Software Engineer, AI accelerator Runtime

About the Team OpenAI’s Hardware organization develops AI-native silicon and system-level solutions for the unique demands of advanced AI workloads. Building on efforts like Jalapeño, the team is developing future generations of AI-native silicon and tightly integrated systems to power the next generation of frontier models. By co-designing chips, systems, tools, and methodologies, the team helps deliver faster, more efficient, and production-ready hardware for OpenAI’s supercomputing platform. About the Role You will build the model runtime within the inference engine that executes complex, frontier models at scale on OpenAI’s custom silicon. The runtime will sit between models running on the hardware and the upper layers of the cluster serving software stack, translating demanding inference workloads into efficient execution while optimizing for throughput, latency, utilization, and reliability. You will work across model architecture, distributed systems, compilers, kernels, and silicon to design a production-grade runtime comparable in ambition to systems such as vLLM and SGLang, but customized and optimized for OpenAI’s AI accelerator. Your work will shape how new model capabilities map onto the platform and how quickly custom silicon can deliver meaningful performance in production. In this role, you will: Design and implement the LLM inference runtime for frontier models running on custom silicon. Build scheduling, continuous batching, memory management, KV-cache management, and execution orchestration for high-performance inference. Develop distributed execution strategies across chips, hosts, and racks, including model partitioning, communication, and synchronization. Optimize end-to-end latency, throughput, memory efficiency, and hardware utilization across diverse model architectures and serving workloads. Partner with kernel, compiler, architecture, and silicon teams to co-design interfaces and remove performance bottlenecks across the stack. Enable new

About the Team The GPT Infrastructure team builds systems that turn advances in model inference and optimization into reliable production capabilities. We enable OpenAI workloads to be qualified and optimized across new accelerator platforms without requiring a one-off port and tuning effort for every hardware target. Our work spans distributed systems, model execution, compilers and runtimes, performance engineering, secure partner integrations, evaluation systems, and developer tooling. We build the infrastructure that makes optimization workflows automated, reproducible, and trustworthy. About the Role We are seeking a software engineer to help build the platform that qualifies and optimizes inference workloads across heterogeneous compute environments. You will develop both OpenAI-hosted services and secure partner-side software for running long-lived optimization workflows. These workflows generate candidate kernels, runtime configurations, and serving-stack changes; compile and execute them on target hardware; verify their correctness; measure their performance; and use the results to guide further optimization. You will work across model architecture, distributed execution, compilers, runtimes, networking, and accelerator systems. A central part of the role is turning research prototypes and one-off hardware bring-up efforts into reliable, reusable infrastructure with clear contracts, reproducible results, strong observability, and well-defined security boundaries. Key Responsibilities Design, build, and operate APIs and control-plane services for long-running workload qualification and optimization campaigns, including scheduling, retries, checkpointing, resource budgets, and observability. Build secure partner-side execution and evaluation software that can compile, run, verify, profile, and benchmark candidate artifacts on accelerator hardware. Integrate model workloads, hardware profiles, compiler toolchains, runtimes, serving engines, and distributed-exe

About the Team OpenAI’s Hardware organization develops AI-native silicon and system-level solutions for the unique demands of advanced AI workloads. Building on efforts like Jalapeño, the team is developing future generations of AI-native silicon and tightly integrated systems to power the next generation of frontier models. By co-designing chips, systems, tools, and methodologies, the team helps deliver faster, more efficient, and production-ready hardware for OpenAI’s supercomputing platform. About the Role On the Accelerators team, you will help OpenAI evaluate and bring up new compute platforms that can support large-scale AI training and inference. Your work will range from prototyping system software on new accelerators to enabling performance optimizations across our AI workloads. You’ll work across the stack, collaborating with both hardware and software aspects - working on kernels, sharding strategies, scaling across distributed systems, and performance modeling. You'll help adapt OpenAI's software stack to non-traditional hardware and drive efficiency improvements in core AI workloads. This is not a compiler-focused role, rather bridging ML algorithms with system performance - especially at scale. In this role, you will: Prototype and enable OpenAI's AI software stack on new, exploratory accelerator platforms. Optimize large-scale model performance (LLMs, recommender systems, distributed AI workloads) for diverse hardware environments. Develop kernels, sharding mechanisms, and system scaling strategies tailored to emerging accelerators. Collaborate on optimizations at the model code level (e.g. PyTorch) and below to enhance performance on non-traditional hardware. Perform system-level performance modeling, debug bottlenecks, and drive end-to-end optimization. Work with hardware teams and vendors to evaluate alternatives to existing platforms and adapt the software stack to their architectures. Contribute to runtime improvements, compute/communication over

About the Team Our mission at OpenAI is to discover and enact the path to safe, beneficial AGI. To do this, we believe that many technical breakthroughs are needed in generative modeling, reinforcement learning, large-scale optimization, active learning, and other areas. The team builds the performance-critical systems that allow OpenAI's models to run efficiently across a diverse set of AI accelerators. We work across the inference stack, from low-level kernels and compilers through model execution, to unlock the full capabilities of the underlying hardware. About the Role As a Software Engineer, Trainium, you will help bring OpenAI's inference workloads to AWS Trainium and build the software stack required to run cutting-edge frontier models efficiently on the platform. This is a deeply technical, cross-stack role spanning kernels, compilers, and model execution. You will work on the systems needed to support OpenAI's inference stack on Trainium, including developing and optimizing high-performance kernels, improving compiler support, and enabling efficient execution of the model forward pass. You'll work closely with engineers across inference, compilers, kernels, and ML systems to identify performance bottlenecks and build the software needed to take full advantage of Trainium. The work may range from low-level hardware-specific optimization to compiler and runtime improvements to integrating new model architectures into the inference stack. If you enjoy working at the intersection of ML systems, compilers, kernels, and accelerator hardware, this role is for you. We're looking for engineers who are self-directed, comfortable operating across abstraction layers, and excited to solve challenging performance problems for frontier-scale AI systems. In This Role, You Will Build and optimize OpenAI's inference stack for AWS Trainium. Develop high-performance kernels for critical model operations and workloads. Extend and improve compiler support to efficiently target

The Opportunity Typography is central to how ideas are communicated. If you're passionate about beautifully created design, have deep curiosity about what AI can do, and take personal responsibility for creating products that people love; then this may be the role for you. Adobe Fonts supports millions of creatives in choosing and using typefaces across fonts.adobe.com, Express, Photoshop, Illustrator, Acrobat, and more Creative Cloud platforms. Our Internal Services team provides the platform engineering and deployment backbone for all of these. We manage CI/CD, deployment approaches, the services and data layers our engineers depend on, our observability and security stance, and increasingly the agentic tools that transform how our entire organization delivers software. We're seeking a Senior Software Development Engineer to lead this exciting journey in our San Francisco location. What you'll do Own and evolve our deployment platform. Lead strategy for CI/CD, PR environments, and release safety across a mixed fleet that includes containerized services, serverless services, and static front ends. Build the foundation for AI-accelerated development. Help build our agent factory and grow our internal agentic toolkit and skill library. Ship inference applications at scale. Take greenfield services from spec to production and standardize our ML/inference footprint. Modernize our services for the AI era. Identify where an existing service is held back by its current build and lead the fix. Rethink our security posture for agentic threats. Lead how we secure autonomous agents and their tool use. Expose Adobe Fonts to the agentic ecosystem. Extend our Model Context Protocol (MCP) surface and conversational, intent-based font discovery. Work higher up the stack, too. Contribute directly to search, browse, discovery, and the customer-facing experie

About Mixpanel Mixpanel is the leading product intelligence and analytics platform, trusted by more than 29,000 companies to help understand how people use the products they build. By combining powerful analytics with AI that knows your business, Mixpanel helps teams see what’s working, diagnose what’s not, and decide what to build next. Learn more at mixpanel.com . About Mixpanel Mixpanel turns data clarity into innovation. Trusted by more than 29,000 companies, including Workday, Pinterest, LG, and Rakuten Viber, Mixpanel’s AI-first digital analytics help teams accelerate adoption, improve retention, and ship with confidence. Powering this is an industry-leading platform that combines product and web analytics, session replay, experimentation, feature flags, and metric trees. Mixpanel delivers insights that customers trust. Visit mixpanel.com to learn more. About The Team Mixpanel Engineering is a small, fast-moving team focused on delivering real value to customers. We build powerful AI-powered product analytics while obsessing over clarity, simplicity, and delight. Engineers here own problems end to end. You can move across the stack to ship impact without being blocked by silos or heavy process. Product innovation drives our business, and product engineering teams own that responsibility. Our OLAP engine queries over 500 trillion events; a typical blob storage system we interact with processes 300 PiB/month at 1.2 Tbps sustained, and we run many of them across the world. The Data Runtime team owns the data execution layer that powers every Mixpanel product. We ensure that every customer query runs fast, cheap, and reliably, at any scale. This is an exciting time to join. Mixpanel's agentic and AI-first products are driving rapid growth in query volume, and Data Runtime is making the big bets that power it. We’re investing in elastic query compute and a distributed file cache that will let us scale query workloads dramatically without scaling cost with them. We