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Location: San Francisco Workplace: Hybrid WHY HARVEY At Harvey, we’re transforming how legal and professional services operate. By combining frontier agentic AI, an enterprise-grade platform, and deep domain expertise, we’re reshaping how critical knowledge work gets done for decades to come. This is a rare chance to help build a generational company at a true inflection point. We have strong product-market fit and world-class investor support. We’re scaling fast and defining a new category in real time. The work is ambitious, the bar is high, and the opportunity for growth — personal, professional, and financial — is unmatched. Our team moves fast, takes ownership, and is deeply committed to the mission — operating with intensity, staying close to our customers, and pushing each other for excellence. We live by three values: Decisiveness, Simplicity, and Job's Not Finished. We act quickly on clear judgment over perfect information, we believe simplicity is what scales, and we're never satisfied with where we are. If you want to do the best work of your career alongside people who share that drive, we'd love to build with you. At Harvey, the future of professional services is being written today — and we’re just getting started. ROLE OVERVIEW In this role, you'll build the data and measurement systems that power our post-training, benchmarking, and agent capability efforts. We recently post-trained our first model built for legal work, and we’re now building the team to scale up our post-training efforts. You’ll own synthetic data pipelines, evaluation design, the tasks and grading behind training environments, and the systems that turn feedback from domain experts into training signals and rewards. These are tasks where correctness often can’t be verified automatically, so defining and measuring quality is a core research challenge. You’ll join a small, high-bar team of hands-on engineers and researchers, where each person owns a large part of the work. We’re looking for research engineers who can build reliable data and evaluation systems, question what a metric actually tells us, and work closely with researchers and legal experts to turn model failures into measurable improvements. Learn more about our work at harvey.ai/research http://harvey.ai/research. WHAT YOU'LL DO - Build synthetic data pipelines that generate, filter, and curate training and evaluation samples for complex legal reasoning and agentic workflows. - Design evaluations and verifiers that reflect real legal work, calibrate them against expert judgment, and investigate where they fail. - Build and maintain capability benchmarks like our Legal Agent Benchmark (LAB), along with product-focused evals that track how models perform on real customer workflows. - Work with legal domain experts to define quality metrics, rubrics, and other grading mechanisms for training and evaluation. - Own training and evaluation data quality, including provenance, coverage, and safeguards against contamination. - Close the loop from product usage and customer feedback to new evaluations and training data, so models improve where customers need them most. - Partner with researchers to investigate model failures, distinguish real gains from misleading metrics, and use findings to guide training and release decisions. WHAT YOU HAVE - Experience building data pipelines, ML evaluation systems, or experimentation tooling, with strong Python skills and ownership of production-quality code. - A rigorous approach to measurement, including statistical reasoning, experimental design, and understanding when a metric can mislead. - The ability to translate ambiguous goals or expert judgment into structured datasets, rubrics, and measurable criteria. - Experience with synthetic data, LLM evaluations, automated raters, or preference data is a plus. Strong data and measurement engineering experience close to ML model development is valuable even without prior LLM work. We are hiring for this role across multiple levels. COMPENSATION $230,000 - $450,000 USD DEPENDING ON YOUR LOCATION, AN APPLICANT PRIVACY NOTICE MAY APPLY TO YOU. YOU CAN FIND ALL OF OUR APPLICANT PRIVACY NOTICES HERE https://harveyai.notion.site/Harvey-Candidate-Privacy-Notices-319ac3fcdd7a803bb807d5094f249922?pvs=74. #LI-DZ1 Harvey is an equal opportunity employer and does not discriminate on the basis of race, gender, sexual orientation, gender identity/expression, national origin, disability, age, genetic information, veteran status, marital status, pregnancy or related condition, or any other basis protected by law. We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made by emailing accommodations@harvey.ai

Location: London Workplace: Hybrid WHY HARVEY At Harvey, we’re transforming how legal and professional services operate. By combining frontier agentic AI, an enterprise-grade platform, and deep domain expertise, we’re reshaping how critical knowledge work gets done for decades to come. This is a rare chance to help build a generational company at a true inflection point. We have strong product-market fit and world-class investor support. We’re scaling fast and defining a new category in real time. The work is ambitious, the bar is high, and the opportunity for growth — personal, professional, and financial — is unmatched. Our team moves fast, takes ownership, and is deeply committed to the mission — operating with intensity, staying close to our customers, and pushing each other for excellence. We live by three values: Decisiveness, Simplicity, and Job's Not Finished. We act quickly on clear judgment over perfect information, we believe simplicity is what scales, and we're never satisfied with where we are. If you want to do the best work of your career alongside people who share that drive, we'd love to build with you. At Harvey, the future of professional services is being written today — and we’re just getting started. ROLE OVERVIEW In this role, you'll build the data and measurement systems that power our post-training, benchmarking, and agent capability efforts. We recently post-trained our first model built for legal work, and we’re now building the team to scale up our post-training efforts. You’ll own synthetic data pipelines, evaluation design, the tasks and grading behind training environments, and the systems that turn feedback from domain experts into training signals and rewards. These are tasks where correctness often can’t be verified automatically, so defining and measuring quality is a core research challenge. You’ll join a small, high-bar team of hands-on engineers and researchers, where each person owns a large part of the work. We’re looking for research engineers who can build reliable data and evaluation systems, question what a metric actually tells us, and work closely with researchers and legal experts to turn model failures into measurable improvements. Learn more about our work at harvey.ai/research http://harvey.ai/research. WHAT YOU'LL DO - Build synthetic data pipelines that generate, filter, and curate training and evaluation samples for complex legal reasoning and agentic workflows. - Design evaluations and verifiers that reflect real legal work, calibrate them against expert judgment, and investigate where they fail. - Build and maintain capability benchmarks like our Legal Agent Benchmark (LAB), along with product-focused evals that track how models perform on real customer workflows. - Work with legal domain experts to define quality metrics, rubrics, and other grading mechanisms for training and evaluation. - Own training and evaluation data quality, including provenance, coverage, and safeguards against contamination. - Close the loop from product usage and customer feedback to new evaluations and training data, so models improve where customers need them most. - Partner with researchers to investigate model failures, distinguish real gains from misleading metrics, and use findings to guide training and release decisions. WHAT YOU HAVE - Experience building data pipelines, ML evaluation systems, or experimentation tooling, with strong Python skills and ownership of production-quality code. - A rigorous approach to measurement, including statistical reasoning, experimental design, and understanding when a metric can mislead. - The ability to translate ambiguous goals or expert judgment into structured datasets, rubrics, and measurable criteria. - Experience with synthetic data, LLM evaluations, automated raters, or preference data is a plus. Strong data and measurement engineering experience close to ML model development is valuable even without prior LLM work. We are hiring for this role across multiple levels. DEPENDING ON YOUR LOCATION, AN APPLICANT PRIVACY NOTICE MAY APPLY TO YOU. YOU CAN FIND ALL OF OUR APPLICANT PRIVACY NOTICES HERE https://harveyai.notion.site/Harvey-Candidate-Privacy-Notices-319ac3fcdd7a803bb807d5094f249922?pvs=74. #LI-DZ1 Harvey is an equal opportunity employer and does not discriminate on the basis of race, gender, sexual orientation, gender identity/expression, national origin, disability, age, genetic information, veteran status, marital status, pregnancy or related condition, or any other basis protected by law. We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made by emailing accommodations@harvey.ai

Location: Remote Workplace: Remote WHY HARVEY At Harvey, we’re transforming how legal and professional services operate. By combining frontier agentic AI, an enterprise-grade platform, and deep domain expertise, we’re reshaping how critical knowledge work gets done for decades to come. This is a rare chance to help build a generational company at a true inflection point. We have strong product-market fit and world-class investor support. We’re scaling fast and defining a new category in real time. The work is ambitious, the bar is high, and the opportunity for growth — personal, professional, and financial — is unmatched. Our team moves fast, takes ownership, and is deeply committed to the mission — operating with intensity, staying close to our customers, and pushing each other for excellence. We live by three values: Decisiveness, Simplicity, and Job's Not Finished. We act quickly on clear judgment over perfect information, we believe simplicity is what scales, and we're never satisfied with where we are. If you want to do the best work of your career alongside people who share that drive, we'd love to build with you. At Harvey, the future of professional services is being written today — and we’re just getting started. ROLE OVERVIEW In this role, you'll build the data and measurement systems that power our post-training, benchmarking, and agent capability efforts. We recently post-trained our first model built for legal work, and we’re now building the team to scale up our post-training efforts. You’ll own synthetic data pipelines, evaluation design, the tasks and grading behind training environments, and the systems that turn feedback from domain experts into training signals and rewards. These are tasks where correctness often can’t be verified automatically, so defining and measuring quality is a core research challenge. You’ll join a small, high-bar team of hands-on engineers and researchers, where each person owns a large part of the work. We’re looking for research engineers who can build reliable data and evaluation systems, question what a metric actually tells us, and work closely with researchers and legal experts to turn model failures into measurable improvements. Learn more about our work at harvey.ai/research http://harvey.ai/research. WHAT YOU'LL DO - Build synthetic data pipelines that generate, filter, and curate training and evaluation samples for complex legal reasoning and agentic workflows. - Design evaluations and verifiers that reflect real legal work, calibrate them against expert judgment, and investigate where they fail. - Build and maintain capability benchmarks like our Legal Agent Benchmark (LAB), along with product-focused evals that track how models perform on real customer workflows. - Work with legal domain experts to define quality metrics, rubrics, and other grading mechanisms for training and evaluation. - Own training and evaluation data quality, including provenance, coverage, and safeguards against contamination. - Close the loop from product usage and customer feedback to new evaluations and training data, so models improve where customers need them most. - Partner with researchers to investigate model failures, distinguish real gains from misleading metrics, and use findings to guide training and release decisions. WHAT YOU HAVE - Experience building data pipelines, ML evaluation systems, or experimentation tooling, with strong Python skills and ownership of production-quality code. - A rigorous approach to measurement, including statistical reasoning, experimental design, and understanding when a metric can mislead. - The ability to translate ambiguous goals or expert judgment into structured datasets, rubrics, and measurable criteria. - Experience with synthetic data, LLM evaluations, automated raters, or preference data is a plus. Strong data and measurement engineering experience close to ML model development is valuable even without prior LLM work. We are hiring for this role across multiple levels. COMPENSATION $230,000 - $450,000 USD DEPENDING ON YOUR LOCATION, AN APPLICANT PRIVACY NOTICE MAY APPLY TO YOU. YOU CAN FIND ALL OF OUR APPLICANT PRIVACY NOTICES HERE https://harveyai.notion.site/Harvey-Candidate-Privacy-Notices-319ac3fcdd7a803bb807d5094f249922?pvs=74. #LI-DZ1 Harvey is an equal opportunity employer and does not discriminate on the basis of race, gender, sexual orientation, gender identity/expression, national origin, disability, age, genetic information, veteran status, marital status, pregnancy or related condition, or any other basis protected by law. We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made by emailing accommodations@harvey.ai

Location: New York Workplace: Hybrid WHY HARVEY At Harvey, we’re transforming how legal and professional services operate. By combining frontier agentic AI, an enterprise-grade platform, and deep domain expertise, we’re reshaping how critical knowledge work gets done for decades to come. This is a rare chance to help build a generational company at a true inflection point. We have strong product-market fit and world-class investor support. We’re scaling fast and defining a new category in real time. The work is ambitious, the bar is high, and the opportunity for growth — personal, professional, and financial — is unmatched. Our team moves fast, takes ownership, and is deeply committed to the mission — operating with intensity, staying close to our customers, and pushing each other for excellence. We live by three values: Decisiveness, Simplicity, and Job's Not Finished. We act quickly on clear judgment over perfect information, we believe simplicity is what scales, and we're never satisfied with where we are. If you want to do the best work of your career alongside people who share that drive, we'd love to build with you. At Harvey, the future of professional services is being written today — and we’re just getting started. ROLE OVERVIEW In this role, you'll build the data and measurement systems that power our post-training, benchmarking, and agent capability efforts. We recently post-trained our first model built for legal work, and we’re now building the team to scale up our post-training efforts. You’ll own synthetic data pipelines, evaluation design, the tasks and grading behind training environments, and the systems that turn feedback from domain experts into training signals and rewards. These are tasks where correctness often can’t be verified automatically, so defining and measuring quality is a core research challenge. You’ll join a small, high-bar team of hands-on engineers and researchers, where each person owns a large part of the work. We’re looking for research engineers who can build reliable data and evaluation systems, question what a metric actually tells us, and work closely with researchers and legal experts to turn model failures into measurable improvements. Learn more about our work at harvey.ai/research http://harvey.ai/research. WHAT YOU'LL DO - Build synthetic data pipelines that generate, filter, and curate training and evaluation samples for complex legal reasoning and agentic workflows. - Design evaluations and verifiers that reflect real legal work, calibrate them against expert judgment, and investigate where they fail. - Build and maintain capability benchmarks like our Legal Agent Benchmark (LAB), along with product-focused evals that track how models perform on real customer workflows. - Work with legal domain experts to define quality metrics, rubrics, and other grading mechanisms for training and evaluation. - Own training and evaluation data quality, including provenance, coverage, and safeguards against contamination. - Close the loop from product usage and customer feedback to new evaluations and training data, so models improve where customers need them most. - Partner with researchers to investigate model failures, distinguish real gains from misleading metrics, and use findings to guide training and release decisions. WHAT YOU HAVE - Experience building data pipelines, ML evaluation systems, or experimentation tooling, with strong Python skills and ownership of production-quality code. - A rigorous approach to measurement, including statistical reasoning, experimental design, and understanding when a metric can mislead. - The ability to translate ambiguous goals or expert judgment into structured datasets, rubrics, and measurable criteria. - Experience with synthetic data, LLM evaluations, automated raters, or preference data is a plus. Strong data and measurement engineering experience close to ML model development is valuable even without prior LLM work. We are hiring for this role across multiple levels. COMPENSATION $230,000 - $450,000 USD DEPENDING ON YOUR LOCATION, AN APPLICANT PRIVACY NOTICE MAY APPLY TO YOU. YOU CAN FIND ALL OF OUR APPLICANT PRIVACY NOTICES HERE https://harveyai.notion.site/Harvey-Candidate-Privacy-Notices-319ac3fcdd7a803bb807d5094f249922?pvs=74. #LI-DZ1 Harvey is an equal opportunity employer and does not discriminate on the basis of race, gender, sexual orientation, gender identity/expression, national origin, disability, age, genetic information, veteran status, marital status, pregnancy or related condition, or any other basis protected by law. We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made by emailing accommodations@harvey.ai

Location: San Francisco Workplace: Hybrid WHY HARVEY At Harvey, we’re transforming how legal and professional services operate. By combining frontier agentic AI, an enterprise-grade platform, and deep domain expertise, we’re reshaping how critical knowledge work gets done for decades to come. This is a rare chance to help build a generational company at a true inflection point. We have strong product-market fit and world-class investor support. We’re scaling fast and defining a new category in real time. The work is ambitious, the bar is high, and the opportunity for growth — personal, professional, and financial — is unmatched. Our team moves fast, takes ownership, and is deeply committed to the mission — operating with intensity, staying close to our customers, and pushing each other for excellence. We live by three values: Decisiveness, Simplicity, and Job's Not Finished. We act quickly on clear judgment over perfect information, we believe simplicity is what scales, and we're never satisfied with where we are. If you want to do the best work of your career alongside people who share that drive, we'd love to build with you. At Harvey, the future of professional services is being written today — and we’re just getting started. ROLE OVERVIEW In this role, you'll own how we adapt and improve leading open-weight models for legal work. We recently post-trained our first model built for legal work, and we’re now building the team to scale up our post-training efforts. You’ll own the post-training recipe across SFT and RL, including reward design and verification for challenging, semi-verifiable legal tasks. You’ll help set the research direction and carry ideas from experiments through training runs to models used by customers. You’ll join a small, high-bar team of hands-on engineers and researchers, where each person owns a large part of the work. We’re looking for research engineers and scientists who have gotten training runs working at scale, are immersed in RL for language models, and want their work to reach users quickly. Learn more about our work at harvey.ai/research http://harvey.ai/research. WHAT YOU'LL DO - Own and pursue a research agenda to improve model capability on long-horizon legal reasoning and agentic workflows. - Design and run post-training experiments across SFT and RL, building on leading open-weight models. - Develop reward functions and verifiers for semi-verifiable tasks, including rubrics, learned preference models, and expert preference data, and study where they break. - Run and debug training at scale, addressing divergence, reward hacking, and instability. - Build and use rigorous evals to track improvements and decide what ships. - Design, implement, test, and debug code across our research stack. WHAT YOU HAVE - Hands-on experience post-training LLMs with SFT and RL, including preference optimization, and direct ownership of training runs and results. - Deep ML fundamentals and experience getting distributed training runs working reliably. - Strong engineering skills in Python, with production-quality code and end-to-end system ownership. - Research experience in RL for language models or reward modeling, with the ability to move quickly from experiments to shipped improvements. Publications welcome, not required. We are hiring for this role across multiple levels. COMPENSATION $230,000 - $450,000 USD DEPENDING ON YOUR LOCATION, AN APPLICANT PRIVACY NOTICE MAY APPLY TO YOU. YOU CAN FIND ALL OF OUR APPLICANT PRIVACY NOTICES HERE https://harveyai.notion.site/Harvey-Candidate-Privacy-Notices-319ac3fcdd7a803bb807d5094f249922?pvs=74. #LI-DZ1 Harvey is an equal opportunity employer and does not discriminate on the basis of race, gender, sexual orientation, gender identity/expression, national origin, disability, age, genetic information, veteran status, marital status, pregnancy or related condition, or any other basis protected by law. We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made by emailing accommodations@harvey.ai

Location: Remote Workplace: Remote WHY HARVEY At Harvey, we’re transforming how legal and professional services operate. By combining frontier agentic AI, an enterprise-grade platform, and deep domain expertise, we’re reshaping how critical knowledge work gets done for decades to come. This is a rare chance to help build a generational company at a true inflection point. We have strong product-market fit and world-class investor support. We’re scaling fast and defining a new category in real time. The work is ambitious, the bar is high, and the opportunity for growth — personal, professional, and financial — is unmatched. Our team moves fast, takes ownership, and is deeply committed to the mission — operating with intensity, staying close to our customers, and pushing each other for excellence. We live by three values: Decisiveness, Simplicity, and Job's Not Finished. We act quickly on clear judgment over perfect information, we believe simplicity is what scales, and we're never satisfied with where we are. If you want to do the best work of your career alongside people who share that drive, we'd love to build with you. At Harvey, the future of professional services is being written today — and we’re just getting started. ROLE OVERVIEW In this role, you'll own how we adapt and improve leading open-weight models for legal work. We recently post-trained our first model built for legal work, and we’re now building the team to scale up our post-training efforts. You’ll own the post-training recipe across SFT and RL, including reward design and verification for challenging, semi-verifiable legal tasks. You’ll help set the research direction and carry ideas from experiments through training runs to models used by customers. You’ll join a small, high-bar team of hands-on engineers and researchers, where each person owns a large part of the work. We’re looking for research engineers and scientists who have gotten training runs working at scale, are immersed in RL for language models, and want their work to reach users quickly. Learn more about our work at harvey.ai/research http://harvey.ai/research. WHAT YOU'LL DO - Own and pursue a research agenda to improve model capability on long-horizon legal reasoning and agentic workflows. - Design and run post-training experiments across SFT and RL, building on leading open-weight models. - Develop reward functions and verifiers for semi-verifiable tasks, including rubrics, learned preference models, and expert preference data, and study where they break. - Run and debug training at scale, addressing divergence, reward hacking, and instability. - Build and use rigorous evals to track improvements and decide what ships. - Design, implement, test, and debug code across our research stack. WHAT YOU HAVE - Hands-on experience post-training LLMs with SFT and RL, including preference optimization, and direct ownership of training runs and results. - Deep ML fundamentals and experience getting distributed training runs working reliably. - Strong engineering skills in Python, with production-quality code and end-to-end system ownership. - Research experience in RL for language models or reward modeling, with the ability to move quickly from experiments to shipped improvements. Publications welcome, not required. We are hiring for this role across multiple levels. COMPENSATION $230,000 - $450,000 USD DEPENDING ON YOUR LOCATION, AN APPLICANT PRIVACY NOTICE MAY APPLY TO YOU. YOU CAN FIND ALL OF OUR APPLICANT PRIVACY NOTICES HERE https://harveyai.notion.site/Harvey-Candidate-Privacy-Notices-319ac3fcdd7a803bb807d5094f249922?pvs=74. #LI-DZ1 Harvey is an equal opportunity employer and does not discriminate on the basis of race, gender, sexual orientation, gender identity/expression, national origin, disability, age, genetic information, veteran status, marital status, pregnancy or related condition, or any other basis protected by law. We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made by emailing accommodations@harvey.ai

Location: London Workplace: Hybrid WHY HARVEY At Harvey, we’re transforming how legal and professional services operate. By combining frontier agentic AI, an enterprise-grade platform, and deep domain expertise, we’re reshaping how critical knowledge work gets done for decades to come. This is a rare chance to help build a generational company at a true inflection point. We have strong product-market fit and world-class investor support. We’re scaling fast and defining a new category in real time. The work is ambitious, the bar is high, and the opportunity for growth — personal, professional, and financial — is unmatched. Our team moves fast, takes ownership, and is deeply committed to the mission — operating with intensity, staying close to our customers, and pushing each other for excellence. We live by three values: Decisiveness, Simplicity, and Job's Not Finished. We act quickly on clear judgment over perfect information, we believe simplicity is what scales, and we're never satisfied with where we are. If you want to do the best work of your career alongside people who share that drive, we'd love to build with you. At Harvey, the future of professional services is being written today — and we’re just getting started. ROLE OVERVIEW In this role, you'll own how we adapt and improve leading open-weight models for legal work. We recently post-trained our first model built for legal work, and we’re now building the team to scale up our post-training efforts. You’ll own the post-training recipe across SFT and RL, including reward design and verification for challenging, semi-verifiable legal tasks. You’ll help set the research direction and carry ideas from experiments through training runs to models used by customers. You’ll join a small, high-bar team of hands-on engineers and researchers, where each person owns a large part of the work. We’re looking for research engineers and scientists who have gotten training runs working at scale, are immersed in RL for language models, and want their work to reach users quickly. Learn more about our work at harvey.ai/research http://harvey.ai/research. WHAT YOU'LL DO - Own and pursue a research agenda to improve model capability on long-horizon legal reasoning and agentic workflows. - Design and run post-training experiments across SFT and RL, building on leading open-weight models. - Develop reward functions and verifiers for semi-verifiable tasks, including rubrics, learned preference models, and expert preference data, and study where they break. - Run and debug training at scale, addressing divergence, reward hacking, and instability. - Build and use rigorous evals to track improvements and decide what ships. - Design, implement, test, and debug code across our research stack. WHAT YOU HAVE - Hands-on experience post-training LLMs with SFT and RL, including preference optimization, and direct ownership of training runs and results. - Deep ML fundamentals and experience getting distributed training runs working reliably. - Strong engineering skills in Python, with production-quality code and end-to-end system ownership. - Research experience in RL for language models or reward modeling, with the ability to move quickly from experiments to shipped improvements. Publications welcome, not required. We are hiring for this role across multiple levels. DEPENDING ON YOUR LOCATION, AN APPLICANT PRIVACY NOTICE MAY APPLY TO YOU. YOU CAN FIND ALL OF OUR APPLICANT PRIVACY NOTICES HERE https://harveyai.notion.site/Harvey-Candidate-Privacy-Notices-319ac3fcdd7a803bb807d5094f249922?pvs=74. #LI-DZ1 Harvey is an equal opportunity employer and does not discriminate on the basis of race, gender, sexual orientation, gender identity/expression, national origin, disability, age, genetic information, veteran status, marital status, pregnancy or related condition, or any other basis protected by law. We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made by emailing accommodations@harvey.ai

Location: New York Workplace: Hybrid WHY HARVEY At Harvey, we’re transforming how legal and professional services operate. By combining frontier agentic AI, an enterprise-grade platform, and deep domain expertise, we’re reshaping how critical knowledge work gets done for decades to come. This is a rare chance to help build a generational company at a true inflection point. We have strong product-market fit and world-class investor support. We’re scaling fast and defining a new category in real time. The work is ambitious, the bar is high, and the opportunity for growth — personal, professional, and financial — is unmatched. Our team moves fast, takes ownership, and is deeply committed to the mission — operating with intensity, staying close to our customers, and pushing each other for excellence. We live by three values: Decisiveness, Simplicity, and Job's Not Finished. We act quickly on clear judgment over perfect information, we believe simplicity is what scales, and we're never satisfied with where we are. If you want to do the best work of your career alongside people who share that drive, we'd love to build with you. At Harvey, the future of professional services is being written today — and we’re just getting started. ROLE OVERVIEW In this role, you’ll own how we adapt and improve leading open-weight models for legal work. We recently post-trained our first model built for legal work, and we’re now building the team to scale up our post-training efforts. You’ll own the post-training recipe across SFT and RL, including reward design and verification for challenging, semi-verifiable legal tasks. You’ll help set the research direction and carry ideas from experiments through training runs to models used by customers. You’ll join a small, high-bar team of hands-on engineers and researchers, where each person owns a large part of the work. We’re looking for research engineers and scientists who have gotten training runs working at scale, are immersed in RL for language models, and want their work to reach users quickly. Learn more about our work at harvey.ai/research http://harvey.ai/research. WHAT YOU'LL DO - Own and pursue a research agenda to improve model capability on long-horizon legal reasoning and agentic workflows. - Design and run post-training experiments across SFT and RL, building on leading open-weight models. - Develop reward functions and verifiers for semi-verifiable tasks, including rubrics, learned preference models, and expert preference data, and study where they break. - Run and debug training at scale, addressing divergence, reward hacking, and instability. - Build and use rigorous evals to track improvements and decide what ships. - Design, implement, test, and debug code across our research stack. WHAT YOU HAVE - Hands-on experience post-training LLMs with SFT and RL, including preference optimization, and direct ownership of training runs and results. - Deep ML fundamentals and experience getting distributed training runs working reliably. - Strong engineering skills in Python, with production-quality code and end-to-end system ownership. - Research experience in RL for language models or reward modeling, with the ability to move quickly from experiments to shipped improvements. Publications welcome, not required. We are hiring for this role across multiple levels. COMPENSATION $230,000 - $450,000 USD DEPENDING ON YOUR LOCATION, AN APPLICANT PRIVACY NOTICE MAY APPLY TO YOU. YOU CAN FIND ALL OF OUR APPLICANT PRIVACY NOTICES HERE https://harveyai.notion.site/Harvey-Candidate-Privacy-Notices-319ac3fcdd7a803bb807d5094f249922?pvs=74. #LI-DZ1 Harvey is an equal opportunity employer and does not discriminate on the basis of race, gender, sexual orientation, gender identity/expression, national origin, disability, age, genetic information, veteran status, marital status, pregnancy or related condition, or any other basis protected by law. We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made by emailing accommodations@harvey.ai

Location: Москва **О команде** Мы занимаемся независимой оценкой качества и модельных рисков Kandinsky – семейства генеративных моделей для создания и редактирования изображений, видео и аудиовизуального контента, а также моделей мира (world models). Наша задача – находить слабые места моделей, проверять выбранные подходы к оценке и готовить рекомендации, которые помогают принимать решения о релизе и возможных сценариях применения моделей. Мы работаем с новыми версиями Kandinsky до их выхода к пользователям. Разрабатываем собственные методологии, бенчмарки и инструменты полного цикла, объединяя автоматические метрики, human evaluation и подходы LLM/VLM-as-a-judge. **О роли** Мы ищем специалиста на стыке исследований и инженерной разработки. В этой работе не всегда существует готовая методология или универсальная метрика. Нужно определить, из чего складывается качество модели, спроектировать проверяемый эксперимент и реализовать воспроизводимый evaluation-пайплайн. Роль охватывает весь цикл оценки: от формулирования критериев и подготовки тестовых данных до анализа результатов и рекомендаций для команды разработки. Важно не только измерить качество модели, но и понять, насколько она подходит для реальных пользовательских и бизнес-сценариев, какие ограничения критичны и что осталось непроверенным. Обязанности **Проектировать оценку с нуля** * декомпозировать качество на измеримые критерии и собирать под них тестовые сценарии, промпты и выборки * проектировать оценку там, где готовой методологии ещё нет. **Разрабатывать evaluation-пайплайны** * реализовывать на Python инструменты для запуска моделей, сбора результатов и расчёта метрик * обеспечивать воспроизводимость экспериментов и развивать внутреннюю библиотеку оценки * готовить инструкции для асессоров и контролировать качество разметки. **Строить VLM-as-a-judge** * разрабатывать судей под конкретные критерии, учитывать их bias и ограничения * понимать, где автоматическая оценка работает, а где без человека нельзя. **Разбираться в ошибках моделей** * понимать ограничения diffusion-моделей и причины типовых артефактов * отличать реальную проблему модели от ошибки методологии или замера. **Формировать заключение по модельному риску** * анализировать качество по отдельным сценариям, типам промптов и срезам, а не только по среднему значению * формулировать условия релиза, ограничения и явно фиксировать то, что осталось непроверенным. Требования **Хорошее понимание устройства генеративных моделей**: * понимание принципов работы Transformers, diffusion- и мультимодальных моделей и основных задач: T2I, Image Editing, T2V, Audio, Video-Audio. * понимание типовых ограничений, ошибок и артефактов моделей * способность разбираться в причинах ошибок и способах их проверки. **Способность проектировать оценку качества моделей**: * декомпозировать качество на измеримые критерии и проектировать тестовые сценарии, промпты и выборки. * выбирать корректный подход к оценке: human evaluation, автоматические метрики, LLM/VLM-as-a-judge, а также подходящие модели для сравнения; * проверять валидность метрик и уметь проектировать оценку для новых задач и модальностей, где ещё нет готовой методологии. **Навыки работы с LLM и VLM:** * prompt engineering, structured output, tool calling и работа с контекстом и ограничениями моделей * построение и валидация LLM/VLM-as-a-judge, включая проверку автоматической оценки относительно человеческой * понимание нестабильности, bias и галлюцинаций моделей * умение подбирать модель под конкретную исследовательскую задачу. **Понимание AI-агентов и агентных систем**: * понимать, чем агент отличается от обычного вызова LLM; * знать основные компоненты агентной системы: роли, инструменты, контекст, память и оркестрация * понимать, когда агентный подход действительно полезен, а когда создаёт лишнюю сложность * уметь оценивать надёжность и ограничения агентных решений. **Умение проводить технический и научный ресерч**: * находить и критически разбирать актуальные статьи, бенчмарки, модели и реализации * сравнивать решения с актуальными подходами и оценивать достоверность результатов * переводить ресерч в конкретные идеи для тестирования и развития внутренних подходов. **Будет плюсом** **Умение работать с агентами и субагентами**: * декомпозировать задачи между агентами, задавая им чёткие роли и зоны ответственности * управлять контекстом и изоляцией субагентов, организовывать проверку результатов * собирать результаты нескольких агентов в единое решение и критически оценивать их выводы * **Навыки вайбкодинга и AI-assisted coding**: * уверенно работать с Codex, Claude Code и другими coding-агентами * проверять и дорабатывать сгенерированный код, использовать агентов для тестирования * понимать архитектуру создаваемого решения **Продуктовое мышление**: * связывать тестирование с реальными пользовательскими сценариями и понимать, какие ошибки критичны для продукта * приоритизировать направления проверки и переводить результаты оценки в рекомендации для разработки * понимать влияние результатов на релиз и дальнейшее развитие модели. Условия * комфортный современный офис рядом с м. Кутузовская * возможность выбрать удобный график - офис / гибрид * ежегодный пересмотр зарплаты и годовая премия * корпоративный спортзал и зоны отдыха * более 400 образовательных программ СберУниверситета для профессионального и карьерного развития * расширенный ДМС, льготное страхование для семьи и корпоративная пенсионная программа * гибкий дисконт по ипотечному кредиту, равный 1/3 ключевой ставки ЦБ * подписка Прайм с возможностью совместного использования на трёх близких * скидки на продукты компаний-партнеров * вознаграждение за рекомендацию друзей в команду Сбера

Ищем кванта, который сам закрывает полный цикл ресерча. Кого ищем 🔍 Senior Quant/ML Researcher, который закрывает полный цикл ресерча сам: от гипотезы и данных до бэктеста, валидации и результата.

Ищем кванта, который сам закрывает полный цикл ресерча. Кого ищем 🔍 Senior Quant/ML Researcher, который закрывает полный цикл ресерча сам: от гипотезы и данных до бэктеста, валидации и результата.

Ищем кванта, который сам закрывает полный цикл ресерча. Кого ищем 🔍 Senior Quant/ML Researcher, который закрывает полный цикл ресерча сам: от гипотезы и данных до бэктеста, валидации и результата.

Location: Remote-Friendly (Travel-Required) | San Francisco, CA | Seattle, WA | 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 is at the forefront of AI research, dedicated to developing safe, ethical, and powerful artificial intelligence. Our mission is to ensure that transformative AI systems are aligned with human interests. We are seeking a Research Engineer to join our Pre-training team, responsible for developing the next generation of large language models. In this role, you will work at the intersection of cutting-edge research and practical engineering, contributing to the development of safe, steerable, and trustworthy AI systems. Key Responsibilities Conduct research and implement solutions in areas such as model architecture, algorithms, data processing, and optimizer development Independently lead small research projects while collaborating with team members on larger initiatives Design, run, and analyze scientific experiments to advance our understanding of large language models Optimize and scale our training infrastructure to improve efficiency and reliability Develop and improve dev tooling to enhance team productivity Contribute to the entire stack, from low-level optimizations to high-level model design Qualifications Advanced degree (MS or PhD) in Computer Science, Machine Learning, or a related field Strong software engineering skills with a proven track record of building complex systems Expertise in Python and experience with deep learning frameworks (PyTorch preferred) Familiarity with large-scale machine learning, particularly in the context of language models Ability to balance research goals with practical engineering constraints Strong problem-solving skills and a results-oriented mindset Excellent communication skills and ability to work in a collaborative environment Care about the societal impacts of your work Preferred Experience Work on high-performance, large-scale ML systems Familiarity with GPUs, Kubernetes, and OS internals Experience with language modeling using transformer architectures Knowledge of reinforcement learning techniques Background in large-scale ETL processes You'll thrive in this role if you Have significant software engineering experience Are results-oriented with a bias towards flexibility and impact Willingly take on tasks outside your job description to support the team Enjoy pair programming and collaborative work Are eager to learn more about machine learning research Are enthusiastic to work at an organization that functions as a single, cohesive team pursuing large-scale AI research projects Are working to align state of the art models with human values and preferences, understand and interpret deep neural networks, or develop new models to support these areas of research View research and engineering as two sides of the same coin, and seek to understand all aspects of our research program as well as possible, to maximize the impact of your insights Have ambitious goals for AI safety and general progress in the next few years, and you’re working to create the best outcomes over the long-term. Sample Projects Optimizing the throughput of novel attention mechanisms Comparing compute efficiency of different Transformer variants Preparing large-scale datasets for efficient model consumption Scaling distributed training jobs to thousands of GPUs Designing fault tolerance strategies for our training infrastructure Creating interactive visualizations of model internals, such as attention patterns 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: $500,000 — $850,000 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. For roles performing work in California: We will consider for employment all qualified applicants, including those with arrest or conviction records or criminal histories, in a manner consistent with the requirements of applicable state and local laws, including the San Francisco Fair Chance Ordinance, the City of Los Angeles’ Fair Chance Initiative for Hiring Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act. 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 Workplace: OnSite ABOUT US Sierra is the leading platform for customer-facing AI agents, working with many of the world's biggest brands — including The GAP, Rocket Mortgage, SoFi, Sutter Health, and SoftBank — to transform how they serve customers and grow their businesses. We are primarily an in-person company based in San Francisco, with growing offices across North America, Europe, and Asia. We are guided by a set of values that are at the core of our actions and define our culture: Trust, Customer Obsession, Craftsmanship, Intensity, and a commitment to balancing Family along the way. These values are the foundation of our work, and we are committed to upholding them in everything we do. Sierra was co-founded by Bret Taylor https://www.linkedin.com/in/brettaylor/ and Clay Bavor https://www.linkedin.com/in/claybavor/ who first met while working together at Google over 20 years ago. Bret currently serves as Chairman of the Board of Open AI. Previously, he was co-CEO of Salesforce and CTO of Facebook. Clay spent 18 years at Google, where he most recently led Google Labs. Sierra’s Applied Research team works to make the models behind our agents better, faster, and cheaper across many kinds of tasks. Our work covers post-training open-weight models for specialized capabilities, new approaches to voice AI, and the benchmarks the field uses to measure agents, including τ-bench. As an Applied Research Intern, you’ll join the team for a research project, working with a mentor, you’ll pick a problem that matters to how AI agents perform in the real world, run rigorous experiments, and potentially write up what you find for a top venue. If you want your research to reach both the research community and production agents used by millions of people, come do it at Sierra. WHAT YOU’LL DO - Own a research project from start to finish, from framing the question and running experiments to writing and submitting a paper. - Push agent evaluation forward, for example by extending τ-bench to new domains, modalities, or long-horizon tasks, or by building new ways to measure reliability and consistency. - Study how agents reason, use tools, and follow policies over multi-turn conversations, and find out where and why they fail. - Explore post-training methods such as supervised fine-tuning and reinforcement learning to improve agent behavior, and measure the gains rigorously. - Work with large-scale, proprietary datasets of text and audio interactions to find research questions grounded in real customer problems. - Work with researchers and engineers across Sierra, and where it makes sense, turn your findings into improvements in production. WHAT YOU’LL BRING - Currently pursuing a PhD in computer science, machine learning, or a related field. - A research track record, such as first-author publications or submissions at venues like NeurIPS, ICML, ICLR, ACL, EMNLP, Interspeech or ICASSP. - Strong experimental skills: you design clean experiments, build careful baselines, and are honest about what the results do and do not show. - Fluency in Python and modern ML tooling (for example, PyTorch, and LLM inference and training stacks). - High agency and a builder’s mindset. You’ll work across the stack to get an experiment running. - Clear, direct communication, both in writing and when presenting research. EVEN BETTER - Research experience with LLM agents, tool use, or multi-turn dialogue. - Experience designing benchmarks or evaluations, especially ones tied to real-world outcomes. - Experience with supervised fine-tuning, reinforcement learning, or long-horizon optimization. - Experience with speech, voice, or multimodal models. - Open-source contributions to ML research code or benchmarks. INTERN QUALIFICATIONS - The internship is open to students currently enrolled in a PhD program. - Duration and start dates are flexible. Interns are embedded with the Applied Research team and work under the guidance of a mentor. - The internship is based in our San Francisco office. We are in person Monday through Thursday. OUR VALUES - Trust: We build trust with our customers with our accountability, empathy, quality, and responsiveness. We build trust in AI by making it more accessible, safe, and useful. We build trust with each other by showing up for each other professionally and personally, creating an environment that enables all of us to do our best work. - Customer Obsession: We deeply understand our customers’ business goals and relentlessly focus on driving outcomes, not just technical milestones. Everyone at the company knows and spends time with our customers. When our customer is having an issue, we drop everything and fix it. - Craftsmanship: We get the details right, from the words on the page to the system architecture. We have good taste. When we notice something isn’t right, we take the time to fix it. We are proud of the products we produce. We continuously self-reflect to continuously self-improve. - Intensity: We know we don’t have the luxury of patience. We play to win. We care about our product being the best, and when it isn’t, we fix it. When we fail, we talk about it openly and without blame so we succeed the next time. - Family: We know that balance and intensity are compatible, and we model it in our actions and processes. We are the best technology company for parents. We support and respect each other and celebrate each other’s personal and professional achievements. WHAT WE OFFER We want our benefits to reflect our values and offer the following to full-time employees: - Flexible (unlimited) paid time off - Medical, dental, and vision benefits for you and your family - Life insurance and disability benefits - Retirement plan dependent on country of employment - Parental leave - Fertility and family building benefits - Lunch, as well as delicious snacks and coffee to keep you energized when working onsite - Discretionary benefit stipend giving people the ability to spend where it matters most - Free alphorn lessons These benefits are further detailed in Sierra's policies, may vary by region and employment type, and are subject to change at any time, consistent with the terms of any applicable compensation or benefits plans. Eligible full-time employees can participate in Sierra's equity plans subject to the terms of the applicable plans and policies. BE YOU, WITH US We're working to bring the transformative power of AI to every organization in the world. To do so, it is important to us that the diversity of our employees represents the diversity of our customers. We believe that our work and culture are better when we encourage, support, and respect different skills and experiences represented within our team. We encourage you to apply through the link on this page even if your experience doesn't precisely match the job description. Sierra is a proud equal opportunity employer. All qualified applicants will receive consideration for employment without regard to any characteristic protected by applicable federal, state or local laws, including veteran status and disability status. Hiring decisions are made by people, based on merit. If you need a reasonable accommodation for a disability, a religious practice, pregnancy or any related condition at any point in the application or interview process, contact us at accommodations@sierra.ai. Requests are handled confidentially and do not affect your candidacy. We will consider qualified applicants with arrest and conviction records in accordance with applicable fair chance laws.

Location: Worldwide APPLICATION INSTRUCTIONS: If you’ve already completed the 20four7VA Recruiting Process—including the Final Interview—or have previously been contracted by a 20four7VA Client, please make sure to apply via the 20four7VA Team Portal for faster processing. Use any valid email address to log in. If you’ve just logged in to the Team Portal, please proceed to review the Independent Contractor Opportunity outlined below. 💡 Quick Tip: Upload your resume first—this will automatically populate most of the fields in the application form. Let the magic happen! If you're interested in this opportunity, click APPLY TO POSITION to proceed. ******************************************************************** Title: Independent Contractor – CPT-11675 Research & Cold Calling VA /Business Support VA/Entry-Senior Level (Remote). Overview 20four7VA is seeking offshore independent contractors to deliver remote services to clients worldwide, with a primary focus on developed markets such as the United States, the United Kingdom, Canada, and Australia. A Maryland-based commercial real estate broker is seeking a part-time Commercial Real Estate Research & Outreach Virtual Assistant to support the targeted marketing of a high-value church property located in Bethesda, Maryland. The primary objective of this role is to research financially qualified churches, ministries, religious organizations, and schools that may have an interest in acquiring a marquee property near Washington, D.C. The VA will identify the appropriate decision-makers, make introductory calls, obtain permission for the broker to send the property's offering memorandum, and maintain organized records of all outreach. This is not a traditional sales or closing role. The VA's responsibility is to create qualified introductions and provide the broker with accurate contact information and permission to follow up directly. The objective of each engagement is to assist the client in improving business efficiency, enhancing service delivery, or supporting operational functions through high-quality, task-specific services rendered by a qualified contractor. Scope of Services Contractors will be required to provide services that align with the following client tasks: Prospect Research Review and organize the client-provided database of churches and related organizations Research additional local, regional, national, and potentially international churches, ministries, and schools Identify organizations with the potential financial capacity and strategic interest to purchase the property Research leadership teams, real estate contacts, expansion plans, and organizational structure Identify the appropriate decision-maker for property acquisition discussions Expand prospect lists beyond Maryland and Washington, D.C., prioritizing Virginia if current local outreach does not produce sufficient prospects. Research and build lists of large churches across the United States, including growing congregations from Ethiopian, Korean, Ghanaian, and other international communities. Include embassies as potential property prospects and expand the prospect database as existing leads are exhausted Introductory Outreach Call churches, ministries, schools, and related organizations using an approved script Introduce the property at a high level without conducting the full sales presentation Determine the correct person responsible for real estate, expansion, finance, or executive decisions Obtain permission for the broker to send the offering memorandum and property materials Collect accurate names, titles, phone numbers, and email addresses Escalate qualified or interested contacts to the broker for direct follow-up Make reasonable efforts to reach the actual decision-maker for real estate acquisition or property-related decisions rather than relying solely on receptionists or general office contacts. If the decision-maker cannot be reached after reasonable attempts, obtain the name, direct email address, and phone number of the person who agrees to receive the property information so the broker can follow up with a specific contact. Use a respectful, non-aggressive approach when navigating gatekeepers and requesting access to decision-makers Lead Tracking & Documentation Maintain an organized outreach tracker in Google Sheets Record research findings, call outcomes, decision-maker information, and next steps Track whether permission was granted to send property materials Avoid duplicate outreach and maintain accurate follow-up status Provide regular summaries of completed research and qualified contacts Systems & Process Support Assist with setting up and using Google Voice or another approved calling system Follow established scripts, research criteria, and outreach procedures Help document a repeatable prospecting process that may later be used for other commercial real estate listings Support related research and outreach projects as assigned Outbound Calling & Appointment Setting Conduct consistent, high-volume outbound calls to targeted churches, ministries, schools, and related organizations. Qualify prospects during initial calls and identify appropriate decision-makers and potential opportunities. Set qualified appointments or broker follow-up calls with interested prospects. Complete timely follow-up calls and outreach based on established follow-up schedules. Maintain accurate and up-to-date records of calls, qualified leads, appointments, follow-ups, and next steps in the Google Sheets tracker. Meet mutually agreed performance targets for outbound call volume, qualified leads, appointments set, follow-up activity, and tracker accuracy. Aim for approximately 50 outbound calls per day as a target, adjusting reasonably when longer conversations or meaningful progress affect call volume. Prioritize meaningful conversations and qualified contacts over call counts or permissions obtained from contacts unlikely to forward the property information to the appropriate decision-maker. Each engagement is based on a defined scope of work and is governed by an independent contractor agreement. Services must be performed professionally, with adherence to any timelines, guidelines, and deliverables agreed upon in writing. Skills The Contractor must demonstrate proficiency in the following, depending on the Client’s requirements: Proven cold calling and outbound calling experience required. Strong appointment setting and lead qualification skills required. Experience in lead generation, telemarketing, inside sales, or business development preferred. Ability to handle high-volume calls with a professional, conversational approach. Ability to identify qualified leads and decision-makers during calls. Strong objection handling, persistence, and follow-up skills. Ability to follow scripts while adapting naturally to conversations. Strong spoken and written English communication skills. Proficient in Google Sheets and spreadsheets. Commercial real estate experience preferred but not required. Strong online research and decision-maker identification skills. High attention to detail and data accuracy. APPLICATION SOFTWARE KNOWLEDGEABLE / REQUIRED Google Sheets Google Workspace Google Voice or a similar VoIP platform Online Research Tools Commercial Real Estate Databases (Preferred) Microsoft Office IDEAL CANDIDATE PROFILE Confident and experienced cold caller comfortable with consistent outbound calls. Skilled in lead qualification and appointment setting. Professional and conversational when engaging new prospects. Able to identify and reach key decision-makers. Persistent in reaching difficult-to-contact prospects and handling objections. Able to quickly assess lead quality and fit based on established criteria. Strong follow-up, organization, and documentation skills. Able to follow scripts and processes while maintaining a natural approach. Organized and accurate in managing prospect pipelines and trackers. Comfortable working independently with minimal supervision. Professional and respectful when communicating with churches, ministries, schools, and other organizations. Good judgment in identifying prospects for broker follow-up. Work Schedule Contracted Hours per Week: 20 Hours Scheduled Working Days: Monday through Friday (unless otherwise mutually agreed in writing) Daily Working Time: 10 AM to 2 PM Eastern Time, with flexibility for splitting hours or adjusting based on church office availability and list building needs. Note: The Company will make reasonable efforts to align the Assigned VA’s schedule with the Client’s preferred hours as set forth above. However, availability may vary based on time zones, capacity, and the contracted hours set in this SOW. Any changes must be mutually agreed to in writing through a revised SOW. Terms of Engagement Contractors will operate under a Master Services Agreement and a Statement of Work This is not an offer of employment; contractors are not employees of 20four7VA or any client All compensation is processed through 20four7VA according to the terms of the engagement Role Summary & Requirements REQUIRED SERVICE: Business Support REQUESTED EXPERIENCE TIER LEVEL: Entry, Junior, Senior Level Contracted Hours per Week: 20 hours/week Daily Working Time: 10 AM to 2 PM Eastern Time, with flexibility for splitting hours or adjusting based on church office availability and list-building needs. Scheduled Working Days: Monday-Friday REPORTING TO/POC: To Be Determined Number of VAs: 1 GENDER PREFERENCE: None LANGUAGES SPOKEN/PREFERENCE: English ACCENT PREFERENCE:  Level A-B (Clear and Professional Spoken English Required) REGION PREFERENCES: Any What we offer Competitive rates Weekly payments Various open roles are available Free training and upskilling Constant support and guidance A vibrant community always ready to support you And more!

Location: London & San Francisco Workplace: onsite Application deadline: We are conducting interviews actively and aim to fill this role as soon as we find someone suitable. ABOUT THE OPPORTUNITY The OpenAI/Hugging Face incident may be the biggest warning shot we get before catastrophic loss-of-control. In its aftermath, Anthropic and OpenAI publicly committed to giving embedded evaluators an unprecedented level of access. Embedded evaluations are the best existing method to find out whether alignment is on track. With OpenAI predicting an automated researcher by March 2028, we don't have time to start with an MVP embedded evaluation and iteratively improve it. The first embedded evaluations will set the standard and we have to get it right.  Apollo has taken steps toward becoming an embedded evaluator at frontier labs. We want to hire experienced senior researchers with a strong track record of getting things done to execute our embeddedevaluationplans. KEY RESPONSIBILITIES If embedded evaluations happen, you will be on one of the following teams: On-site embedded evaluators: This team will spend weeks or months embedded at a frontier AI lab, where they will conduct interviews, review the lab’s evidence, gather new evidence, and write a report. They will execute a playbook quickly and adapt to unforeseen circumstances. We hold our embedded evaluators to the highest standards, so you will likely have to first prove yourself within Apollo before being sent into an embedded campaign. That is, you will probably start on the applied research team to gain context on embedded evaluations. For this team, we prefer candidates with the right to work in the US who can be based in San Francisco. Applied researchers: This team will be the force-multiplier behind embedded evaluations. They will prepare on-site evaluators before they are embedded and support them while they are embedded. They will do the conceptual work to determine what evidence would support a safety case, and write the playbook for on-site evaluators to optimize how fast they can gather evidence. They will develop tooling for embedded evaluators, both ahead-of-time and urgently on demand. They will be the bridge connecting fundamental research and the on-site evaluators. For example, they might convert the methodology of “Measuring Reward-Seeking via Contrastive Belief Updates” into turnkey code and procedures. This team can work from any of our offices. Both are high-stakes teams. On-site embedded evaluators should be exceptionally strong at quick execution under high pressure, and very strong at planning and research. Applied researchers should be exceptionally strong at planning and research, and very strong at quick execution under high pressure. For both teams, we are excited for experienced candidates who have owned ambitious projects end-to-end under tight deadlines. KEY REQUIREMENTS We don’t require a formal background or industry experience and welcome self-taught candidates. We assess demonstrated ability, including through independent research and nontraditional paths; prior formal management titles are not required. Technical safety research judgment: You have a strong track record of producing or leading technically rigorous research relevant to understanding and reducing risks from advanced AI. Your background might include alignment, interpretability, model evaluations, control, adversarial ML, or post-training. You understand modern model training well enough to interrogate a lab’s methods and learn unfamiliar pipelines quickly. Deep experience with frontier RL or post-training is valuable but not required. Research leadership and delivery: You have led ambitious technical projects end-to-end under hard deadlines and managed or closely supervised strong individual contributors (ICs). You can set research priorities, delegate meaningful ownership, review technical work, and keep a team focused on the questions that matter most. Data Analysis & Pattern Recognition: You can extract signal from large, messy datasets. You're comfortable with quantitative analysis and know when qualitative assessment is more appropriate. You can identify anomalies and unexpected model behaviors. Writing for high-stakes audiences: You have experience writing about qualitative and quantitative findings for different stakeholders. You can take a pile of unorganized technical information and communicate it effectively to lab safety teams, lab leadership, governments, or the public. Strong understanding of scheming: You have a strong conceptual understanding of ideas like metagaming, evaluation awareness, deception, and reward-seeking. You are familiar with classic literature like Is Power-Seeking AI an Existential Risk? and modern research. AI power-user: You’re capable of using AI to accelerate your work, technical or otherwise. You have experience using different models, know which ones to use for which tasks, when not to use AI, and always experiment with new AI workflows. Professionalism: You can represent Apollo independently in sustained, high-stakes technical collaborations.  Your professional conduct is unimpeachable. You know when to tactfully resolve disagreements with partners and when to stand firm in negotiations, and you are excellent at both of those. Mission commitment: Apollo estimates that RSI will happen soon, and we are extremely concerned that AGI will catastrophically not be aligned with human values. Hence we have an “intense, but in a good way” culture . We will ask a lot from you, but in return you will personally have a large impact on loss-of-control risk. NICE TO HAVES Software engineering skills: Strong software engineering skills correlate highly with effective execution, even in an era of AI agents. Our entire stack uses Python. Prior embedded expertise: You have previous experience in professional services like auditing or consulting. You’ve learned lessons from working on-site with clients that will generalize to working with frontier AI labs. We want to emphasize that people who feel they don’t fulfill all of these characteristics but think they would be a good fit for the position, nonetheless, are strongly encouraged to apply. We believe that excellent candidates can come from a variety of backgrounds and are excited to give you opportunities to shine. BENEFITS This role offers a market-competitive salary, equity, and competitive benefits. Depending on your experience, you may be offered a Lead or individual contributor position. Salary: Recognised experts who can lead projects of significant scope: USD 350,000 - USD 590,000 or £230,000 - £395,000 Strong individual contributors: USD 245,000 - USD 405,000 or £160,000 - £270,000 Unlimited vacation Unlimited sick leave Up to 6 months of paid parental leave Comprehensive health, dental and vision insurance Retirement savings with competitive employer matching (e.g. 401(k) for US employees) Lunch, dinner, and snacks are provided for all employees on workdays Paid work trips, including staff retreats, business trips, and relevant conferences A yearly $1,000 (USD) professional development budget Relocation support and visa fees (if applicable). We are able to sponsor visas to the UK and US. LOGISTICS Time Allocation : Full-time Location : This is an in-person role working out of our London or San Francisco office. We prefer on-site embedded evaluators to be permanently located in San Francisco. Applied researchers can work from any of our offices. We offer flexible working hours and WFH arrangements. Remote candidates will only be considered if they are exceptional and can travel at least 1 week/month to either office. Visa sponsorship: We sponsor visas in both the UK and US. Sponsorship isn't guaranteed for every role or candidate, but if we make you an offer, we'll work with you to find the right visa route ABOUT APOLLO RESEARCH The rapid rise in AI capabilities offers tremendous opportunities, but also presents significant risks. At Apollo Research, we’re primarily concerned with risks from Loss of Control, i.e. risks coming from the model itself rather than e.g. humans misusing the AI. We’re particularly concerned with deceptive alignment / scheming, a phenomenon where a model appears to be aligned but is, in fact, misaligned and capable of evading human oversight. We work on the detection of scheming (e.g., building evaluations and novel evaluation techniques), the science of scheming (e.g., model organisms and the study of scaling trends), and scheming mitigations (e.g., control). We closely work with multiple frontier AI companies, e.g. to test their models before deployment and collaborate on fundamental research. At Apollo, we aim for a culture that emphasizes truth-seeking, being goal-oriented, giving and receiving constructive feedback, and being friendly and helpful. If you’re interested in more details about what it’s like working at Apollo, you can find more information here. ABOUT THE TEAM Alex Meinke leads the science of scheming team’s research agenda with guidance from Marius Hobbhahn, though team members lead individual projects. You will mostly work with the science of scheming team as well as our team of software engineers, but you will likely sometimes interact with the governance team to translate technical knowledge into concrete recommendations. You can find our full team here. Equality Statement: Apollo Research is an Equal Opportunity Employer. We value diversity and are committed to providing equal opportunities to all, regardless of age, disability, gender reassignment, marriage and civil partnership, pregnancy and maternity, race, religion or belief, sex, or sexual orientation.  How to apply: Please complete the application form with your CV. The provision of a cover letter is optional but not necessary. Please also feel free to share links to relevant work samples. About the interview process: Our multi-stage process includes a screening interview, 3 technical interviews, and a final interview with Marius (CEO). The technical interviews will be closely related to tasks the candidate would do on the job. There are no LeetCode-style general coding interviews. If you want to prepare for the interviews, we suggest (a) thinking about what embedded evaluators should investigate, (b) building a tool that would be useful for embedded evaluators and running it on suitable proxy data, and/or (c) writing a report based on mock findings.  Your Privacy and Fairness in Our Recruitment Process: We are committed to protecting your data, ensuring fairness, and adhering to workplace fairness principles in our recruitment process. To enhance hiring efficiency, we use AI-powered tools to assist with tasks such as resume screening. These tools are designed and deployed in compliance with internationally recognized AI governance frameworks. Your personal data is handled securely and transparently. We adopt a human-centred approach: all resumes are screened by a human and final hiring decisions are made by our team. If you have questions about how your data is processed or wish to report concerns about fairness, please contact us at info@apolloresearch.ai.

Location: Boston, MA 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 team Anthropic's Life Sciences team is building a world-class research group focused on making fundamental biological discoveries. The team combines cutting-edge AI with hands-on biological research, positioning Anthropic at the forefront of AI-accelerated scientific discovery. About the role We are hiring a Research Operations Lead to be the operational backbone of our team: the person who makes sure scientists can walk in every morning and get straight to science. You'll take on the operational systems we've started — procurement, inventory, safety, equipment, space — make them better, and scale them as the lab and its needs grow. We're hiring for trajectory as much as for what you've already done: the scope of this role will grow with the size of the team, and we want someone who's excited by that. This role is a rare opportunity to build the operational foundation of Anthropic's biology team and to grow with it. The systems you put in place will shape how a frontier AI company does experimental biology for years to come. If you're energized by high-ownership operational work in the service of ambitious science, we'd love to hear from you. Key responsibilities Own day-to-day operations end to end: ordering and inventory, equipment scheduling and upkeep, consumables, shipping and receiving, and general space organization Build on and extend the operational systems a growing group needs: procurement workflows, inventory and sample tracking, equipment maintenance and calibration schedules, onboarding and training, SOPs Manage facilities and space: coordinate buildouts, moves, and equipment installs; act as the primary interface to building management, contractors, and Anthropic's central Workplace and IT teams Own the operating budget: track spend against plan, run the capital equipment purchasing process, and partner with Finance on forecasting and reporting Manage vendor and supplier relationships: including contract research organizations: source and qualify vendors, negotiate quotes and service contracts, schedule preventive maintenance and field-service visits, and hold suppliers to their commitments Minimum qualifications Have managed the day-to-day operations of a life sciences lab — equipment, inventory, ordering, safety, space, and scheduling Have hands-on procurement and vendor management experience: sourcing suppliers, negotiating quotes and service contracts, and coordinating field-service and maintenance technicians Have tracked and managed an operating budget: purchase orders, invoices, spend-vs-plan, and capital requests Preferred qualifications Are the person everyone in your group already comes to when something is broken, missing, late, or unclear — and you like being that person Enjoy engaging with scientists on the science itself — you want to understand what's happening at the bench well enough to anticipate needs Are results-oriented and practical and pick up slack even when it goes outside your job description Enjoy a role that stretches from the hands-on to the strategic, and are comfortable when priorities shift quickly and feedback is direct Lab operations or lab management experience in a biotech, pharma, diagnostics, or life-sciences startup environment, including exposure to how those organizations build new functions and processes end to end A degree in the life sciences and prior research experience at the bench People-management experience: hiring, onboarding, and supervising operations or facilities staff Experience supporting a team through significant growth in headcount, footprint, or capability, including utilities, permitting, and equipment Familiarity with liquid-handling platforms, including their service, calibration, and consumable needs Experience selecting and administering inventory management, sample tracking, equipment scheduling, or ELN systems Capital equipment lifecycle management experience — evaluation, purchase, qualification (IQ/OQ/PQ), service contracts, and decommissioning Experience with regulated shipping and receiving of biological materials, including import/export, permits, and material transfer agreements Experience using AI tools for efficient team and process management (e.g., Claude Cowork) 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: $210,000 — $260,000 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. For roles performing work in California: We will consider for employment all qualified applicants, including those with arrest or conviction records or criminal histories, in a manner consistent with the requirements of applicable state and local laws, including the San Francisco Fair Chance Ordinance, the City of Los Angeles’ Fair Chance Initiative for Hiring Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act. 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: Thessaloniki, Greece CENSUS is an internationally acclaimed Cybersecurity services provider. We support the needs of multiple industries, providing IT and OT security assessment services to public and private organizations around the world, including international financial institutions and Fortune 500 companies. Powered by cutting-edge research, scientific analysis, and real-world engineering, we deliver unparalleled security assessment services for software, devices, infrastructure, and organizations. Learn more about CENSUS at census-labs.com. About the Role CENSUS is looking for a iOS Vulnerability Researcher to join our security research team, focused on vulnerability discovery and exploitation across the iOS userland attack surface. You’ll spend your time reverse engineering proprietary Apple software, identifying security boundaries and interesting attack surfaces, developing fuzzers and instrumentation, and turning promising primitives into reliable vulnerability research. This is a hands-on research role for someone who enjoys working close to the metal, understands Apple's ecosystem and is comfortable operating without complete documentation. Responsibilities Reverse engineer iOS binaries and system frameworks on ARM64/ARM64e. Identify and investigate previously unknown vulnerabilities in iOS userland components. Analyze system daemons, XPC services, framework APIs, parsers, and other undocumented components of the iOS/macOS system. Develop targeted fuzzers and harnesses. Develop reliable proof-of-concepts and undocumented techniques in controlled research environments. Research the practical impact of modern mitigations including PAC, MTE/MIE, sandboxing, hardened runtime protections, and related platform defenses. Develop custom tooling for binary analysis, instrumentation, tracing, fuzzing, and vulnerability research. Collaborate with other researchers on projects. Technical Requirements Hands-on vulnerability research, reverse engineering, exploitation research, or closely related security research experience. Strong understanding of C/C++ fundamentals, memory corruption, and common vulnerability classes. Solid ARM64/ARM64e assembly and calling-convention knowledge. Practical experience with reverse engineering using tools such as IDA Pro, Ghidra, Hopper, or Binary Ninja. Experience debugging with lldb. Experience performing crash analysis without a fully capable debugging environment. Experience with Frida, DTrace, custom instrumentation, or comparable dynamic-analysis tooling. Ability to write research tooling in Python and at least one systems language such as C, C++, Objective-C, Swift. Understanding of Objective-C & Swift runtime behavior and iOS process architecture. Familiarity with fuzzing methodologies and experience building or adapting fuzzing harnesses. Ability to reason about the exploitability of a vulnerability rather than stopping at crash discovery. Being able to adapt to scenarios where the complete technical picture is not present. Preferred Experience Previous iOS or macOS vulnerability research. Public vulnerability disclosures, CVEs, security advisories, conference talks, or substantial private research experience. Familiarity with arm64e Apple specific internals Familiarity with modern iOS exploitation mitigations. Familiarity with jailbreak-era or modern iOS research tooling is useful, but not required. Experience adapting research techniques between iOS releases and dealing with undocumented platform changes. Experience with reversing Objective-C binaries. Experience with iOS/macOS IPC mechanisms, such as mach messages, MIG, and XPC What We Offer Please note that some of the benefits listed below are applicable only to full-time employees in locations where CENSUS has a legal entity. The chance to be part fast-growing, global cybersecurity company A collaborative, purpose-driven team with a strong learning mindset Competitive salary and benefits, such as but not limited to: Birthday Paid Day off Coffee, lunch and office snacks free of charge Private Medical and Life Insurance covering employees & family members Funded certifications & postgraduate programs Recognition programs including a welcome gift, a new year celebration gift, referral bonus and long service vouchers Exposure to international teams, leaders, and challenges Opportunities for professional growth, development, innovation, and more! Apply today to start the conversation with the People & Culture Team. CENSUS is an equal opportunity employer. The Company does not discriminate on the basis of race, color, creed, pregnancy, religion, gender, national origin, age, disability, marital, or any other legally protected status. The Company also makes reasonable accommodations for disabled employees. Finally, the Company prohibits the harassment of any individual based on their protected status. This policy applies to all areas of personnel actions including recruitment, hiring, training, promotion, compensation, benefits, transfer, and social and recreational programs. LI | Remote