AI at scale. About the Role We are looking for a hands-on Engineering Manager to lead a small, fast-moving team building AI-powered automation systems that redefine how work gets done across OpenAI. This role sits at the intersection of applied AI, research, and product engineering. You’ll lead a team that builds systems that know how to learn from humans, and carry real workloads across, sales, support, finance, IT, and more, while staying deeply involved in the technical work. You will operate in a highly iterative environment, deploying systems directly to internal users, gathering rapid feedback, and evolving solutions in real time. This is a high-ownership role for someone excited about building 0→1 systems, working closely with customers, and shaping how AI transforms operational wor

About the Team Our team analyzes inference stack performance across the application, model, and fleet layers to identify bottlenecks and drive faster, cheaper inference. We combine systems profiling, benchmarking, and analysis to understand where time and cost are spent, then turn that understanding into performance optimizations and models that project performance and capacity needs for future launches. About the Role In this role, you will model inference performance across application, model, and fleet layers with higher fidelity. You will build cost-to-serve estimates from microbenchmarks and create tools that help cross-functional teams reason about latency, capacity, utilization, and cost tradeoffs. In this role, you will Build and refine performance models that translate microbenchmark results into cost-to-serve estimates. Analyze inference workloads end to end across applications, models, and fleet infrastructure. Enhance tooling to identify bottlenecks across layers for latency and throughput. Partner with other teams to turn performance insights into concrete improvements and project how future changes affect inference. You might thrive in this role if you: Enjoy reasoning from first principles about distributed systems, model inference, and hardware efficiency. Are comfortable working across abstraction layers, from application behavior to kernels, accelerators, networking, and fleet scheduling. Have deep expertise with performance profiling, benchmarking, analysis, and optimization. Enjoy collaborating with engineering and research teams to improve real production systems. 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 o

About the Role Together AI is building the AI Acceleration Cloud. We are building an end-to-end platform for the generative AI lifecycle, integrating fast, reliable inference and model-shaping services with cutting-edge AI cloud infrastructure. We're looking for an exceptional Senior GTM & Business Recruiter who can help us scale our teams while keeping the bar of excellence high. You'll own end-to-end hiring across GTM and Business functions, from first conversation through offer close, and partner with GTM leadership to translate ambitious hiring plans into real teams. This role is based out of our Headquarters in San Francisco. Responsibilities Own full-cycle strategic recruiting across GTM and Business organizations. Partner with executives and leaders to define hiring strategies, candidate profiles, and interview processes. Manage the candidate journey from sourcing handoff to offer stage, ensuring a seamless and exceptional experience. Provide market intelligence, funnel metrics, and competitive insights to shape hiring strategies and influence decisions. Collaborate with sourcers, coordinators, and recruiters to streamline processes and deliver a consistent, brand-aligned candidate experience. Act as a strategic thought partner to leaders, scaling from concept to execution with data-driven insights, creativity, and sound judgment. Requirements 5+ years of business or GTM recruiting experience in high-growth tech environments. Strong ability to build and maintain relationships with leadership and hiring teams. You thrive in a fast-paced, ambiguous environment with rapidly shifting priorities. Expertise in designing new interview processes and hiring strategies from the ground up. You adapt gracefully under pressure and navigate changing priorities with ease. Ability to do more with less, you thrive in a scrappy environment and you find creative solutions to problems About Together AI Together AI, the AI Native Cloud, is purpose-built for AI engineers

About ElevenLabs ElevenLabs is an AI research and product company transforming how we interact with technology. We launched in January 2023 with the first human-like AI voice model. Today, we serve millions of users and thousands of businesses - from fast-growing startups to large enterprises like Deutsche Telekom and Meta. Our investors are some of the world's most prominent, including Andreessen Horowitz, ICONIQ Growth and Sequoia. We've raised $781M in funding and our last valuation was $11B - multiples of 11, always. We have expanded from voice into three main platforms: ElevenAgents enables businesses to deliver seamless and intelligent customer experiences, with the integrations, testing, monitoring, and reliability necessary to deploy voice and chat agents at scale. ElevenCreative empowers creators and marketers to generate and edit speech, music, image, and video across 70+ languages. ElevenAPI gives developers access to our leading AI audio foundational models. Everything we do is the result of the creativity and commitment of our team - builders doing the best work of their lives. We are researchers, engineers, and operators. IOI medalists and ex-founders. If you want to work hard and create lasting positive impact, we want to hear from you. How we work High-velocity: Rapid experimentation, lean autonomous teams, and minimal bureaucracy. Impact not job titles: We don’t have job titles. Instead, it’s about the impact you have. No task is above or beneath you. AI first: We use AI to move faster with higher-quality results. We do this across the whole company—from engineering to growth to operations. Excellence everywhere: Everything we do should match the quality of our AI models. Global team: We prioritize your talent, not your location. What we offer Innovative culture: You’ll be part of a generational opportunity to define the trajectory of AI, surrounded by a team pushing the boundaries of what’s possible. Growth paths: Joining ElevenLabs means joining a

About the Team The Finance & Strategy team accelerates the growth of our business by identifying and implementing new opportunities and solutions. We partner with teams across the company to improve the profitability, scalability and defensibility of our business model. We're looking for people who can use strategy to identify areas of opportunity, and collaborate to help carry projects from conception through execution. About the Role We are looking for a Finance Manager who will work with partners across DoorDash to ensure we are making the most efficient investment and capital allocation decisions while improving the experience for our merchants and other marketplace participants. You will help lead financial reporting, forecasting, and planning, and will guide financial insights and direction for all of our operational decisions and strategies. You’re excited about this opportunity because you will… Conduct weekly reporting of financial performance of DoorDash’s Merchant business, forecast key financial metrics based on quantitative analysis of historical data.Support quarterly financial planning, collaborate with cross functional business partners to identify growth areas and business priorities.Deep-dive into complex operational questions and collaborate cross-functionally outside Finance (Sales, Operations, Support) as well as within Finance (Accounting, Analytics, BI) to uncover the most optimal data-driven business decisions and investments.Identify key opportunities & risks to the business, quantify and evaluate the impacts, propose a solution and investment case, and ensure efficient execution. Leverage and apply knowledge of SQL, finance, analytics, and technology to manage financial reporting, forecasting, and quantitative analysis to assist DoorDash in making better business decisions. We’re excited about you because… 6+ years of experience overall, preferably from Strategic Finance, Corporate Finance, Investment Banking, or Consulti

About the Team Life sciences is one of the clearest areas where advances in intelligence can meaningfully benefit the world at large. The OpenAI Life Sciences team works at the intersection of advancing frontier life sciences model capabilities and building products to help scientists accelerate their research and leverage the full potential of AI for scientific work. Rosalind Workbench brings together the scientific tools, data sources, interactive biology file viewers and core life sciences workflows into a central environment to help scientists investigate questions, design experiments, analyze results, and advance discovery. Rosalind Workbench can be used with any OpenAI model, including GPT-Rosalind--our dedicated life sciences model, which combines frontier reasoning with specialized tool orchestration across medicinal chemistry, genomics, wet-lab assistance, and other scientific applications. Our long term vision is for teams of agents to work together across these domains, giving researchers access to broader expertise and the ability to pursue more ambitious scientific questions. About the Role We’re looking for a Product Manager to shape Rosalind Workbench for life sciences. You will own product strategy and execution, working directly with researchers in academic labs, biotech, and pharma to understand where AI can meaningfully improve their work. You’ll partner with engineering, research, design, and customer-facing teams to turn emerging capabilities into intuitive products that scientists return to. This role calls for strong product judgment, depth in life sciences, and the ability to move from an ambiguous research problem to a focused, shippable experience. In this role, you will have the opportunity to define the future of AI guided scientific discovery and build the capabilities and tools that help advance the scientific frontier for researchers and help increase the accessibility of model intelligence for scientific use. This is a chance to shape

Role Summary The CISO, Infrastructure and Cloud Services organization enables Pfizer’s mission by delivering secure, resilient, and scalable technology platforms that support operations and future growth. Through a unified operating model, it strengthens accountability, accelerates decision‑making, and improves availability, security, and cost efficiency. The CISO Marketing Manager will support the Senior Manager, Marketing and Enterprise Awareness to develop meaningful marketing materials that tell the CISO story to external clients by executing strategic, creative, and visually compelling communications including presentations, graphics, pictures, charts and tables that support cybersecurity awareness, organizational priorities, and business outcomes. The manager will lead the creation of a marketing strategy for the Chief Information Security Office Team. Role Responsibilities Lead the development and execution of integrated marketing and communications strategies in support of CISO priorities, strategic initiatives, and portfolio management objectives. Produce high-quality written, visual, and digital content that strengthens awareness, engagement, and understanding of cybersecurity programs across the organization. Create and manage internal communications, including newsletters, leadership messages, campaign materials, SharePoint content, presentations, infographics, and other digital assets. Simplify complex cybersecurity and technical information into clear, engaging, and actionable content for diverse audiences, including executives, business partners, and internal colleagues. Partner closely with cross-functional teams across Cyber Defense, Infrastructure, Cloud, and related Digital and Technology groups to ensure consistent, coordinated, and impactful messaging. Develop executive-ready presentations and visual storytelling materials

AI Teammates are Asana’s flagship AI innovation - autonomous agents embedded directly into enterprise workflows that reason, act, and proactively drive work forward. The AI Teammates Platform (AITP) team owns the execution engine and platform layer that makes these AI Teammates effective, reliable, and scalable. We build the core systems behind agentic execution: model evaluation and rollouts, proactive detection of blocked work, execution quality infrastructure, tool orchestration, and the platform APIs that power AI across all of Asana engineering. Rather than building a monolithic feature, we build a composable foundation. If a Teammate reasons, acts, or autonomously improves - that’s us. We’re looking for an experienced Technical Lead to drive the technical strategy, architecture, and execution for the AI Teammates Platform. You will bridge the gap between frontier AI capability and enterprise-grade software engineering, operating in close partnership with frontier model providers while building systems that scale to millions of users. If you are passionate about applied AI, agentic orchestration, high-reliability backend systems, and growing high-performing engineering teams, we’d love to hear from you! This role is based in our San Francisco office with an office-centric hybrid schedule. The standard in-office days are Monday, Tuesday, and Thursday. Most Asanas have the option to work from home on Wednesdays. Working from home on Fridays depends on the type of work you do and the teams with which you partner. If you're interviewing for this role, your recruiter will share more about the in-office requirements. What You’ll Achieve Set the technical strategy for the AI Teammates execution engine, tool orchestration, and platform APIs within Asana, advocating for engineering-driven investments with a vision for keeping our systems flexible, reliable, and maintainable to meet customer needs now and in the future Drive the team to continually and holistically

We build and operate the compute infrastructure our researchers run on, supporting large-scale processing of historical market data and model training on our own hardware across multiple data centers. Our environment includes bare-metal Linux, virtualization, storage, and GPU clusters, where performance, reliability, and predictable system behavior are critical. Our Infrastructure team covers monitoring and automation, distributed storage, hardware and OS provisioning, GPU clusters and workload scheduling, high-speed networking, L2/L3 Linux support, and security engineering. Engineers here own their tasks end to end, so there's room to go deeper in your area and pick up the parts you haven't touched yet. We’re looking for a Linux Infrastructure Engineer who can work hands-on with server and cluster environments, from deployment and configuration to performance tuning, troubleshooting, and ongoing improvement What You’ll Be Doing: Deploying, configuring, and maintaining Linux-based bare-metal servers across our data centers Building and operating clustered environments, including virtualization, storage, GPU compute, and database clusters Troubleshooting complex Linux, hardware, networking, and cluster-level issues Performance tuning for throughput, latency, stability, and resource utilization Monitoring infrastructure health and performance, identifying bottlenecks, and preventing recurring issues Supporting the full server lifecycle: provisioning, setup, upgrades, and maintenance Improving reliability and predictability during failures, maintenance, and scaling Automating provisioning, configuration, and operational tasks, primarily using Ansible and scripting What We Look For In You: Strong hands-on Linux administration and troubleshooting experience Production experience with on-premise, bare-metal infrastructure Good understanding of Linux performance and bottleneck analysis Experience with: infrastructure monitoring and troubleshooting production issues,