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Location: Ho Chi Minh City, Vietnam Top 3 Reasons To Join Us International Environment Working on the latest tech for the Insurtech Market Leader Competitive remuneration package About Us At CoverGo, our mission is to empower all insurance companies to make insurance 100% digital and accessible to everyone. We are a leading global no-code insurance platform for health, life, and P&C We’re the winner of the Insurtech of the Year in all of Asia and other awards globally We work with insurance enterprise clients such as AXA, Bupa, MSIG, Dai-ichi, Bank of China Group Insurance, and many more We're an international, diverse team of over 120 people with 30 nationalities and team members working remotely from all over the world We are fully funded and backed by reputable VC funds and strategic institutional investors We have a global presence in Asia, EMEA and the Americas We’ve grown our annualized revenue by over 30x since January 2021 We’re constantly working towards making CoverGo a workplace that you love coming to. We deeply believe that bringing together a diversity of thoughts, expressions, and perspectives is key to building the best culture for equally diverse communities all over the world About the Role As the Lead Full Stack Engineer at CoverGo, you will play a pivotal role in driving the technical direction and execution of our innovative insurance enterprise software solutions. Lead and mentor a team of talented engineers, guiding them through the design, development, and maintenance of our SaaS platform. Collaborate closely with stakeholders to understand business requirements, architect scalable solutions, and ensure the delivery of high-quality software. Stay abreast of emerging technologies, advocate for best practices, and foster a culture of innovation and excellence within the engineering team. What You Will Do Lead the end-to-end development lifecycle of our SaaS platform, from design and implementation to deployment and maintenance, ensuring alignment with business objectives and technical requirements. Provide technical leadership and mentorship to a team of Full Stack Engineers, fostering their growth and development through coaching, feedback, and knowledge sharing. Collaborate closely with product managers, designers, and other stakeholders to translate business requirements into technical solutions, prioritizing features and enhancements based on impact and feasibility. Drive architectural decisions and establish coding standards and best practices to ensure the scalability, reliability, and maintainability of the platform. Conduct thorough code reviews, enforce quality standards, and promote a culture of continuous improvement through the adoption of testing methodologies and automation. Proactively identify technical debt, scalability bottlenecks, and areas for optimization, leading initiatives to address them and improve overall system performance. Champion security and compliance initiatives, ensuring adherence to industry regulations and best practices, and driving the implementation of robust security measures. Stay updated with the latest trends, technologies, and best practices in software development, evaluating their potential impact and relevance to our platform, and advocating for their adoption where appropriate. What We Need Demonstrated leadership experience in a technical role, with a track record of successfully leading and mentoring engineering teams in the development of complex SaaS applications. Extensive hands-on experience with Full Stack development using Node.js and related technologies, with a minimum of 8 years of relevant experience. Proficiency in front-end and back-end technologies such as Node.js, Vue.js, GraphQL, MongoDB, Docker, and TypeScript. Strong expertise in cloud platforms such as AWS, Azure, or Google Cloud, with experience in architecting and deploying scalable and resilient applications. In-depth knowledge of Test-Driven Development (TDD) methodologies, unit, integration, and functional testing, and experience with testing frameworks and automation tools. Solid understanding of Object-Oriented Programming (OOP) principles, SOLID principles, and design patterns, with practical experience in their application. Thorough understanding of web security best practices and experience in implementing security measures and ensuring compliance with industry regulations. Familiarity with container technologies like Docker and proficiency in cloud infrastructure and solutions. Experience with Nest.js is a plus. Excellent communication and interpersonal skills, with the ability to effectively collaborate with cross-functional teams and stakeholders. Proven ability to prioritize and manage multiple projects simultaneously, with a proactive and results-oriented approach. Passion for continuous learning and professional development, with a keen interest in staying updated with emerging technologies and industry trends. Why You'll Love Working Here Hybrid Work Model 15 Days Annual Leave 13th-month Pay Annual Performance Bonus Health Insurance Stock Options after 6 months Company activities and events Learning and development plan CoverGo Company Video By submitting your application, you confirm that you have read, understood, and accepted the content of CoverGo’s Privacy Notice and you consent to the processing of your data as part of this application.

Location: Ho Chi Minh City, Vietnam Top 3 Reasons To Join Us Competitive Salary International Environment Working on the latest tech for the Insurtech Market Leader About Us At CoverGo, our mission is to empower all insurance companies to make insurance 100% digital and accessible to everyone. We are a leading global no-code insurance platform for health, life, and P&C We’re the winner of the Insurtech of the Year in all of Asia and other awards globally We work with insurance enterprise clients such as AXA, Bupa, MSIG, Dai-ichi, Bank of China Group Insurance, and many more We're an international, diverse team of over 120 people with 30 nationalities and team members working remotely from all over the world We are fully funded and backed by reputable VC funds and strategic institutional investors We have a global presence in Asia, EMEA and the Americas We’ve grown our annualized revenue by over 30x since January 2021 We’re constantly working towards making CoverGo a workplace that you love coming to. We deeply believe that bringing together a diversity of thoughts, expressions, and perspectives is key to building the best culture for equally diverse communities all over the world About the Role As the Lead Full Stack Engineer, you will take on a pivotal role in guiding the design, implementation, and maintenance of our innovative SaaS platform. You will provide technical direction to the team, working across both front-end and back-end technologies, ensuring scalable and efficient software solutions. Collaborate closely with cross-functional teams to understand end-user requirements, optimize application performance, and enforce best coding practices. Your role will encompass mentoring junior developers, driving innovation, and ensuring the continuous growth and success of our platform. What You Will Do Lead the design, implementation, and maintenance of our insurance enterprise software solutions, providing technical direction to the team. Demonstrate expertise in both front-end and back-end technologies, offering guidance on technology selection, architecture, and best practices. Collaborate with product, design, and engineering teams to understand requirements and technical concepts, ensuring cohesive and effective solutions with a seamless user experience. Optimize platform performance to handle large datasets and high user traffic, ensuring a smooth and responsive user experience. Conduct and lead code reviews, enforce best coding practices, and drive the adoption of standards to maintain high code quality. Investigate and promptly address bugs to uphold platform reliability. Ensure security and compliance by following best practices for securing web applications and adhering to relevant regulations. Stay abreast of the latest technologies, frameworks, and best practices, guiding the team in remaining competitive and innovative. Design the platform with scalability and flexibility, accommodating evolving features and changes as the user base grows. Provide mentorship and technical leadership to junior developers, fostering their growth and contribution to the team's objectives. What We Need Proven experience of 8+ years as a Full Stack Engineer, with a track record of leading complex SaaS applications. Minimum 2 years in a lead/manager role, able to guide and ensure development of teams Strong technical background with proficiency in back-end technologies, such as .NET Core, C#, GraphQL, MongoDB, Docker Proficiency in front-end technologies such as Javascript, Vue.js and/or React.js Familiarity with cloud platforms like AWS, Azure, or Google Cloud for building scalable applications Experience with microservices and/or clean architecture Thorough understanding and practical experience with Test-Driven Development (TDD) to ensure robust and well-tested code Experience with unit, integration and function testing using front-end and back-end testing libraries Proficiency in Domain-Driven Design (DDD) principles to create a maintainable and modular architecture that aligns with business domains In-depth knowledge of web security best practices and experience in ensuring compliance with industry regulations Demonstrated experience in performance optimization for high-traffic web applications Experience with container technologies like docker and good understanding of cloud infrastructure and solutions. Experience with agile development methodologies and collaborating with cross-functional teams Excellent problem-solving skills and a proactive approach to troubleshooting and bug fixing Strong communication skills to effectively interact with both technical and non-technical team members with diverse backgrounds A passion for staying updated with the latest trends, technologies, and best practices in software development Why You'll Love Working Here International Environment Competitive remuneration package Hybrid Work Model for those in Ho Chi Minh City; Remote working for those outside Ho Chi Minh City 15 Days Annual Leave 13th-month Pay Annual Performance Bonus Stock Options after 6 months Health Insurance Company activities and events Learning and development plan CoverGo Company Video By submitting your application, you confirm that you have read, understood, and accepted the content of CoverGo’s Privacy Notice and you consent to the processing of your data as part of this application.

Location: Singapore About the Company Our client is a stealth AI startup backed by one of Southeast Asia's leading technology companies and is currently building its global founding team. The company is developing an AI-native communication platform designed to simplify everyday tasks by integrating AI directly into conversations. Instead of switching between multiple applications, users can plan, organize, compare, research, and complete tasks within a single intelligent assistant. Serving a market of billions of users still relying on traditional productivity tools, the platform focuses on delivering reliable AI workflows, persistent context, multi-step reasoning, and seamless task execution. The mission is to create an AI assistant that significantly improves productivity while making everyday work simpler and more intuitive. About the Role Our client is seeking a Senior Machine Learning Engineer to design and build production-grade machine learning systems that power its AI platform. This is a hands-on technical role focused on solving complex machine learning challenges, translating research into scalable solutions, and delivering reliable AI systems that operate effectively in real-world environments. Working closely with research, product, and engineering teams, this position will independently own critical machine learning components from development through production deployment. Key Responsibilities Build and develop core machine learning systems that power AI-driven product capabilities. Own the end-to-end machine learning lifecycle, including data preparation, model training, evaluation, inference, deployment, and continuous iteration. Translate research concepts into reliable, production-ready machine learning solutions. Investigate and resolve model performance issues and production system failures using real-world operational insights. Continuously improve machine learning systems through rapid experimentation, measurement, and iteration. Collaborate closely with research, product, and engineering teams to deliver impactful AI features. Provide technical guidance through code reviews, best practices, and mentorship of fellow machine learning engineers. Develop solutions that balance real-world production requirements, including latency, scalability, cost efficiency, reliability, and safety. Requirements Proven experience building and deploying production machine learning systems used by real users. Strong understanding of modern machine learning models, including their behavior, limitations, and failure modes in production environments. Excellent software engineering skills with the ability to develop robust, maintainable, production-quality systems. Experience designing and implementing scalable machine learning pipelines and infrastructure. Strong ownership mindset with the ability to independently solve complex technical challenges from concept through deployment. Excellent problem-solving, communication, and collaboration skills within cross-functional engineering teams. Demonstrated ability to iterate quickly and continuously improve machine learning systems based on production feedback. Preferred Technical Skills Experience with the following technologies is preferred: Python PyTorch and/or JAX GPU-based training and inference systems

Location: Singapore About the Company Our client is a stealth AI startup backed by one of Southeast Asia's leading technology companies and is currently building its global founding team. The company is developing an AI-native communication platform designed to simplify everyday tasks by integrating AI directly into conversations. Instead of switching between multiple applications, users can plan, organize, compare, research, and complete tasks within a single intelligent assistant. Serving a market of billions of users still relying on traditional productivity tools, the platform focuses on delivering reliable AI workflows, persistent context, multi-step reasoning, and seamless task execution. The mission is to create an AI assistant that significantly improves productivity while making everyday work simpler and more intuitive. About the Role Our client is seeking a Principal Machine Learning Engineer to build and deploy production-grade machine learning systems that power its AI platform. This role focuses on translating research into scalable solutions by developing robust training pipelines, inference systems, evaluation frameworks, and deployment infrastructure. Working closely with research and application engineering teams, this position will play a key role in delivering reliable, high-performance ML systems that operate effectively under real-world production constraints. Key Responsibilities Build and own end-to-end machine learning pipelines covering data processing, model training, evaluation, inference, and deployment. Fine-tune and adapt models using modern techniques such as LoRA, QLoRA, Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and model distillation. Design and operate scalable inference systems while balancing latency, cost, and reliability. Develop and maintain data pipelines for both synthetic and real-world training datasets. Build evaluation frameworks to assess model performance, robustness, safety, and bias in collaboration with research teams. Optimize production deployments through GPU optimization, memory efficiency, latency reduction, and scaling strategies. Collaborate with application engineering teams to integrate machine learning systems into backend, mobile, and desktop applications. Continuously improve ML systems through rapid iteration and real-world performance monitoring while balancing production constraints such as latency, cost, reliability, and safety. Requirements Strong background in deep learning and transformer-based architectures. Hands-on experience training, fine-tuning, or deploying large-scale machine learning models in production. Proficiency with modern machine learning frameworks such as PyTorch or JAX . Experience with distributed training and inference frameworks, including technologies such as DeepSpeed, FSDP, Megatron, ZeRO, or Ray . Strong software engineering skills with experience building robust, maintainable, production-grade systems. Experience optimizing GPU workloads, including memory efficiency, quantization, and mixed precision. Ability to independently own end-to-end machine learning systems in fast-moving environments. Strong problem-solving skills with a focus on rapid iteration and continuous improvement. Preferred Qualifications Experience with one or more of the following is preferred: LLM inference frameworks such as vLLM, TensorRT-LLM, or FasterTransformer Open-source contributions to machine learning or systems libraries Scientific computing, compiler technologies, or GPU kernel development Reinforcement Learning from Human Feedback (RLHF) pipelines, including PPO, DPO, or ORPO Training or deploying multimodal or diffusion models Large-scale data processing frameworks such as Apache Arrow, Spark, or Ray

Location: Singapore About the Company Our client is a stealth AI startup backed by one of Southeast Asia's leading technology companies and is currently building its global founding team. The company is developing an AI-native communication platform designed to simplify everyday tasks by integrating AI directly into conversations. Instead of switching between multiple applications, users can plan, organize, compare, research, and complete tasks within a single intelligent assistant. Serving a market of billions of users still relying on traditional productivity tools, the platform focuses on delivering reliable AI workflows, persistent context, multi-step reasoning, and seamless task execution. The mission is to create an AI assistant that significantly improves productivity while making everyday work simpler and more intuitive. About the Role Our client is looking for a Full Stack Engineer – AI Systems to build AI-native product experiences that transform advanced AI capabilities into intuitive, production-ready workflows. This role combines frontend, backend, and AI engineering to develop intelligent features that are reliable, scalable, and user-centric. Working closely with product, backend, and machine learning teams, this position will focus on designing end-to-end AI workflows, integrating large language models (LLMs), and delivering seamless user experiences across web applications. Key Responsibilities Build end-to-end product features across frontend, backend, and AI integrations. Design and develop AI agent workflows that support planning, tool usage, failure handling, and recovery across multiple steps. Integrate LLMs, memory systems, and external tools into scalable, production-grade applications. Design responsive AI interactions with streaming capabilities, partial results, and low-latency performance. Improve system reliability, observability, and fallback mechanisms for AI-powered features. Collaborate closely with product, backend, and machine learning teams to deliver features from concept to production. Continuously iterate and improve product functionality based on user feedback, production insights, and real-world usage. Requirements Strong experience in full stack software engineering across frontend and backend development. Solid understanding of system design, scalable architecture, and API development. Experience building AI-powered applications using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), or similar technologies. Ability to work effectively in fast-paced environments with evolving requirements. Strong ownership mindset with experience delivering features from ideation through production deployment. Excellent problem-solving skills and the ability to make pragmatic engineering decisions in ambiguous situations. Preferred Technical Skills Experience with some or all of the following technologies is preferred: Next.js Python Node.js PyTorch OpenAI, Anthropic, or open-source Large Language Models (LLMs) SQL and NoSQL databases Kubernetes Docker

Location: Singapore About the Company Our client is a stealth AI startup backed by one of Southeast Asia's leading technology companies and is currently building its global founding team. The company is developing an AI-native communication platform designed to simplify everyday tasks by integrating AI directly into conversations. Instead of switching between multiple applications, users can plan, organize, compare, research, and complete tasks within a single intelligent assistant. Serving a market of billions of users still relying on traditional productivity tools, the platform focuses on delivering reliable AI workflows, persistent context, multi-step reasoning, and seamless task execution. The mission is to create an AI assistant that significantly improves productivity while making everyday work simpler and more intuitive. About the Role Our client is seeking an Android Software Engineer to build and enhance its AI-native mobile experience. This role is responsible for developing production-grade Android applications where AI is at the core of the user experience, ensuring performance, reliability, and usability across all mobile interactions. Working closely with backend and machine learning teams, this position will focus on delivering scalable Android applications that integrate advanced AI capabilities while maintaining a seamless user experience. Key Responsibilities Build and maintain production Android applications using Kotlin . Integrate AI-powered features such as chat, vision, voice, and recommendation systems through backend APIs. Design intuitive user experiences for AI interactions, including streaming responses, retries, and partial results. Optimize application performance, memory usage, and responsiveness for AI-intensive workflows. Implement analytics, logging, and user feedback mechanisms to support continuous AI evaluation and product improvement. Collaborate closely with backend and machine learning engineers to define API integrations and system behavior. Ensure application stability, security, and scalability within production environments. Requirements Minimum 3 years of Android application development experience using Kotlin. Hands-on experience integrating AI-powered features, such as Large Language Models (LLMs), computer vision, or speech APIs. Strong understanding of asynchronous programming using Coroutines and Flow . Experience working with RESTful or gRPC APIs and structured data formats. Strong debugging, troubleshooting, and performance profiling skills. Experience developing applications that operate reliably under conditions involving latency, partial failures, and non-deterministic behavior. Familiarity with ML Kit or lightweight on-device inference is an advantage. Experience publishing and maintaining production applications on the Google Play Store . Preferred Technical Skills Experience with the following technologies is preferred: Kotlin / Java SQL and NoSQL databases TensorFlow Lite (on-device inference)

Location: Singapore About the Company Our client is a stealth AI startup backed by one of Southeast Asia's leading technology companies and is currently building its global founding team. The company is developing an AI-native communication platform designed to simplify everyday tasks by integrating AI directly into conversations. Instead of switching between multiple applications, users can plan, organize, compare, research, and complete tasks within a single intelligent assistant. Serving a market of billions of users still relying on traditional productivity tools, the platform focuses on delivering reliable AI workflows, persistent context, multi-step reasoning, and seamless task execution. The mission is to create an AI assistant that significantly improves productivity while making everyday work simpler and more intuitive. About the Role Our client is seeking a AI Backend Engineer to build and operate the backend infrastructure that powers AI-driven experiences across its products. The role focuses on owning the inference and orchestration layer that sits between AI models and end users, ensuring every interaction is fast, reliable, scalable, and cost-efficient. Working closely with engineering and AI teams, this position will play a key role in designing production-grade backend services, optimizing system performance, and delivering robust AI-powered features across mobile and desktop platforms. Key Responsibilities Build, maintain, and operate backend systems that power AI-enabled features in production. Design and implement AI inference pipelines, orchestration layers, and service architectures around AI models. Develop scalable, high-performance APIs that integrate seamlessly with frontend applications and machine learning systems. Optimize system performance by improving inference latency, throughput, caching, batching, and streaming capabilities. Own production operations, including monitoring, logging, alerting, and incident response. Troubleshoot and resolve issues within distributed systems to ensure service reliability and stability. Continuously improve backend architecture and production systems based on operational insights and real-world usage. Collaborate with cross-functional teams to deliver reliable, scalable, and high-quality AI-powered solutions. Requirements Strong backend engineering experience in production environments. Proven experience building and operating high-throughput, low-latency backend services. Experience designing scalable APIs and distributed systems. Familiarity with AI inference patterns, including Large Language Models (LLMs), embeddings, and multimodal AI systems. Strong debugging and troubleshooting skills within distributed production environments. Demonstrated ability to deliver production-ready solutions while iterating based on real-world system performance. Excellent analytical, problem-solving, and collaboration skills. Technical Skills Experience with the following technologies is preferred: Python Node.js PyTorch OpenAI, Anthropic, or open-source Large Language Models (LLMs) SQL and NoSQL databases Kubernetes Docker

Location: Tabuk, Saudi Arabia; Jeddah, Saudi Arabia; Riyadh, Saudi Arabia; Yanbu, Saudi Arabia The Operations Engineer will be responsible for monitoring and controlling the operation of all facility systems and coordinating maintenance-related activities at the Primary Nursery Facility in NEOM. The role will also oversee the delivery of support services, including Janitorial, Horticulture, Pest Control, Waste Management, and Office Services. The Operations Engineer will ensure the safe, efficient, and reliable operation of electro-mechanical systems, monitor service provider performance, ensure compliance with operational KPIs, resolve customer complaints, and continuously identify opportunities to improve facility performance, energy efficiency, and end-user satisfaction. Key Responsibilities Supervise and coordinate the daily operation of all MEP and electro-mechanical systems at the Primary Nursery Facility. Monitor and control equipment including MCCs, pumps, VFDs, fans, instrumentation, emergency generators, AHUs, chillers, and related systems. Plan operational activities, establish priorities, and coordinate maintenance and project-related works. Conduct regular inspections and monitor system performance to ensure facility readiness, reliability, and compliance with operational requirements. Analyze system performance, operating parameters, alarms, and set points, and implement corrective actions and improvement initiatives. Respond promptly to emergencies, major faults, equipment failures, and operational interruptions. Support Root Cause Analysis (RCA), incident investigations, commissioning activities, and the implementation of corrective actions. Manage and monitor contractors and service providers delivering Janitorial, Horticulture, Pest Control, Waste Management, and Office Services. Ensure customer satisfaction by resolving complaints, high-priority tickets, and recurring operational issues while monitoring service delivery against KPIs. Requirements Bachelor’s degree in Electrical, Mechanical, Electro-Mechanical Engineering, or a related field. 4–6 years of experience in Facility Management, Facility Operations, or a similar role. Proven experience in the operation and monitoring of MEP and electro-mechanical systems. Good knowledge of facility systems and equipment, including MCCs, pumps, VFDs, generators, AHUs, chillers, fans, and instrumentation. Ability to read and interpret technical drawings and understand MEP commissioning procedures. Experience in contractor management, service provider coordination, and KPI monitoring. Strong knowledge of troubleshooting, problem-solving, Root Cause Analysis (RCA), and corrective action processes. Good communication, leadership, coordination, analytical, and customer service skills. Good command of written and spoken English, with the ability to work effectively under pressure and respond to operational emergencies.

Задачи: — Поддержка и развитие платформенного шелла, включая аутентификацию и интеграцию SSO — Управление навигацией и компонентами приложения на современном фронтенд-фреймворке (например, React / TypeScript) — Управление арендаторами и пользователями с учетом мультиарендности — Создание и поддержка конвейеров загрузки данных — Управление и расширение оркестрационных рабочих процессов в Dagster — Разработка и поддержка логики трансформации в dbt и SQL — Работа с хранилищем данных на Google Cloud Platform (BigQuery) — Поддержка семантического слоя в Cube, включая определение метрик и моделирование данных — Кастомизация и обновление встроенных аналитических компонентов Embeddable — Поддержка темизации и актуализация SDK Embeddable — Ведение централизованной документации в Confluence, включая диаграммы баз данных DBML — Фиксация знаний для передачи и обеспечения непрерывности — Работа с целями по результатам: решение критичных проблем в течение одного рабочего дня, снижение дефектов, обеспечение доступности в рабочие часы — Использование свободного времени на профилактическое обслуживание, усиление и подключение новых источников данных и интеграций Требования: — Опыт AI-first разработки с использованием Claude Code или аналогичных инструментов AI-поддержки на всех этапах жизненного цикла разработки — Сертификация или готовность пройти профессиональные курсы Anthropic — Опыт full-stack разработки, включая современные фронтенд-фреймворки (React / TypeScript), реализацию аутентификации/SSO и архитектуру мультиарендных приложений — Практический опыт работы с Dagster или аналогичными инструментами оркестрации — Сильные навыки работы с dbt и SQL для трансформации данных — Опыт работы с BigQuery и стеком данных Google Cloud Platform — Опыт работы с Cube или аналогичными инструментами семантического слоя и определения метрик — Знакомство с инструментами встроенной аналитики (Embeddable или аналогичные), кастомизация компонентов, темизация, интеграция SDK — Умение работать самостоятельно и взаимодействовать с клиентскими заинтересованными сторонами — Дисциплина в ведении документации: Confluence, DBML/ER диаграммы — Готовность работать в основные часы, согласованные с часовым поясом Чикаго (CT) Желательно: — Опыт поддержки аналитических/BI платформ для корпоративных или спортивных/медийных клиентов — Опыт установки SLA-целей (время решения, доступность) и отчетности по ним Условия: — Занятость 80 часов в месяц с полным покрытием платформенного шелла, стека данных/семантики и поддержки Embeddable — Требуется резервное покрытие для обеспечения непрерывности в случае отсутствия, с возможностью четкой передачи контекста

Location: San Francisco, CA | New York City, NY | Seattle, WA 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 Code RL at Anthropic drives reinforcement learning efforts behind Claude's coding capabilities, creating and scaling agentic coding environments. This is an engineering role with unusual latitude to set technical direction and standards. You'll be part of a team solving the engineering side of research efforts such as embedding with research teams, getting up to speed on their systems and needs, and designing the frameworks, APIs, and infrastructure that let researchers move faster, then rotating off, leaving behind well-oiled systems those teams can understand, own, and maintain themselves. Your remit also includes the ongoing health of production RL runs: maintainable, monitored, and straightforward to triage. The team's problem space spans the client side of sandboxed execution for agentic RL environments, large-scale data processing jobs, the lifecycle of production datasets, and the frameworks researchers build environments on. You won't own all of this yourself, you'll take on the slices where your depth matters most. You'll be a strong fit if you have deep expertise in Python, a refined sense of taste for API and framework design, and hard-won intuition for how complex systems fail — especially silently. You should be comfortable diving into messy research code, finding the load-bearing abstractions, and improving them incrementally while researchers continue to build on top of your work. Key responsibilities Design widely-used APIs, frameworks, and abstractions that other engineers and researchers build on, with careful attention to interface legibility and principled defaults Embed with research teams on a rotational basis: understand their engineering needs, build systems and APIs that support their work, and transfer ownership so teams can maintain those systems after you rotate off Work directly in research codebases, improving reliability and structure without slowing down the research they support Anticipate silent failure modes and prevent them structurally through type safety, well-designed invariants, targeted testing, and refactors that shrink the surface area for bugs Contribute to the reliability of production RL systems, including monitoring, regression detection, and triage tooling Help define engineering standards, review practices, and design patterns for a new team, and mentor researchers and engineers in adopting them Minimum qualifications Deep expertise in Python, including static typing, safe async and concurrency patterns, and writing performant Python code A track record of designing intuitive, safe APIs or frameworks that other engineers or teams adopted and built on Experience working productively in large, evolving, or research-style codebases that you didn't originally write Demonstrated ability to anticipate failure modes — especially silent ones — and prevent them structurally through system design, type safety, and testing Strong written and verbal communication skills, including the ability to explain system designs to collaborators with varied engineering backgrounds Comfort with ambiguity: able to scope your own work from a loosely defined problem and drive it to a maintainable outcome Preferred qualifications Experience building infrastructure, tooling, or frameworks for machine learning research or RL workflows Familiarity with reinforcement learning concepts, agentic systems, or LLM training pipelines Experience building or operating large-scale distributed systems Experience building client libraries or SDKs on top of sandboxed, containerized, or remote execution platforms Experience with large-scale data processing or dataset lifecycle management Experience designing plugin systems or extensible class hierarchies used across an organization Experience embedding with or consulting for other teams, including successfully handing off systems for others to own Experience defining code standards, lint rules, or static verification approaches adopted across multiple teams Prior experience as a technical lead, or setting engineering standards for a team Prior experience maintaining an open source project Representative projects These are examples of the challenges the team tackles; no one person will work on all of them: Design a base RL environment abstraction general enough to be subclassed across a wide range of environments Design a model-tool interface for sandboxed agentic environments that has explicit serialization semantics Partner with the platform teams that own the sandbox runtime to specify low-level features that improve the integrity of agentic coding tasks Design probes that catch sandbox regressions early Design the lifecycle and maintenance scheme for a production dataset Lead a research code refactor replacing loosely structured data containers with equivalents that carry stronger correctness guarantees, without breaking the experiments that depend on them Design lint rules and code-style requirements that favor statically verifiable patterns — including patterns less likely to be overlooked by an LLM reviewing or writing the code — to shrink the surface area for silent bugs Build the access layer that lets researchers discover and reuse data artifacts across teams The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: $405,000 — $625,000 USD Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed.  Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings. How we're different We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences. Come work with us! Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.

Location: San Francisco, CA | New York City, NY 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's Environments organization builds and maintains the infrastructure that improves Claude’s capabilities through reinforcement learning. That includes the frameworks researchers use to build environments and the infrastructure responsible for running them. The team's mission is to productionize research. You'll embed with research teams, get up to speed on how they work, and design the frameworks and APIs that let them move faster, building systems the team can understand, own, and maintain themselves. Scope also includes keeping production RL runs healthy, maintainable, monitored, and easy to triage. You'll be a strong fit if you have deep expertise in Python, a refined sense of taste for API and framework design, and good intuition for how complex systems fail, especially silently. It's a bonus if you've built and operated a stateful distributed system, such as a workflow engine, actor framework, or durable-execution runtime, where correctness depends on getting shared state and recovery right. You should be comfortable diving into messy research code, finding the abstractions that matter, and improving them incrementally while researchers continue to build on your work. You should also be comfortable using AI tools to accelerate your own development, but have an impulse towards deep verification. Key responsibilities Design widely used APIs, frameworks, and abstractions that other engineers and researchers build on, making correct usage the default and ruling out entire classes of errors structurally Own the platform layers that sit beneath every environment, including the agent runtime Build the tooling that lets environment owners understand, debug, and maintain their environments in production without needing an infrastructure engineer in the loop Embed with research teams on a rotational basis, work directly in their codebases without slowing down the research they support, and transfer ownership when you rotate off Anticipate silent failure modes and prevent them structurally through type safety, well-designed invariants, targeted testing, and refactors that reduce the room for correctness issues Drive adoption of new frameworks across the organization, including deprecations and cutovers Help define the engineering standards, review practices, and design patterns for a new team, and mentor researchers and engineers in adopting them Minimum qualifications Deep expertise in Python, including static typing, safe async and concurrency patterns, and writing performant code Strong taste in API and framework design, the ability to explain why an interface is right or wrong rather than just recognizing it, and a track record of other engineers or teams adopting and building on frameworks you have built Experience designing or operating stateful concurrent or distributed systems, and reasoning carefully about failure, retries, idempotency, and consistency A habit of verification: you measure before you conclude, and you build the checks that let a system show it's correct Experience working productively in large, evolving, or research-style codebases that you didn't originally write Strong written and verbal communication with collaborators of varied engineering backgrounds, and comfort with ambiguity: able to scope your own work from a loosely defined problem and drive it to a maintainable outcome Preferred qualifications Experience building infrastructure, tooling, or frameworks for machine learning research or RL workflows, and familiarity with agentic systems or LLM training pipelines Experience building agent frameworks, orchestration engines, or multi-agent systems, including checkpoint and restore, replay, and coordination of long-running stateful processes Experience using AI coding tools on code where correctness matters, with good judgment about what to delegate and how to make the results verifiable Experience building client libraries or SDKs on top of sandboxed, containerized, or remote execution platforms Experience with large-scale data processing, dataset lifecycle management, or data lineage systems Experience designing serialization schemes, plugin systems, or extensible class hierarchies used across an organization Experience embedding with or consulting for other teams and handing off systems for others to own, or defining code standards adopted across teams, or prior experience as a technical lead Representative projects These are examples of the challenges the team tackles: Design a base RL environment abstraction that can be subclassed to support the large majority of environments built across RL Redesign the model-tool interface for sandboxed agentic environments so that state is guaranteed to survive serialization, making it structurally impossible to write a tool that silently loses state Design the state-sharing and recovery model for multi-agent workloads, so that losing a sandbox partway through a task becomes a transparent resume rather than lost work Define the failure and retry model for a sandboxed execution platform, distinguishing infrastructure faults from genuine task outcomes so that each is handled correctly Build the tooling that lets an environment owner diagnose why their environment is unhealthy in a production run, and fix it themselves The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: $405,000 — $605,000 USD Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed.  Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings. How we're different We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences. Come work with us! Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.

Location: United States (Remote) Workplace: Remote ABOUT ONEPAY OnePay is the consumer fintech trusted by millions of Americans to make money better. Our financial system is broken. High fees, low rates, and too few ways to actually grow your money. We’re fixing it. And we’re moving fast. We’re an all-in-one financial services platform that brings together banking, high-yield savings, credit cards, point-of-sale lending, investing, and crypto in one place. We also partner with employers, HCM providers, gig platforms, and others to deliver embedded financial services to millions of employees and frontline workers. We’re backed by Walmart, the world’s largest retailer, and Ribbit Capital, one of fintech’s most respected investors, giving us rare scale, distribution, and the opportunity to build something truly category-defining. But what really sets OnePay apart is how we move. Our customers don’t have time to wait… and neither do we. This place moves fast, and we’re looking for people who are: - Ready to run - Hungry and driven by urgency - Exceptional at what they do, with low ego - Comfortable operating in motion THE ROLE As a Platform Engineer on the Builder Experience Team at OnePay, you will build the core platform services and developer frameworks that power large-scale, distributed systems and enable the next generation of intelligent, agent-driven applications.   WHAT YOU’LL DO - Design and operate platform-oriented core services that support high-scale distributed systems across OnePay’s product ecosystem - Develop internal developer tooling and shared infrastructure that improve velocity, reliability, and observability across engineering teams - Build and evolve frameworks that support the construction and orchestration of agentic applications, enabling intelligent, automated workflows across products - Drive enhancements in CI/CD pipelines, automation, and developer workflows to increase deployment speed, reliability, and confidence across teams   YOU BRING - 2-9 years of experience in software engineering within fintech, cloud infrastructure, or large-scale distributed systems environments - Experience building developer platforms, internal tooling, or frameworks that enable other engineers to move faster - Deep expertise in distributed systems, asynchronous processing, and high-availability platform services - Production experience in AWS cloud environments and building cloud-native infrastructure - Demonstrated experience designing and building agentic developer tools that empower automation, collaboration, or intelligent workflows - Strong experience working with CI/CD pipelines and driving continuous integration and delivery improvements - Drive and proactivity – everyone here is a builder and executor TOOLS WE USE We use Typescript front to back, with node and NestJS frameworks for server code and React Native for our iOS, Android, and Web clients. We run on a highly distributed cloud-native stack with Kubernetes and AWS, and scale and resiliency are at the heart of everything we build. We also embrace agentic development, so engineers have their choice of a variety of models and agent harnesses, including Claude Code, Cursor, Codex, and more to fit their workflow. While you don't need experience with our exact stack, familiarity with modern software engineering practices will help you ramp up quickly. WHAT WE OFFER - Competitive base salary, stock options, and health benefits from Day 1 - 401(k) plan with company match - Remote-friendly (US), flexible time off (FTO), and opportunities for growth - A high-growth, mission-driven, inclusive culture where your work has real impact STANDARD INTERVIEW PROCESS Our process varies by role. Most candidates go through: - AI-assisted initial screen - Interview with Talent Partner - Technical or Hiring Manager Interview - Team Interview - Executive Interview - Offer! EQUAL EMPLOYMENT OPPORTUNITY To build technology and products that are used and loved by people and solve real-world problems, we need to build a team with many different perspectives and experiences. We are an equal opportunity employer. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status. We encourage candidates from all backgrounds to apply. Applicants in need of special assistance or accommodation during the interview process or in accessing our website may contact us at talent@onepay.com.

Location: Canada; United States Role: We are is looking for qualified Payload System Verification and Validation Engineers to join our advanced satellite payload development team. This role focuses on system-level engineering of next-generation digital communications payloads, with a strong emphasis on requirements verification through test . The selected candidate will contribute to the entire payload lifecycle — from architecture definition and requirement specification to integration, verification, and in-orbit validation. The role requires good understanding of both digital signal processing and RF payload architectures , especially involving Software Defined Radio (SDR) and Direct Radiating Array (DRA) systems. Tasks Define, analyze, and validate system and subsystem requirements for digital telecommunications payloads. Develop and execute verification plans , including defining test strategies, writing test procedures, and analyzing results. Lead or support system-level payload test campaigns, both at equipment and spacecraft level. Interface with payload hardware and software teams to ensure correct implementation and traceability of requirements. Participate in technical reviews and support anomaly investigation and resolution. Work with multi-disciplinary teams including RF, digital, embedded software, FPGA, and systems engineering. Contribute to technology evolution by identifying opportunities for design optimization and innovation. Must haves Bachelor’s degree in Electrical Engineering, Telecommunications, Aerospace Engineering, or a related field. 5 years+ of experience in telecommunications systems, particularly in digital payloads. (experience outside of the Space industry will also be considered) Strong expertise in requirements verification by test , including practical experience in developing and executing test plans. Solid understanding of digital payload architectures , including SDR-based flexible processors and beamforming using DRA. Familiarity with hardware and software tools used in payload verification, such as: Vector Network Analyzers (VNA) and Spectrum Analyzers for RF testing Baseband signal generators , Channel Emulators , and BER testers Python , MATLAB , or LabVIEW for test automation and data analysis SCPI , VISA , and similar interfaces for instrument control Good understanding of FPGA-based systems and embedded software architectures is a plus. Must be eligible to work in Canada, capable of obtaining necessary security clearance. Complementary skills/knowledge Previous experience in Space or Constellation Operations Development and testing of new automated testing methodologies Knowledge of requirements Management tools such as DOORS/Windchill/Jama Demonstrated capability to efficiently work autonomously and with little to no supervision Written and verbal communications skills in English, knowledge of French will be considered favorably

Location: Canada; United States The Satellite-Level Verification and Validation (V&V) Engineer focuses on the highest tier of spacecraft integration, ensuring the fully assembled satellite meets all mission, launch provider, and environmental requirements. Role Overview This role manages the end-to-end verification lifecycle at the highest tier of architecture. It starts at the beginning of the program with verification planning and test methodology design, continues through full-system functional and environmental validation, and concludes with final qualification sign-off and in-orbit initialization readiness. Key Responsibilities & Tasks Verification Architecture & Matrix Ownership: Define the global verification strategy; author the System Verification Plan (SVP) and own the master Verification Control Matrix (VCM) to prove 100% requirement compliance. Method of Compliance (MoC) Definition: Determine the optimal, most cost-effective blend of Verification by Test, Analysis, Demonstration, and Inspection (TADI) for complex mission-level requirements. End-to-End Mission Validation: Design and execute full-system functional tests, "Day-in-the-Life" (DITL) mission simulations, and complex Fault Detection, Isolation, and Recovery (FDIR) validation scenarios. Environmental & Launch Qualification: Define test profiles, pass/fail criteria, and post-test correlation frameworks for full spacecraft environmental campaigns (Thermal Vacuum, Sine-Vibe, Acoustic, Shock). Sign-off & Qualification Review: Lead Technical Review Boards, manage non-conformance reports (NCRs), present compliance evidence at Qualification Reviews (QR), and build the final Flight Readiness data package. Candidate Prerequisites ("Must Haves") Experience: 5+ years in aerospace systems engineering, systems V&V, or space mission qualification roles. Systems Engineering Mastery: Deep understanding of how platform subsystems and digital/RF payloads interact to satisfy top-level mission performance metrics. Requirements Management: Expert-level capability in tracing, decomposing, and closing out complex requirements using enterprise tools like DOORS, Jama, or Windchill. Data Synthesis & Analysis: Advanced skills using Python or MATLAB to parse multi-subsystem telemetry, correlate test data with analytical models, and automate post-test verification reporting.

Location: Canada; United States The Platform Systems Verification and Validation (V&V) Engineer focuses on the satellite bus, ensuring the core infrastructure subsystems are fully verified and ready to support the payload. Role Overview This role focuses on the functional verification of the utility subsystems that keep the satellite alive and oriented. It ensures the core platform—including power, thermal control, propulsion, and pointing—operates flawlessly and responds correctly to the onboard flight software. Key Responsibilities & Tasks Bus Subsystem Verification: Test and validate critical platform subsystems, including Attitude Determination and Control (ADCS), Electrical Power Subsystems (EPS), Thermal Control (TCS), and Propulsion. Hardware-in-the-Loop (HIL): Utilize HIL testbeds to simulate orbital dynamics and sensor inputs (star trackers, GPS, sun sensors) to verify ADCS pointing accuracy. Power & Harness Validation: Perform power budget verification, battery charge/discharge cycle testing, and solar array simulator (SAS) integration. Flight Software (FSW) Interfacing: Test command routing, telemetry packet generation, and fault detection, isolation, and recovery (FDIR) loops. Subsystem Interface Control: Verify that all physical, data (CAN, SpaceWire, RS-422), and electrical interfaces between platform subsystems are compliant. Candidate Prerequisites ("Must Haves") Experience: 5+ years verifying aerospace subsystems, embedded robotics, or satellite platform architectures. Technical Breadth: Strong knowledge of power electronics, orbital mechanics, thermal management, or control systems. Simulators & Automation: Experience with automated test scripting (Python/MATLAB) to control power supplies, loads, and simulated sensor inputs. Protocol Expertise: Familiarity with space-industry data protocols and avionics testing architectures.

Location: San Francisco, CA 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 We're standing up a dedicated executive support function within IT, and we're hiring the IT Support Engineer who will build it. IT Support is the front door for every employee technology need at Anthropic, and this role creates its most visible lane: white-glove support for our executives, their administrative business partners (ABPs), and our board. You'll define what that service looks like: the support model for execs, board, and ABPs, the runbooks behind it, the intake and escalation paths, and the trusted relationships with ABPs and exec staff that make it work. You'll set the bar for discretion and polish as you go. You'll also be the person our leadership reaches when technology gets in the way. The requests that land in this lane range from routine to highly sensitive, and many arrive urgent: an executive locked out minutes before a meeting, an AV issue in a room full of leadership, a board member who needs setup handled quietly and correctly the first time. You own the request end to end and pull in specialist functions or IT Engineering pods as needed. Responsibilities Stand up the executive support intake channels and run it: monitor, respond, and follow through on every request from executives, ABPs, and board members Define the white-glove service model for discretion-sensitive asks and deliver to it, holding confidentiality under pressure Design the board-meeting AV support playbook with the AV pod, and provide AV support for executive rooms and board meetings, from setup and pre-checks through live triage Create exec-support runbooks and SLAs that keep the service consistent as it grows Build trusted relationships with ABPs and executive staff, and establish clear intake paths so leadership knows exactly where to go Handle ABP escalations and exec-urgent issues calmly, and route into specialist lanes or IT Engineering pods when a request needs them Establish the board member device, account, and access setup process with our Onboarding lane Work the shared ticket queue with the SF Core team: walk-ups, desk-side help, hardware swaps, it-assist overflow, and AV T1 triage Participate in the Core on-call rotation and contribute to the knowledge base and runbooks Maintain a regular on-site presence in San Francisco You may be a good fit if you have 7+ years of experience in IT support and 3+ years in executive support Experience standing up a service or program from scratch, such as an executive support desk, a VIP support function, or a white-glove service Experience supporting executives, their assistants, or a board to a white-glove standard A track record of handling sensitive requests with discretion, and the judgment to know what stays confidential without being told Strong hands-on IT support fundamentals: macOS-primary fleets, identity and access, SaaS troubleshooting, hardware lifecycle AV fluency for executive rooms and board meetings: you can set up, verify, and fix a room under time pressure Composure when an urgent escalation lands, and the judgment to know when to pull in help High polish and low ego: you take pride in service quality and still pick up whatever the team needs, including the ordinary queue work A security-first mindset and excellent judgment about when user experience yields to security requirements Comfort with ambiguity: you can make progress before the process exists, and you enjoy shaping how things should work The instinct to write things down: runbooks, KB articles, and status updates that keep people informed Nice to have Experience supporting a C-suite or board of directors at a company with significant security or compliance requirements AV depth beyond first response, such as broadcast, live events, or room design input Experience building runbooks, self-service content, or support automation Experience supporting AI/ML research environments or highly technical user bases Deadline to apply: None. Applications will be received on a rolling basis. The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: $180,000 — $265,000 USD Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed.  Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings. How we're different We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences. Come work with us! Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.

Location: Montreal, Canada The Platform Systems Verification and Validation (V&V) Engineer focuses on the satellite bus , ensuring the core infrastructure subsystems are fully verified and ready to support the payload. Role Overview This role focuses on the functional verification of the utility subsystems that keep the satellite alive and oriented. It ensures the core platform—including power, thermal control, propulsion, and pointing—operates flawlessly and responds correctly to the onboard flight software. Key Responsibilities & Tasks Bus Subsystem Verification : Test and validate critical platform subsystems, including Attitude Determination and Control (ADCS), Electrical Power Subsystems (EPS), Thermal Control (TCS), and Propulsion. Hardware-in-the-Loop (HIL) : Utilize HIL testbeds to simulate orbital dynamics and sensor inputs (star trackers, GPS, sun sensors) to verify ADCS pointing accuracy. Power & Harness Validation : Perform power budget verification, battery charge/discharge cycle testing, and solar array simulator (SAS) integration. Flight Software (FSW) Interfacing : Test command routing, telemetry packet generation, and fault detection, isolation, and recovery (FDIR) loops. Subsystem Interface Control : Verify that all physical, data (CAN, SpaceWire, RS-422), and electrical interfaces between platform subsystems are compliant. Candidate Prerequisites ("Must Haves") Experience : 5+ years verifying aerospace subsystems, embedded robotics, or satellite platform architectures. Technical Breadth : Strong knowledge of power electronics, orbital mechanics, thermal management, or control systems. Simulators & Automation : Experience with automated test scripting (Python/MATLAB) to control power supplies, loads, and simulated sensor inputs. Protocol Expertise : Familiarity with space-industry data protocols and avionics testing architectures.