Systems Research Engineer Jobs

About the team The Frontier Assurance team strengthens OpenAI’s safety decisions through independent expertise, rigorous assessment, and public transparency. We coordinate external evaluations of frontier models and safeguards, develop approaches such as embedded auditing, and manage the misalignment disclosure process and safeguard testing. Our work helps independent experts scrutinize OpenAI’s safety approach and ensures their findings inform consequential decisions about advanced AI systems. About the role As a researcher on Frontier Assurance, you will build the technical foundations for rigorous assessments of frontier AI risks. You will help design technical assessments with external evaluators and internal teams: identify consequential safety claims to test, define the evidence and methods needed to assess them, and address gaps in coverage. You will work closely with external evaluators and embedded auditors, helping them understand the systems they are assessing and access the models, data, and technical information their work requires. Where direct access is not possible, you will conduct experiments and analyses on their behalf, with methods and results they can meaningfully scrutinize. This role combines research engineering, experimental design, and close technical collaboration. You will move between investigating specific safety questions, navigating complex internal systems, and building reusable tools that make assessments more thorough and efficient. Your work will help turn independent scrutiny into concrete evidence about model capabilities, safeguards, and remaining risks. In this role, you will: Help design assessments with external evaluators and internal teams: identify consequential safety claims to test, define the evidence and methods needed to assess them, and address gaps in coverage. Support the technical execution of third-party assessments and embedded audits focused on frontier risks, working with evaluators to translate research que

Who are we? Cohere is the leading security-first enterprise AI company. We build cutting-edge foundation AI models and end-to-end products that are designed to solve real-world business problems. We’re training and deploying frontier models for enterprises who are building AI systems. We believe that our work is instrumental to the widespread adoption of AI and we are looking for folks that want to be part of that. We obsess over what we build. Each one of us is responsible for contributing to increasing the capabilities of our models and the value they drive for our customers. Cohere is a team of researchers, engineers, designers, and more, who are all passionate about their craft. We are a global technology company headquartered in Toronto with key offices in London, New York City, San Francisco, Montreal, Paris, Berlin and Seoul. Join us! Role Overview: We are looking for world-class research scientists and research engineers to build cutting-edge multilingual AI to serve the world! At Cohere, we care deeply about building technologies that are broadly accessible and useful, regardless of language. As a Member of Technical Staff on the Multilingual team, you'll be at the forefront of advancing language models that serve the world. You’ll push the boundaries of what's possible in natural language processing. This isn't just a technical role—it's an opportunity to contribute to groundbreaking research that will define the next generation of AI. Key Responsibilities: Lead the design and implementation of scalable solutions to improve multilingual LLM performance across a wide variety of skills – if we need it to make a great multilingual model, you can build it! Is perfect for someone passionate about languages and AI, with a keen eye for detail and strong technical skills. Offers the opportunity to contribute to cutting-edge language technology, making a global impact. Requires a self-starter who can work independently and deliver results efficiently. Publish resea

Figma is growing our team of passionate creatives and builders on a mission to make design accessible to all. Figma’s platform helps teams bring ideas to life—whether you're brainstorming, creating a prototype, translating designs into code, or iterating with AI. From idea to product, Figma empowers teams to streamline workflows, move faster, and work together in real time from anywhere in the world. If you're excited to shape the future of design and collaboration, join us! The AI Research team at Figma is working on the cutting edge of AI and Design. We work on optimizing agentic systems for design quality and end user experience. We build robust evaluators for Design and post-train models. We are a very collaborative, close-knit and impact-focused team of researchers and research engineers working on some of the most challenging technical problems at the intersection of AI and creativity. We are seeking PhD interns with expertise in machine learning and artificial intelligence to develop innovative AI solutions for design and creative products. Through both fundamental and applied research, your work will contribute to new AI-driven features in Figma products. This internship can take place in Winter 2027 or Summer 2027 in San Francisco or New York. What you’ll do at Figma: Drive fundamental and applied research in Generative AI with Figma product use cases in mind Formulate and implement new modeling approaches both to improve the effectiveness of Figma’s current AI models as well as enable the launch of entirely new AI-powered product features Explore the boundaries of what is possible with the current technology set and experiment with novel ideas Depending on your project, you may have the opportunity to publish your research in leading AI/ML conferences We'd love to hear from you if you have: Are a 4th or 5th year senior Ph.D student in AI, Machine Learning, Computer Vision, Natural Language Processing, Human Computer Interaction or a related field Pro

About the team OpenAI’s mission is to build safe artificial general intelligence (AGI) which benefits all of humanity. This long-term undertaking brings the world’s best scientists, engineers, and business professionals into one lab together to accomplish this. In pursuit of this mission, our Go To Market (GTM) team is responsible for helping customers learn how to leverage and deploy our highly capable AI products across their business. The team is made of Sales, Solutions, Support, Marketing, and Partnership professionals that work together to create valuable solutions that will help bring AI to as many users as possible. About the role As an Account Director focused on Healthcare, you will own executive-level relationships with leading healthcare organizations, including integrated delivery networks (IDNs), health systems, academic medical centers, health insurers, digital health companies, and healthcare technology providers. You’ll help these organizations safely and effectively deploy OpenAI’s technology to improve clinical and administrative workflows, enhance patient and provider experiences, automate operational processes, and accelerate enterprise-wide AI adoption. This role blends enterprise sales expertise, technical depth, business acumen, and relationship-driven selling. You will collaborate closely with researchers, engineers, and healthcare-focused solution strategists to design secure, compliant, and high-impact AI deployments. This role is based in San Francisco. 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’ll: Manage a focused portfolio of large healthcare provider, payer, and healthcare technology accounts, developing long-term strategic account plans. Lead complex, multi-stakeholder sales cycles spanning clinical, operational, IT, digital transformation, and executive leadership teams. Partner with Solutions and Research Engineering to design pilots that demon

About the AI Security Institute The AI Security Institute is the world's largest and best-funded team dedicated to understanding advanced AI risks and translating that knowledge into action. We’re in the heart of the UK government with direct lines to No. 10 (the Prime Minister's office), and we work with frontier developers and governments globally. We’re here because governments are critical for advanced AI going well, and UK AISI is uniquely positioned to mobilise them. With our resources, unique agility and international influence, this is the best place to shape both AI development and government action. Expression of Interest - Core Technology Team Our Core Technology Team is open to expressions of interest from talented software engineers, research engineers, and security engineers at all levels of seniority who want to work at the forefront of frontier AI security and would like to be considered for potential upcoming roles in our Core Technology Team. Our talent team will reach out should a suitable role arise. About the team AISI's Core Technology Team comprises several teams building tools and infrastructure used across all of our research work. This includes projects like Inspect plugins, systems for running evaluations at scale, hosting frontier open-weights models for evaluations, the platform where our researchers do their work, and tooling to design and deploy apps like human studies. As a member of our Core Technology Team, you might: Collaborate with the open-source community on plugins and features for Inspect Implement support for a new class of open-weights model on our model hosting platform Scale our proxy infrastructure for secure model access Support a research team with designing custom infrastructure for a new research project Assist with an evaluation testing exercise of a frontier AI model, making sure the infrastructure and monitoring setup for it is robust and secure If that sounds exciting to you, we'd love for you to apply! Sa

About the AI Security Institute The AI Security Institute is the world's largest and best-funded team dedicated to understanding advanced AI risks and translating that knowledge into action. We’re in the heart of the UK government with direct lines to No. 10 (the Prime Minister's office), and we work with frontier developers and governments globally. We’re here because governments are critical for advanced AI going well, and UK AISI is uniquely positioned to mobilise them. With our resources, unique agility and international influence, this is the best place to shape both AI development and government action. Expression of Interest – Chem Bio AISI’s Chem Bio team is open to expressions of interest from talented research engineers and scientists of all levels of seniority who want to work at the forefront of frontier AI safety and security, but do not see a role listed that matches their skills and experience. Our talent team will reach out should a role arise that may be a good match. Team Description The Chem Bio team conducts cutting-edge research to understand, evaluate and mitigate chemical and biological risks associated with frontier AI systems. We assess the extent to which advanced models may enable harmful capabilities, develop methods for evaluating these risks rigorously, and share our findings directly with leading AI companies, UK officials and allied governments to inform deployment, research and policy decisions. Our work spans a range of technical and scientific challenges, including capability elicitation, model evaluations, benchmarking, threat modelling, and the development of safeguards and monitoring approaches. We combine expertise from machine learning, chemistry, biology, biosecurity and related disciplines to better understand how frontier AI systems may affect the chemical and biological risk landscape. We work closely with partners across government, academia and industry, and collaborate with other technical teams across AISI to ensure ou

We believe that the way people interact with their finances will drastically improve in the next few years. We’re dedicated to empowering this transformation by building the tools and experiences that thousands of developers use to create their own products. Plaid powers the tools millions of people rely on to live a healthier financial life. We work with thousands of companies like Venmo, SoFi, several of the Fortune 500, and many of the largest banks to make it easy for people to connect their financial accounts to the apps and services they want to use. Plaid’s network covers 12,000 financial institutions across the US, Canada, UK and Europe. Founded in 2013, the company is headquartered in San Francisco with offices in New York, Seattle, Washington D.C., Raleigh, London, and Amsterdam. We are the Data Foundation & AI team within Plaid’s Data organization. Our mission is to build the shared ML and AI infrastructure that powers intelligent capabilities across Plaid’s product suite. We develop the foundational systems, models, and data assets that transform Plaid’s unique financial network data into scalable, general-purpose representations that teams across the company can leverage. Our work spans the full ML lifecycle — from large-scale data curation and model pretraining to production serving, evaluation, and monitoring. As part of the team, you’ll work at the intersection of machine learning infrastructure, applied AI, and distributed systems, helping establish the core AI platform that enables innovation across Plaid. As a Staff Machine Learning Engineer, you will lead the technical strategy and development of Plaid’s foundation models, driving key decisions across pretraining objectives, model architecture, and fine-tuning approaches that power a wide range of downstream product applications. You will serve as the technical lead for the full machine learning lifecycle, overseeing everything from data curation and experimentation to production deployment,

Perplexity is seeking a TLM (Tech Lead Manager) to lead and grow our highly driven Agents engineering team. The Agents team consists of AI/ML, backend, and full-stack engineers who collaborate to build delightful agentic experiences within our Comet ecosystem . Our vision is to empower our users with AI agents that can faithfully actualize their intent, however and wherever expressed, through open-ended interactions with the world. As the Agents TLM, you will bring AI expertise, sharp product intuition, and strong engineering management skills to advance the frontier of what agents can accomplish for our millions of devoted users. You will lead, grow, and support a team in solving many open problems in AI, including: Designing AI agents to navigate the digital world and perform increasingly valuable units of work for our users; Training action and decision models that determine, based on complex multimodal states, how to accomplish user-specified objectives; Providing consistently excellent experiences across desktop, mobile, headless cloud, and other environments through flexible abstractions and frictionless backgrounding; Developing permission architectures, payload classifiers, and other methods to implement secure-by-design agentic capabilities; Designing optimal data representations and modes of interaction between agents and their environments; and much, much more. Responsibilities Provide technical leadership across multiple layers of a rapidly growing product in the AI agents space. Develop and leverage cutting-edge AI models, infrastructure, and browser technologies to advance the capability frontier and scale those capabilities for a rapidly growing userbase. Exercise sharp technical & product intuition to guide the team’s system architectures and product roadmaps. Drive product reliability, code quality, AI evaluation, testing, and maintenance for the broader team. Oversee hiring, onboarding, and mentorship for a rapidly growing team. Develop rigorou

About Perplexity AI In 2026, we launched Computer, the defining product for the new era of agentic AI. We’ve scaled beyond the millions of people using Perplexity every day for research, shopping, investing and curiosity into a new paradigm of using AI to transform knowledge into action. As an engineer at Perplexity, your role will be to build products and systems that define how AI empowers humans to think, decide and act. You’ll ship fast and work between both product and infrastructure advancements in our technology. Why Perplexity is different Craftsmanship . We build high quality, tasteful products targeting both the AI native and AI curious. Ownership . You identify the problem, design the solution and ship it. Entrepreneurship . We think like founders, act with urgency, and hustle to deliver for each other and our users. Scholarship . Work among highly talented peers, pursuing knowledge and truth, upleveling ourselves, our teams, and our products. Partnership . We amplify each others' strengths, break down silos, and give selflessly to help our colleagues deliver excellence. About the Role Perplexity is looking for a deeply technical software engineer to own and evolve our model serving platform, which is responsible for connecting our products and research systems with our inference layers. This mission-critical system gives teams one fast, reliable interface across providers. You will work at the intersection of distributed systems, AI products, and infrastructure. You will make it dramatically easier for teams across Perplexity to adopt new models, run rigorous experiments, and ship dependable products. The ideal candidate combines sound technical judgment forged in production systems with active curiosity around the rapidly changing capabilities and failure modes of frontier models. You will design core abstractions, lead high-conviction technical decisions, and write production Rust and Python for a service that serves hundreds of millions of model reque

Perplexity is revolutionizing how people discover and interact with information through AI-powered search and knowledge tools. As we expand our global footprint, we're establishing a strategic presence in London to drive innovation and growth across Europe. The Role: We're seeking an exceptional Site Lead to establish and scale our London office. This is a unique opportunity to shape Perplexity's presence in one of the world's leading tech hubs, building teams and culture from the ground up while driving technical excellence in infrastructure and AI systems. As Site Lead, you'll serve as the face of Perplexity in London, responsible for building our technical organization, fostering a world-class engineering culture, and directly managing one or more infrastructure teams. You'll report to senior leadership and work cross-functionally with teams across our global footprint. The individual in this role will manage teams in LON themselves while also facilitating. Responsibilities: Site Leadership & Culture Establish and lead Perplexity's London office, setting the cultural foundation and operating principles Build a collaborative, high-performance engineering culture that aligns with Perplexity's values while embracing the strengths of the London tech ecosystem Serve as the primary point of contact for all London-based activities and represent the site in company-wide strategic discussions Partner with People/HR, Finance, and Operations to ensure seamless site operations Drive local community engagement, partnerships, and Perplexity's brand presence in the London and European tech community Technical Leadership: Directly manage and mentor one or more infrastructure or AI infrastructure teams in London (5-15+ engineers) Set technical direction and strategy for London-based infrastructure initiatives in alignment with company-wide goals Drive architectural decisions and technical excellence across teams Ensure robust systems for deployment, monitoring, scalability, a

Perplexity is seeking experienced ML engineers to design, build, and optimize the recommendation systems that power core experiences on Perplexity. Perplexity builds AI for those who expect more. Our products are designed to help people find answers, make their most consequential decisions, and complete increasingly ambitious work. Across these use cases, the Perplexity experience must feel deeply personal. Great recommendations and user understanding are central to delighting and delivering for each user. To do this, we are reimagining recommendation systems for the LLM era. Our goal is to combine the intelligence of frontier LLMs, the personalization context that comes from real product usage, and the continual learning capabilities of modern recommendation systems. We build systems that draw on past context and connected data sources to deeply understand each user's needs and recommend the actions that help them get the most out of Perplexity. Why Perplexity is Different Craftsmanship . We build high quality, tasteful products targeting both AI native and AI curious users. Ownership . You identify the problem, design the solution and ship it. Entrepreneurship . We think like founders, act with urgency, and hustle to deliver for each other and our users. Scholarship . Work among highly talented peers, pursuing knowledge and truth, upleveling ourselves, our teams, and our products. Partnership . We amplify each others’ strengths, break down silos, and give selflessly to help our colleagues deliver excellence. What you'll do Own the personalization and ranking behind key product surfaces to make Perplexity more useful and drive impact on core user and business metrics. Build user modeling that captures intent, preference, and propensity, and powers more relevant, more personalized experiences. Design the decision layer that balances competing objectives to produce the best overall experience for the user. Build the data and evaluation foundations that let these syst

We are looking for an AI Infra engineer to join our growing team. We work with Kubernetes, Slurm, Python, C++, PyTorch, and primarily on AWS. As an AI Infrastructure Engineer, you will be partnering closely with our Inference and Research teams to build, deploy, and optimize our large-scale AI training and inference clusters. Responsibilities Design, deploy, and maintain scalable Kubernetes clusters for AI model inference and training workloads Manage and optimize Slurm-based HPC environments for distributed training of large language models Develop robust APIs and orchestration systems for both training pipelines and inference services Implement resource scheduling and job management systems across heterogeneous compute environments Benchmark system performance, diagnose bottlenecks, and implement improvements across both training and inference infrastructure Build monitoring, alerting, and observability solutions tailored to ML workloads running on Kubernetes and Slurm Respond swiftly to system outages and collaborate across teams to maintain high uptime for critical training runs and inference services Optimize cluster utilization and implement autoscaling strategies for dynamic workload demands Qualifications Strong expertise in Kubernetes administration, including custom resource definitions, operators, and cluster management Hands-on experience with Slurm workload management, including job scheduling, resource allocation, and cluster optimization Experience with deploying and managing distributed training systems at scale Deep understanding of container orchestration and distributed systems architecture High level familiarity with LLM architecture and training processes (Multi-Head Attention, Multi/Grouped-Query, distributed training strategies) Experience managing GPU clusters and optimizing compute resource utilization Required Skills Expert-level Kubernetes administration and YAML configuration management Proficiency with Slurm job scheduling, resource mana

We are looking for an AI Infra engineer to join our growing team. We work with Kubernetes, Slurm, Python, C++, PyTorch, and primarily on AWS. As an AI Infrastructure Engineer, you will be partnering closely with our Inference and Research teams to build, deploy, and optimize our large-scale AI training and inference clusters Responsibilities Design, deploy, and maintain scalable Kubernetes clusters for AI model inference and training workloads Manage and optimize Slurm-based HPC environments for distributed training of large language models Develop robust APIs and orchestration systems for both training pipelines and inference services Implement resource scheduling and job management systems across heterogeneous compute environments Benchmark system performance, diagnose bottlenecks, and implement improvements across both training and inference infrastructure Build monitoring, alerting, and observability solutions tailored to ML workloads running on Kubernetes and Slurm Respond swiftly to system outages and collaborate across teams to maintain high uptime for critical training runs and inference services Optimize cluster utilization and implement autoscaling strategies for dynamic workload demands Qualifications Strong expertise in Kubernetes administration, including custom resource definitions, operators, and cluster management Hands-on experience with Slurm workload management, including job scheduling, resource allocation, and cluster optimization Experience with deploying and managing distributed training systems at scale Deep understanding of container orchestration and distributed systems architecture High level familiarity with LLM architecture and training processes (Multi-Head Attention, Multi/Grouped-Query, distributed training strategies) Experience managing GPU clusters and optimizing compute resource utilization Required Skills Expert-level Kubernetes administration and YAML configuration management Proficiency with Slurm job scheduling, resource manag

We build and run the inference engine behind every Perplexity query and deploy dozens of model architectures at scale with tight latency and cost budgets. Our stack is Rust, Python, CUDA, and CuTe DSL - and we need another engineer to join us. What you will work on Examples of real work the team does: New models support. Support transformer-based retrieval, text-generation, and multimodal models in our inference infrastructure, from weight loading, request scheduling and KV-cache management to support in API Gateway. GPU kernels migration to CuTe DSL. Port our in-house CUDA kernels to NVIDIA's CuTe DSL so they run on GB200 today and are portable to Vera Rubin racks tomorrow. Rust-native serving runtime. Develop our internal Rust-based inference server to solve all Python pains and keep up with rapidly growing traffic. Performance optimisation. Profile and fix bottlenecks from network ingress through continuous batching and GPU kernel interleaving. Reliability and observability. Build dashboards, alerts, and automated remediation so we catch regressions before users do. Respond to and learn from production incidents. Who we're looking for Deep experience with GPU programming and performance work (CUDA, Triton, CUTLASS, or similar). Any other deep systems programming experience is a plus. You understand modern LLM architectures and are able to bring them up reliably in a production environment. You've built and operated production distributed systems under real load - ideally performance-critical ones. Comfortable working across languages and layers: Rust for the serving runtime, Python for model code, CUDA/CuteDSL for kernels. You own problems end-to-end. You can read a research paper on Monday, write a kernel on Wednesday, and debug a production incident on Friday. Self-directed. You do well in fast-moving environments where the path forward isn't laid out for you. Good if you touched any of ML compilers and framework internals: PyTorch internals, torch.compile, cus

We are looking for an AI Inference Engineer to join our growing team. We build and run the inference engine behind every Perplexity query and deploy dozens of model architectures at scale with tight latency and cost budgets. Our stack is Rust, Python, CUDA, and CuTe DSL. Responsibilities: New models support. Support transformer-based retrieval, text-generation, and multimodal models in our inference infrastructure, from weight loading, request scheduling and KV-cache management to support in API Gateway. GPU kernels migration to CuTe DSL. Port our in-house CUDA kernels to NVIDIA's CuTe DSL so they run on GB200 today and are portable to Vera Rubin racks tomorrow. Rust-native serving runtime. Develop our internal Rust-based inference server to solve all Python pains and keep up with rapidly growing traffic. Performance optimisation. Profile and fix bottlenecks from network ingress through continuous batching and GPU kernels interleaving. Reliability and observability. Build dashboards, alerts, and automated remediation so we catch regressions before users do. Respond to and learn from production incidents. Who we're looking for: Deep experience with GPU programming and performance work (CUDA, Triton, CUTLASS, or similar). Any other deep systems programming experience is a plus. You understand modern LLM architectures and are able to bring them up reliably in a production environment. You've built and operated production distributed systems under real load - ideally performance-critical ones. Comfortable working across languages and layers: Rust for the serving runtime, Python for model code, CUDA/CuteDSL for kernels. You own problems end-to-end. You can read a research paper on Monday, write a kernel on Wednesday, and debug a production incident on Friday. Self-directed. You do well in fast-moving environments where the path forward isn't laid out for you. Nice-to-have: ML compilers and framework internals: PyTorch internals, torch.compile, custom operators. Distribut