Ai Runtime Engineer Jobs

As a Senior Security Engineer within Application Security at Datadog, you will help secure emerging application architectures where established security patterns may not yet exist, including agentic applications, LLM-enabled products, protocol-driven tool use, and untrusted execution environments. You will establish and validate security models, requirements, and controls while partnering with product and platform teams to develop secure-by-default approaches. Your work will address evolving trust boundaries, agent identity, data access, tool permissions, and runtime behavior through threat modeling, security solutions, and practical implementation. You will have the opportunity to shape how Datadog safely supports new application paradigms and address emerging security risks before they become systemic. At Datadog, we place value in our office culture - the relationships and collaboration it builds and the creativity it brings to the table. We operate as a hybrid workplace to ensure our Datadogs can create a work-life harmony that best fits them. What You’ll Do: Define security models, requirements, and guardrails for emerging application architectures where established patterns are incomplete or do not yet exist. Design, implement, and validate practical security controls that reduce risk in emerging architectures and high-risk system behaviors. Shape how emerging systems authenticate, authorize actions, isolate workloads, and access data, applying least-privilege principles throughout. Partner with product, platform, Infrastructure Security, and other engineering teams to identify security gaps and embed secure-by-default requirements from early design through production. Lead threat modeling and security assessments for new and existing products, focusing on evolving threats, unfamiliar trust boundaries, and new interaction patterns. Prioritize and drive remediation of emerging security risks, including continuous security testing where it provides durable cover

At Snowflake, we are powering the era of the agentic enterprise. To usher in this new era, we seek AI-native thinkers across every function who are energized by the opportunity to reinvent how they work. You don’t just use tools; you possess an innate curiosity, treating AI as a high-trust collaborator that is core to how you solve problems and accelerate your impact. We look for low-ego individuals who thrive in dynamic and fast-moving environments and move with an experimental mindset — who rapidly test emerging capabilities to discover simpler, more powerful ways to deliver results. At Snowflake, your role isn't just to execute a function, but to help redefine the future of how work gets done. We are looking for talented systems developers and researchers to join the Snowflake AI Research team and advance the state of the art in LLM inference systems and optimization . Our mission is to build the next generation of high-performance and intelligent inference systems. We optimize not only how fast and efficiently models run, but also how quickly inference systems can adapt to new models, architectures, hardware, and workloads. Our work spans the full inference stack—from distributed serving and runtime systems to GPU kernels and model-system co-design. We explore techniques such as adaptive parallelism, speculative and parallel decoding, disaggregated inference, scheduling and batching, KV-cache optimization, model swapping, quantization, and GPU kernel optimization to push the frontier of latency, throughput, scalability, and cost. Beyond optimizing individual models, we are building intelligent and adaptive inference systems that can automate performance optimization—rapidly profiling new models and workloads, identifying bottlenecks, selecting effective execution strategies, and adapting system configurations with minimal manual tuning. We embrace AI-native engineering , using AI not only as the workload we optimize, but also as a tool to accelerate system devel

Join us in building the future of finance. Our mission is to democratize finance for all. An estimated $124 trillion of assets will be inherited by younger generations in the next two decades. The largest transfer of wealth in human history. If you’re ready to be at the epicenter of this historic cultural and financial shift, keep reading. About the team + role We are building an elite team, applying frontier technologies to the world's biggest financial problems. We're looking for bold thinkers. Sharp problem-solvers. Builders who are wired to make an impact. Robinhood isn't a place for complacency, it's where ambitious people do the best work of their careers. We're a high-performing, fast-moving team with ethics at the center of everything we do. Expectations are high, and so are the rewards. Robinhood's Security Engineering org is a builder-first security engineering team — engineers organized across Cloud Security, Infrastructure Security, Security Platform, and Detection & Response. Unlike traditional security teams that identify risk and hand it off, this org builds the systems that solve it. We own the critical authentication and identity infrastructure that protects every Robinhood user, the cryptographic libraries and access provisioning systems that keep internal operations secure, and the telemetry and detection platforms that give our security operations team the signal they need to move fast. If it secures how Robinhood runs — we built it. AI is reshaping what this team does and how it operates across two dimensions. First, we are embedding security directly into Robinhood's growing AI and agentic infrastructure — building the runtime authentication, credential management, and validation systems that make it possible to run AI safely in production at scale. Second, we are using AI to fundamentally multiply the capability of the security program itself — building intelligent internal platforms that surface vulnerabilities, accelerate detection

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 work on the systems software strategy and execution that brings new AI silicon from first power-on to a fully integrated system running production-representative models at expected functionality and performance. You will define how software exercises and validates compute, memory, interconnect, and I/O subsystems, then build the diagnostics, automation, and observability needed to find issues quickly. This role sits at the center of silicon, firmware, platform, systems, and workload teams. You will turn hardware specifications and performance targets into an end-to-end bringup plan, drive cross-functional debug, and establish the stress and regression infrastructure that makes each new platform reliable across operating environments. In this role, you will: Contribute to the end-to-end software bringup and validation strategy for new silicon and first-party systems. Define software-driven test coverage across compute, memory, interconnect, I/O, and their system-level interactions. Build diagnostics, test automation, telemetry, and regression infrastructure that accelerate first-silicon learning and issue isolation. Lead bringup from initial silicon arrival through board and system integration, docking, runtime enablement, and model execution. Design stress tests that characterize reliability, performance, and stability across workloads and operating conditions. Translate architecture specifications and performance models into measurable acceptance crit

By submitting your resume, you’re expressing interest in our 2027 RDSS (Research and Development Substitute Services) program. Please confirm your eligibility with the local district office before applying the role. NVIDIA's invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, we are increasingly known as “the AI computing company”. We are looking to grow our company, and grow our teams with the smartest people in the world. What you’ll be doing: You will work with ground breaking technologies for the Tegra SoC and various NVIDIA embedded platforms Implement power and thermal management software features in Linux Kernel and user space Collaborate with power architects, hardware and software engineers on usecase power estimation, power and performance optimization What we need to see: MS in CS, CE, EE, Systems Engineering or related software/hardware engineering major Software development experience with a significant focus on Linux Excellent C programming/debugging skills within Linux kernel and user space software Background with working on embedded systems and ARM processor specific System-level debugging experience and problem-solving skills Excellent communication skills Ways to stand out from the crowd: Experience in working with the Linux and open-source software communities Understanding of the Linux power management features (scheduler, dynamic frequency scaling, runtime power management, su

About the Team The Spark Platform team owns and operates DoorDash's Apache Spark ecosystem — the execution runtime, remote shuffle service, cluster scheduler, and reliability tooling that powers the company's data, analytics, and ML workloads. We run Spark across the company at significant scale and continue to expand the workloads, capabilities, and consumer base we serve. Orchestrating and operating thousands of Spark cluster deployments is a complex distributed system problem which the team invests heavily in runtime optimization, systems architecture, multi-tenant scheduling, and end-user tooling. About the Role As a Senior Software Engineer on Spark Platform, you will set the technical direction for our in-house Spark deployment and shape the architecture that will run DoorDash's data, analytics, and ML compute for the next five years and beyond. You will own the deep, cross-cutting problems that span the runtime, the shuffle service, the scheduler, and the overall service reliability — making the architectural calls that compound across the platform's lifetime. You will partner with the Engineering Manager on technical roadmap, hiring, and team shape, and act as the senior technical voice in cross-team partnerships with Data Engineering, ML Platform, and product engineering teams that depend on the platform. You must be located in San Francisco, Sunnyvale, Seattle, or New York City for this hybrid position. You will report into the Engineering Manager on our Spark Platform team. You're excited about this opportunity because you will… Set the multi-year technical direction for an in-house Spark-on-Kubernetes platform — runtime, shuffle, scheduler, reliability — and make the architectural calls that compound for years. Own the deepest distributed-systems problems on the team: shuffle architecture, multi-tenant scheduling, runtime performance, and the failure modes that only show up at scale. Partner with the Engineering Manager on technical roadmap, hiring, inte

Who We Are Nuro believes self-driving vehicles are the most immediate and profound opportunity for AI to drive positive change in the physical world. Safer streets, more time for what matters, and easier access to the world around us, that’s why we’re building a universal autonomy platform: self-driving for all roads and all rides. Founded in 2016, Nuro is a physical AI company developing Level 4 autonomous driving technology for a wide range of vehicles, use cases, and markets. Powered by the Nuro Driver™, our universal autonomy platform enables the global mobility ecosystem to deploy autonomy at scale, from robotaxis and logistics fleets to personal vehicles. With years of real-world deployment experience and a flexible, partner-led business model, Nuro is working toward a future where millions of autonomous vehicles powered by our technology help make everyday life safer, easier, and more connected. Nuro has raised over $2B in capital from Uber, NVIDIA, Google, Softbank, Fidelity, T. Rowe Price, and other leading investors. About the Role We’re a team applying ML to push autonomy performance forward. As a Perception ML Engineer, you’ll develop and improve perception models, with ownership of modeling, training, and evaluation. Your work will directly impact how autonomous systems understand rare scenarios, adapt to global geographies, and scale safely. Design and advance perception models that: Improve scene understanding from real sensor data, including 3D and spatial representations Generalize across sensor platforms, geographies, and rare driving scenarios Meet requirements for model quality and onboard inference runtime Tailor Your Impact: Applied ML Depth: Own model architecture, loss design, training, and experiments that deliver measurable perception improvements. Collaborative Delivery: Partner with ML Data Infrastructure and autonomy engineers, contributing across the stack when needed while keeping primary ownership in modeling. About the work You’ll so

ABOUT BASETEN Baseten powers mission-critical inference for the world's most dynamic AI companies, like Cursor, Notion, OpenEvidence, Abridge, Clay, Gamma, and Writer. By uniting applied AI research, flexible infrastructure, and seamless developer tooling, we enable companies operating at the frontier of AI to bring cutting-edge models into production. We're growing quickly and recently raised our $1.5B Series F , led by Altimeter Capital, Conviction Partners, and Spark Capital. Join us and help build the platform engineers turn to ship AI products. THE ROLE We're looking for inference performance engineers who want to make the world's most demanding AI workloads run faster and more efficiently. You'll work across the stack, from the inference engine and runtime through scheduling, serving, and routing. Along the way you'll apply techniques like prefill/decode disaggregation, speculative decoding, and KV-cache management. You'll reason from first principles about where time and memory go, find what's holding performance back, and close the gap. Your work directly impacts how fast our customers' models run and how efficiently we serve them. This role is ideal for someone who thrives in a fast-paced startup environment and is eager to make significant contributions to the exciting field of LLM inference. EXAMPLE INITIATIVES You'll get to work on these types of projects as an Inference Performance engineer: Agentic Kernels in Production How we built the new fastest API for GLM-5.2 Live draft model training for speculative decoding Making Kimi K3 Tokenization 18x faster The Baseten Inference Stack Driving model performance optimization RESPONSIBILITIES Implement and productionize cutting-edge inference techniques, working deep in runtime internals. That includes quantization, speculative decoding, KV-cache reuse, chunked prefill, LoRA, guided generation for structured outputs, and custom scheduling and routing algorithms. Profile and optimize inference end to end, from

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 define and build the CI/CD systems that qualify frontier models and the software stack running on OpenAI’s custom AI accelerators. Your work will make correctness, performance, and stability measurable on every change, from individual commits through production releases. This role spans model workloads, accelerator software, systems infrastructure, and developer productivity. You will establish scalable CI/CD, GitOps, and monorepo practices; design trustworthy regression and benchmarking pipelines; and partner with model, compiler, kernel, runtime, firmware, and hardware teams to shorten feedback loops without compromising production readiness. In this role, you will: Define the model CI strategy for frontier models running on OpenAI custom silicon and integrated accelerator systems. Build production CI/CD pipelines for functional regression, performance benchmarking, software stability, and release qualification. Design representative test matrices across models, configurations, hardware generations, software components, and deployment environments. Create reliable performance baselines, regression detection, bisect and triage workflows, and clear ownership for failures. Establish GitOps practices for reproducible configuration, promotion, rollback, auditability, and environment consistency. Shape monorepo architecture, dependency management, build and test boundaries, change validation, and developer workflows at scale. Develop scalable orchestration

About Pinterest: Millions of people around the world come to our platform to find creative ideas, dream about new possibilities and plan for memories that will last a lifetime. At Pinterest, we’re on a mission to bring everyone the inspiration to create a life they love, and that starts with the people behind the product. Discover a career where you ignite innovation for millions, transform passion into growth opportunities, celebrate each other’s unique experiences and embrace the flexibility to do your best work. Creating a career you love? It’s Possible. At Pinterest, AI isn't just a feature, it's a powerful partner that augments our creativity and amplifies our impact, and we’re looking for candidates who are excited to be a part of that. To get a complete picture of your experience and abilities, we’ll explore your foundational skills and how you collaborate with AI. Through our interview process, what matters most is that you can always explain your approach, showing us not just what you know, but how you think. You can read more about our AI interview philosophy and how we use AI in our recruiting process here . People use Pinterest to find ideas and brands that they love. We aspire to help our advertisers and partners reach their audiences with inspiring content. Pinterest is looking for an Engineering Manager to lead our iOS Modernization team in Toronto. You’ll lead the engineering team that is empowering iOS developers at Pinterest to build modern, world-class app experiences for Pinners and advertisers with high productivity, performance, and quality. What you'll do: Lead, coach and grow a specialized team of iOS engineers. Set the team’s technical vision and roadmap in partnership with Client Platforms and cross-functional product teams. Drive platform modernization that helps product teams adopt modern client architectures and build high-quality experiences. Improve client stability, health metrics, browser compatibility, runtime frameworks and c

About Pinterest: Millions of people around the world come to our platform to find creative ideas, dream about new possibilities and plan for memories that will last a lifetime. At Pinterest, we’re on a mission to bring everyone the inspiration to create a life they love, and that starts with the people behind the product. Discover a career where you ignite innovation for millions, transform passion into growth opportunities, celebrate each other’s unique experiences and embrace the flexibility to do your best work. Creating a career you love? It’s Possible. At Pinterest, AI isn't just a feature, it's a powerful partner that augments our creativity and amplifies our impact, and we’re looking for candidates who are excited to be a part of that. To get a complete picture of your experience and abilities, we’ll explore your foundational skills and how you collaborate with AI. Through our interview process, what matters most is that you can always explain your approach, showing us not just what you know, but how you think. You can read more about our AI interview philosophy and how we use AI in our recruiting process here . People use Pinterest to find ideas and brands that they love. We aspire to help our advertisers and partners reach their audiences with inspiring content. Pinterest is looking for an Engineering Manager to lead our Android Modernization team in Toronto. You’ll lead the engineering team that is empowering Android developers at Pinterest to build modern, world-class app experiences for Pinners and advertisers with high productivity, performance, and quality. What you'll do: Lead, coach and grow a specialized team of Android engineers. Set the team’s technical vision and roadmap in partnership with Client Platforms and cross-functional product teams. Drive platform modernization that helps product teams adopt modern client architectures and build high-quality experiences. Improve client stability, health metrics, browser compatibility, runtime fram

Who We Are Nuro believes self-driving vehicles are the most immediate and profound opportunity for AI to drive positive change in the physical world. Safer streets, more time for what matters, and easier access to the world around us, that’s why we’re building a universal autonomy platform: self-driving for all roads and all rides. Founded in 2016, Nuro is a physical AI company developing Level 4 autonomous driving technology for a wide range of vehicles, use cases, and markets. Powered by the Nuro Driver™, our universal autonomy platform enables the global mobility ecosystem to deploy autonomy at scale, from robotaxis and logistics fleets to personal vehicles. With years of real-world deployment experience and a flexible, partner-led business model, Nuro is working toward a future where millions of autonomous vehicles powered by our technology help make everyday life safer, easier, and more connected. Nuro has raised over $2B in capital from Uber, NVIDIA, Google, Softbank, Fidelity, T. Rowe Price, and other leading investors. About the Team The Devices Platform team's mandate is to lay the foundation of Nuro's onboard software for our sensor and compute platform, including device drivers, inter-device protocols and pipelines, and device runtime APIs. Sensors and compute hardware are the eyes, ears, and brains of our self-driving robots. We are creating the hardware-agnostic platform to be used by the perception and autonomy SW stack, and to realize the full potential of our sensor and compute HW in reliability, quality, and performance. The projects we work on are high impact and high visibility within Nuro. This team is also responsible for working with internal stakeholders and external suppliers to define, evaluate, integrate the next generation HW platform for Nuro's products and to build the necessary tooling to assist continuous testing and validation. About the Work Design and develop sensor and compute systems for robotics Architect and/or deploy Nuro

About Anyscale: At Anyscale , we're on a mission to democratize distributed computing and make it accessible to software developers of all skill levels. We're commercializing Ray , a popular open-source project that's creating an ecosystem of libraries for scalable machine learning. Companies like OpenAI , Uber , Spotify , Instacart , Cruise , and many more, have Ray in their tech stacks to accelerate the progress of AI applications out into the real world. With Anyscale, we're building the best place to run Ray, so that any developer or data scientist can scale an ML application from their laptop to the cluster without needing to be a distributed systems expert. Proud to be backed by Andreessen Horowitz, NEA, and Addition with $250+ million raised to date. About the role Anyscale is looking for a Software Engineer to join the ML Developer Experience (MLDevX) team. MLDevX owns the experience layer of the Anyscale platform: the interfaces through which users and coding agents discover, configure, run, observe, debug, and productionize AI workloads. Every user journey crosses this layer through the CLI, SDKs, APIs, UI, Workspaces, MCP, or the workflows and integrations built on top of them. Together, these form the user’s primary interface into Anyscale, turning distributed computing from a systems problem back into a coding problem. We build the common contracts, tools, control-plane services, and architecture that power these surfaces. You will work across the stack from developer tooling to cloud infrastructure and the Ray runtime. Manage long-running operations and make failures across jobs, tasks, actors, nodes, and GPUs easier to diagnose. The systems you build must scale with the platform, remain predictable through failures, and be intuitive for developers, programmable for applications, and operable by coding agents. This is a high impact individual-contributor role with end-to-end ownership. You will work directly with users and field teams to identify high

At Snowflake, we are powering the era of the agentic enterprise. To usher in this new era, we seek AI-native thinkers across every function who are energized by the opportunity to reinvent how they work. You don’t just use tools; you possess an innate curiosity, treating AI as a high-trust collaborator that is core to how you solve problems and accelerate your impact. We look for low-ego individuals who thrive in dynamic and fast-moving environments and move with an experimental mindset — who rapidly test emerging capabilities to discover simpler, more powerful ways to deliver results. At Snowflake, your role isn't just to execute a function, but to help redefine the future of how work gets done. At Snowflake, we are shaping how data becomes the center piece of the agentic enterprise. Time to value is what’s most critical to our customers. For them, moving from other platforms to Snowflake is key to unlocking new business opportunities and out-innovating their competition. With AIM Virtualization we’re redefining interoperability for the modern data stack. Our platform makes existing applications instantly work on Snowflake by bringing the core tenets of virtualization to the database. With our runtime, customers move to Snowflake without changing SQL or APIs. As a result, AIM Virtualization makes time-consuming migrations obsolete. Our platform is uniquely situated at the intersection of database internals and their practical application. Our team gets to tackle some of the most intricate database implementation challenges. All while pioneering an industry-first framework which customers consistently describe as “too good to be true”. If you’re looking to work on interesting problems that truly matter to our customers while advancing how we can use AI across the organization, this is the team for you. We are looking for AI-first engineers who are not afraid to take on hard and unusual challenges. We are one of Snowflake’s leading teams in AI-driven software synthes

About the Team The Spark Platform team owns and operates DoorDash's Apache Spark ecosystem — the execution runtime, remote shuffle service, cluster scheduler, and reliability tooling that powers the company's data, analytics, and ML workloads. We run Spark across the company at significant scale and continue to expand the workloads, capabilities, and consumer base we serve. Orchestrating and operating thousands of Spark cluster deployments is a complex distributed system problem which the team invests heavily in runtime optimization, systems architecture, multi-tenant scheduling, and end-user tooling. About the Role As a Software Engineer on Spark Platform, you will execute across the surfaces of our in-house Spark deployment that serves the entire company. The work spans Spark runtime upgrades and performance, multi-tenant scheduling and executor bin-packing on Kubernetes, cluster lifecycle automation, and the observability and incident automation that keep the platform sustainable. You will move between layers as the work demands — picking up the next high-leverage problem regardless of where it sits — and partner closely with the rest of the team and with platform consumers across the company. You must be located in San Francisco, Sunnyvale, Seattle, or New York City for this hybrid position. You will report into the Engineering Manager on our Spark Platform team. You're excited about this opportunity because you will… Build and operate an in-house Spark platform that runs at company-wide scale, spanning runtime, scheduler, reliability, and user-facing tooling. Drive multi-tenant scheduling, executor bin-packing, and cost-aware placement that let a small team serve dozens of consumer teams. Own pieces of cluster lifecycle automation — provisioning, upgrades, capacity changes, and node-failure handling — at a scale where these stop being manual events. Build the observability and incident automation that make the platform debuggable end-to-end and keep on-call sus