Systems Research Engineer Jobs

About the Team This team builds and operates the systems that enable OpenAI researchers to run reliable, scalable, and efficient research workflows. The team sits close to research and works across infrastructure, systems, and automation to make sure researchers have the tools and environments they need to move quickly. The work spans software engineering, infrastructure, systems administration, cluster operations, and reliability engineering. As OpenAI’s infrastructure evolves from bespoke bare-metal systems toward more standard, scalable platforms, the team needs engineers who can understand how systems work end-to-end and build the right abstractions without reinventing the wheel. About the Role As a Software Engineer on this team, you will build and operate the infrastructure that supports frontier research and critical research-facing systems. You will work on systems that sit close to the metal, but the role is not limited to classic operations or sysadmin work. We are looking for someone who can reason about networking, bootstrapping, Kubernetes, scalability, automation, and reliability - while also writing software to make these systems better over time. This role is a strong fit for an independent, high-ownership engineer who enjoys reliability-heavy infrastructure work but still wants to build. You do not need to come in as a kernel expert or highly algorithmic optimization engineer, but you should be deeply curious about infrastructure, comfortable debugging complex systems, and excited to support researchers doing novel work. We expect you to: Build and operate reliable infrastructure for research workloads and research-facing services. Support and improve systems across data infrastructure, processing, crawl and ingest, caching, search, observability, and clusterwide services. Improve cluster bootstrapping, provisioning, automation, and deployment workflows. Debug issues across networking, compute, storage, orchestration, and service reliability layers.

We anticipate the application window for this opening will close on - 10 Oct 2026 Careers that change lives start here. Medtronic is a global leader in healthcare technology with a Mission to alleviate pain, restore health, and extend life. Our 95,000 employees work across more than 150 countries to put patients first — developing innovative medical technologies that improve the lives of 72+ million patients each year. Your unique talents will help shape the future of healthcare while building a career grounded in purpose, growth, and impact. A Day in the Life The Neuromodulation Research & Development organization develops therapies and technologies that address chronic pain and other neurological conditions. Within the Interventional Pain portfolio, teams focus on minimally invasive therapies used to treat musculoskeletal pathologies, including vertebral compression fractures, nerve pain, metastatic bone tumors, and benign bone tumors. This role supports the development of system-level verification and validation strategies for new products and enhancements across mechanical, electrical, and software components. This position is based in Santa Clara, California, and follows an onsite work model. No travel is required for this role. As a Research & Development Test Engineer, you will lead and execute verification and validation activities that support the development of interventional pain therapies and associated medical device systems. You will collaborate with cross-functional partners to define test strategies, evaluate product performance, manage technical risks, and generate evidence needed to support product development and regulatory requirements. Primary Responsibiliti

Staff Software Engineer - AI Research Infrastructure P-1215 At Databricks, we are obsessed with enabling data teams to solve the world’s toughest problems, from security threat detection to cancer drug development. We do this by building and running the world’s best data and AI platform so our customers can focus on the high-value challenges that are central to their own missions. The Databricks AI Research organization enables companies to develop AI models and agents using their own data, with technologies ranging from post-training open source LLMs to developing advanced multi-agent architectures. Databricks AI does so by producing novel science and putting it into production. Databricks AI is committed to the belief that a company’s AI models and agents are just as valuable as any other core IP, and that high-quality AI should be available to all. Job Description As a Staff Software Engineer, AI Research Infrastructure, you will be developing and running the research stack that powers Databricks AI Research. You will design and build services that schedule, orchestrate, and observe large‑scale training and inference experiment workloads across thousands of GPUs, improve our dev tooling and ensure that researchers can iterate quickly without sacrificing reliability, efficiency, or security. You’ll partner closely with research scientists, ML engineers, and platform teams to turn experimental workloads into robust, repeatable pipelines, and to push the limits of what our infrastructure can support. The Impact you will have As a Staff Software Engineer on the AI Research Infra Team at Databricks, you will: Design and implement infrastructure that supports large‑scale experiments, data processing, and model training (e.g., HPC clusters, GPU fleets, or cloud‑based systems) Enable researchers to go from idea to large‑scale experiment in minutes, not days, by building powerful abstractions for job submission, scheduling, and monitoring. Create tooling that improv

Staff Software Engineer - AI Research Infrastructure P-1215 At Databricks, we are obsessed with enabling data teams to solve the world’s toughest problems, from security threat detection to cancer drug development. We do this by building and running the world’s best data and AI platform so our customers can focus on the high-value challenges that are central to their own missions. The Databricks AI Research organization enables companies to develop AI models and agents using their own data, with technologies ranging from post-training open source LLMs to developing advanced multi-agent architectures. Databricks AI does so by producing novel science and putting it into production. Databricks AI is committed to the belief that a company’s AI models and agents are just as valuable as any other core IP, and that high-quality AI should be available to all. Job Description As a Staff Software Engineer, AI Research Infrastructure, you will be developing and running the research stack that powers Databricks AI Research. You will design and build services that schedule, orchestrate, and observe large‑scale training and inference experiment workloads across thousands of GPUs, improve our dev tooling and ensure that researchers can iterate quickly without sacrificing reliability, efficiency, or security. You’ll partner closely with research scientists, ML engineers, and platform teams to turn experimental workloads into robust, repeatable pipelines, and to push the limits of what our infrastructure can support. The Impact you will have As a Staff Software Engineer on the AI Research Infra Team at Databricks, you will: Design and implement infrastructure that supports large‑scale experiments, data processing, and model training (e.g., HPC clusters, GPU fleets, or cloud‑based systems) Enable researchers to go from idea to large‑scale experiment in minutes, not days, by building powerful abstractions for job submission, scheduling, and monitoring. Create tooling that improv

About the Team The Platform Systems team at OpenAI operates at the intersection of cutting-edge AI and large-scale distributed systems. We build the engineering and research infrastructure required to train OpenAI’s flagship models on some of the world’s largest, custom-built supercomputers. Our team develops core model training software and works deep in the stack - spanning collective communication, compute efficiency, parallelism strategies, fault tolerance, failure detection, and observability. The systems we build are foundational to OpenAI’s research velocity, enabling reliable, efficient training at frontier scale. We collaborate closely with researchers across the organization, continuously incorporating learnings from across OpenAI into the evolution of our training platform. About the Role As a Software Engineer, Platform Systems, you will design and build distributed systems that provide visibility into large-scale training workloads and help operate them reliably at scale. You’ll work on failure detection, tracing, and observability systems that identify slow or faulty nodes, surface performance bottlenecks, and help engineers understand and optimize massive distributed training jobs. This infrastructure is critical to operating OpenAI’s training stack and is actively evolving to support new use cases and increasingly complex workloads. This role sits at the core of our training infrastructure, blending systems engineering, performance analysis, and large-scale debugging. In This Role, You Will Design and build distributed failure detection, tracing, and profiling systems for large-scale AI training jobs Develop tooling to identify slow, faulty, or misbehaving nodes and provide actionable visibility into system behavior Improve observability, reliability, and performance across OpenAI’s training platform Debug and resolve issues in complex, high-throughput distributed systems Collaborate with systems, infrastructure, and research teams to evolve platform

About the Team The Platform Systems team at OpenAI operates at the intersection of cutting-edge AI and large-scale distributed systems. We build the engineering and research infrastructure required to train OpenAI’s flagship models on some of the world’s largest, custom-built supercomputers. Our team develops core model training software and works deep in the stack - spanning collective communication, compute efficiency, parallelism strategies, fault tolerance, failure detection, and observability. The systems we build are foundational to OpenAI’s research velocity, enabling reliable, efficient training at frontier scale. We collaborate closely with researchers across the organization, continuously incorporating learnings from across OpenAI into the evolution of our training platform. About the Role As a Software Engineer, Platform Systems, you will design and build distributed systems that provide visibility into large-scale training workloads and help operate them reliably at scale. You’ll work on failure detection, tracing, and observability systems that identify slow or faulty nodes, surface performance bottlenecks, and help engineers understand and optimize massive distributed training jobs. This infrastructure is critical to operating OpenAI’s training stack and is actively evolving to support new use cases and increasingly complex workloads. This role sits at the core of our training infrastructure, blending systems engineering, performance analysis, and large-scale debugging. In This Role, You Will Design and build distributed failure detection, tracing, and profiling systems for large-scale AI training jobs Develop tooling to identify slow, faulty, or misbehaving nodes and provide actionable visibility into system behavior Improve observability, reliability, and performance across OpenAI’s training platform Debug and resolve issues in complex, high-throughput distributed systems Collaborate with systems, infrastructure, and research teams to evolve platform

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 . We're looking for a highly technical Senior Engineering Manager with a deep understanding of modern recommendation systems to manage, lead and develop a team of ML researchers and engineers within the Applied Science team. In this role, you will lead the strategy of the Recommendation System innovation team. You will help the team build a balanced portfolio of work that addresses both immediate short-term business impact and long-term strategic breakthroughs in areas like Generative Recommendation System (GenRecSys), Large Sequential Models, and Reinforcement Learning (RL). Partnering closely with executive leadership, product management, and engineering partners, you will evolve our technical roadmap and directly drive Pinterest’s core mission forward. What you will do: Technical Vision and Execution: Own the tech

Scale AI accelerates the development of AI systems by providing the data, infrastructure, and tooling that power the most advanced models in the world. Our teams operate at the intersection of cutting-edge research, large-scale engineering, and real-world deployment, partnering with leading frontier labs, enterprises, and government agencies to push Generative AI into new capabilities and applications. As AI rapidly evolves from static models to dynamic, agentic systems, Scale is building the foundational research, evaluation methodologies, and agent/RL infrastructure that will define this next era. You’ll join a high-impact research organization driving advances in large-language models, post-training, evaluation, and agentic/RL environments, helping shape how next-generation AI is built, measured, and deployed. As a Research Scientist Manager, you will lead a world-class team of research scientists and engineers, define the research roadmap, and drive execution from early prototyping to deployment. You’ll thrive in a fast-moving environment, balancing deep technical leadership with people management, vision setting, and delivery. You will: Lead, mentor and grow a team of research scientists and engineers working on GenAI research initiatives (e.g., evaluation, post-training, agents, RL environments). Define and drive a multi-year research roadmap: identify key scientific questions, set milestones, allocate resources, and ensure rigorous execution. Collaborate cross-functionally with engineering, product, client-facing teams and external academic or industry partners to translate research into components, insights, and actionable outcomes. Communicate compellingly: publish research, present at conferences, engage in open-source contributions, and represent the team externally. Drive an inclusive, high-performing culture: help your team through technical challenges, provide growth opportunities, and attract top talent. Stay deeply connected to the resear

About the Team Our Cyber team builds AI systems and products that help trusted defenders understand and respond to cyber threats while improving the safety and reliability of frontier models in security-sensitive settings. The team works across product engineering, model training, evaluations, safeguards, and deployment to make advanced cyber capabilities useful to defenders and responsibly managed. We collaborate closely with Safety/Preparedness, Research, Security, Legal, Communications, GTM, and external partners across OpenAI’s broader cyber work. About the Role We’re looking for research and software engineers to join Codex Cyber. You’ll help define and ship security products, work with trusted defenders and customers, shape model training and access patterns, and build research and evaluation systems for assessing cyber capabilities, validating safeguards, and improving training data. This role is hands-on and cross-functional, connecting product launches, model development, safety work, and real-world security use cases. In this role, you will: Help define and execute the technical roadmap for Codex Cyber’s security products, including evaluations, safeguards, trusted-defender workflows, and deployment decisions. Work with trusted defenders, customers, and partner teams to understand cyber use cases, evaluate risk, and turn feedback into product and research priorities. Shape cyber-specific model training and access patterns, including data, evaluations, validation, and deployment criteria. Build and validate systems for measuring cyber capabilities, monitoring misuse risk, and proving safeguards work in practice. Collaborate with Safety/Preparedness, Research, Security, Legal, Communications, Go-to-Market, and external partners on company-wide cyber priorities. Translate frontier cyber research into launch-ready tools, operational playbooks, and durable infrastructure for Codex and security products. You might thrive in this role if you: Enjoy 0 -> 1 envi

About Snorkel Snorkel AI is the frontier AI data lab, helping teams build the data and environments behind high-performing frontier and agentic AI. We combine technology with research-driven AI data development to create datasets, benchmarks, evals, and custom solutions for real-world AI systems. Founded out of the Stanford AI Lab in 2019, Snorkel works with leading AI labs and enterprises to move from better data to better outcomes. Excited to help us redefine how AI is built? Apply to be the newest Snorkeler! About The Team Snorkel's AI Platform organization builds the infrastructure and systems that power AI development at scale - synthetic data generation, evaluation, agentic workflows, simulation environments, LLM infrastructure, and distributed compute. Our platform enables engineering and research teams to rapidly experiment with models and agents, measure their behavior, and turn successful experiments into reliable production systems. We're a small team operating at the intersection of distributed systems and applied AI, and we're in the middle of a foundational shift toward agent-first workflows where models interact with tools, environments, data, and other agents over long-running trajectories. The systems we build need to make these inherently non-deterministic workloads observable, reproducible, measurable, and scalable. You will help define how we do that. About The Role We're looking for Software Engineers at Senior / Staff levels who combine strong software and distributed systems fundamentals with experience operating AI systems in production. You'll build the infrastructure that lets teams create, experiment with, evaluate, and operate LLM and agentic workloads at significant scale - from synthetic data and evaluation pipelines to simulation environments, orchestration systems, and LLM infrastructure. You'll work on systems where correctness is not defined by a single deterministic output. Instead, you'll build the infrastructure neede

Who we are About Stripe Stripe is a financial infrastructure platform for businesses. Millions of companies - from the world’s largest enterprises to the most ambitious startups - use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone's reach while doing the most important work of your career. About the team The Abuse Research team is dedicated to proactively hunting for emerging abuse vectors and studying complex attacker behaviors. Rather than just reacting to alerts, the team maps complete abuse paths across Stripe products and external systems to validate novel findings and explain the underlying product conditions that enable fraud. By building continuous abuse tests with agentic testing and related systems, they translate their deep research into actionable threat advisories, strategic control recommendations, and regression scenarios that fortify Stripe’s defenses. What you’ll do You will lead the Abuse Research Group (ARG)—a team of threat intelligence analysts, fraud researchers, and detection engineers focused on proactively identifying and mitigating threats to Stripe and our merchants. You will set the research agenda, guiding work across threat actor tracking, hands-on fraud investigations, merchant ecosystem defense, and the detection pipelines that turn findings into production enforcement. You will scale the team through hiring, coaching, and talent development, while serving as a technical advisor on complex investigations. You will also own ARG's relationships with key partners—including Risk, Trust & Safety, Fraud Platform, and Security Engineering. Lead, develop, and retain a team of threat intelligence analysts, fraud researchers, and detection engineers who thrive at the intersection of adversarial research an

About the Team Our Inference team brings OpenAI’s most capable research and technology to the world through our products. We empower consumers, enterprise and developers alike to use and access our start-of-the-art AI models, allowing them to do things that they’ve never been able to before. We focus on performant and efficient model inference, as well as accelerating research progression via model inference. About the Role We are looking for an engineer who wants to take the world's largest and most capable AI models and optimize them for use in a high-volume, low-latency, and high-availability production and research environment. In this role, you will: Work alongside machine learning researchers, engineers, and product managers to bring our latest technologies into production. Work alongside researchers to enable advanced research through awesome engineering. Introduce new techniques, tools, and architecture that improve the performance, latency, throughput, and efficiency of our model inference stack. Build tools to give us visibility into our bottlenecks and sources of instability and then design and implement solutions to address the highest priority issues. Optimize our code and fleet of Azure VMs to utilize every FLOP and every GB of GPU RAM of our hardware. You might thrive in this role if you: Have an understanding of modern ML architectures and an intuition for how to optimize their performance, particularly for inference. Own problems end-to-end, and are willing to pick up whatever knowledge you're missing to get the job done. Have at least 5 years of professional software engineering experience. Have or can quickly gain familiarity with PyTorch, NVidia GPUs and the software stacks that optimize them (e.g. NCCL, CUDA), as well as HPC technologies such as InfiniBand, MPI, NVLink, etc. Have experience architecting, building, observing, and debugging production distributed systems. Bonus point if worked on performance-critical distributed systems. Have need

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role Anthropic trains and serves Claude across multiple cloud providers and accelerator platforms. The Platform Portability team owns the abstractions, tooling, and deployment infrastructure that make this possible without every engineering and research team needing to become experts in different clouds. As an engineer on Platform Portability, you'll design and build the systems that let us bring up new capacity, ship our platform to cloud provider platforms & marketplaces, and move workloads between providers with confidence. You'll work closely with our cloud partners on capabilities we need that don't exist yet, and with internal Compute, Inference, and Security teams to make "which cloud is this running on?" a question most engineers at Anthropic never have to ask. Your work directly determines how quickly and reliably we can put safe, frontier AI in front of users — wherever they are. What you'll do Our work splits into two workstreams, and we need your help across both. Portable infrastructure. We build the provider-agnostic abstractions for storage, messaging, and connectivity that let Anthropic's systems run on any cloud, and they have to hold up at the scale of frontier model inference. You'll design and build those abstractions, plus the tooling that lets internal teams provision, configure, and operate services across clouds through one consistent interface. You'll also hunt down the provider-specific assumptions hiding in our codebase and set the patterns that keep new ones from creeping in. Claude on partner clouds. We ship Claude onto third-party cloud platforms, and every launch has to be repeatable. You'l

Sendbird is building AI agents for customer experience. Our platform already powers billions of conversations every month across chat, voice, video, and messaging APIs. We are now using that foundation to build agents that understand customer context, reason over business data, and take reliable action in production. We are looking for a Machine Learning Engineer to research, build, and productionize new capabilities for those agents. This role sits at the intersection of agent product development, applied AI research, and production engineering. You will work on systems that enterprise customers depend on every day, not demos or isolated prototypes. About Sendbird and delight.ai Sendbird has spent more than a decade building communication infrastructure for in-app chat, voice, video, and messaging APIs. More than 4,000 brands use our platform, including DoorDash, Match Group, Noom, Yahoo Sports, and Rakuten. Our systems support more than 7 billion messages every month. In 2024, we made a strategic shift toward AI-first customer experience. In 2025, we launched our enterprise AI agent product, delight.ai. Delight.ai helps businesses deliver customer support and engagement that is faster, more contextual, and more personal. Unlike simple FAQ bots, our agents are built to remember customer context, use tools, retrieve relevant knowledge, connect across channels, and handle real customer workflows with accuracy and control. The Role As a Machine Learning Engineer, you will design, build, evaluate, and ship new capabilities for our AI agents. You will work across agent architecture, retrieval, memory, planning, tool use, workflow automation, voice, evaluation, data pipelines, model adaptation, inference, and production integration. This is a hands-on engineering role for someone who can turn AI research and product ideas into reliable customer-facing features. Some problems will require training, fine-tuning, or adapting models. Others will require better retrieval, bet

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About Anthropic Anthropic's mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About Beneficial Deployments Beneficial Deployments ensures AI reaches and benefits the communities that need it most. We partner with nonprofits, foundations, and mission-driven organizations to deploy Claude in education, global health, economic mobility, and life sciences — focusing on raising the floor for those who need it most. About the Role We're looking for an Applied AI Engineer to join our Beneficial Deployments, Life Sciences team. Our goal is for Claude to close the loop on scientific discovery, and a significant part of this is helping Claude to drive scientific hardware and software. You'll help deploy the Model Hardware Standard (MHS) to labs doing the world’s most important research. You’ll scale what you learn in the field by contributing back to the standard, building scalable assets, and driving associated internal product and research direction. Responsibilities Work with labs to automate their experiments end-to-end with Claude - from advising, to prototyping, to writing production code. Drive product, engineering and research direction based on field experience - e.g. building evals to measure limitations of our models in the physical world (capabilities and safety), developing new features on MHS, etc. Scale your impact to the long tail of labs through reusable cookbooks,