Вакансии

Задачи: — Создать систему мониторинга, которая регулярно сверяет инвестиционные тезисы с новыми данными и показывает, подтверждается тезис, слабеет или требует пересмотра. — Создать point-in-time хранилище данных: рыночные и фундаментальные данные, документы SEC, стенограммы звонков, новости, внутренняя аналитика. — Проверять инвестиционные гипотезы на исторических данных (backtesting) и разрабатывать сигналы в дополнение к фундаментальному анализу. — Проводить портфельную аналитику: концентрация, факторные экспозиции, ликвидность, сценарные риски, размер позиций. — Развивать AI-агентов в исследовательском процессе и оценивать качество их работы. Требования: — Сильная база в математике, статистике и машинном обучении. — Опыт от 3 лет в quantitative research или data science на финансовых данных. — Опыт доведения модели или сигнала до практического применения с демонстрацией проверки результата. — Опыт backtesting на point-in-time данных с пониманием look-ahead bias, survivorship bias и переобучения. — Умение самостоятельно строить пайплайны данных и писать надёжный, тестируемый код на Python и SQL. — Опыт построения систем на LLM с измеримым качеством: evaluation, тестовые наборы, контроль ошибок. — Понимание фундаментального анализа и портфельного риска для формализации инвестиционного тезиса. — Уверенный английский язык. Условия: — Формат работы: full-time, удалённо. — Конкурентная оплата труда и годовой бонус по личным результатам и результатам компании. — Собственный бюджет на инфраструктуру и найм команды при подтверждении эффективности системы.

Задачи: — Создать систему мониторинга, которая регулярно сверяет инвестиционные тезисы с новыми данными и показывает, подтверждается тезис, слабеет или требует пересмотра. — Создать point-in-time хранилище данных: рыночные и фундаментальные данные, документы SEC, стенограммы звонков, новости, внутренняя аналитика. — Проводить backtesting и разрабатывать модели для проверки инвестиционных гипотез на исторических данных, создавать сигналы в дополнение к фундаментальному анализу. — Анализировать портфель: концентрация, факторные экспозиции, ликвидность, сценарные риски, размер позиций. — Развивать AI-агентов в исследовательском процессе и оценивать качество их работы. Требования: — Сильная база в математике, статистике и машинном обучении. — Опыт от 3 лет в quantitative research или data science на финансовых данных. — Практический опыт доведения модели или сигнала до применения с демонстрацией проверки результата. — Опыт backtesting на point-in-time данных с пониманием look-ahead bias, survivorship bias и переобучения. — Умение самостоятельно строить пайплайны данных и писать надёжный, тестируемый код на Python и SQL. — Опыт построения систем на LLM с измеримым качеством: evaluation, тестовые наборы, контроль ошибок. — Понимание фундаментального анализа и портфельного риска для формализации инвестиционного тезиса. — Уверенный английский язык. Желательно: — Опыт построения систем на LLM с измеримым качеством: evaluation, тестовые наборы, контроль ошибок. — Опыт работы на buy-side или в прямом взаимодействии с портфельными управляющими. Условия: — Работа с реальным портфелем, инструменты тестируются в инвестиционном процессе. — Доступ к профессиональным источникам данных и современным AI-инструментам. — Конкурентная оплата труда и годовой бонус по личным и корпоративным результатам. — Собственный бюджет на инфраструктуру и найм команды при подтверждении эффективности системы.

Задачи: — Повышать скорость экспериментов команды, ускорять запуск, получение и анализ результатов. — Поддерживать и развивать JAX фреймворк для обучения моделей, обеспечивая масштабируемость и эффективность на TPU. — Улучшать качество данных для обучения моделей, выявлять и исправлять проблемы, повышая точность. — Анализировать точность продакшн-моделей, строить системы оценки и определять приоритетные улучшения. — Переводить исследовательские прототипы в продакшн-системы, рефакторить и модернизировать архитектуры и инфраструктуру. — Оптимизировать продакшн-инференс для речевых моделей, включая архитектуру сервиса и техники квантования, спекулятивного декодирования. — Исследовать и устранять узкие места в производительности на всех уровнях стека, от низкоуровневых ядер (XLA, Pallas) до системного дизайна. — Сотрудничать с исследователями, инфраструктурной и продакшн-командами для выявления и устранения проблем. Требования: — Экспертное владение JAX и TPU, включая экосистему Flax, Optax, XLA. — Дисциплина измерений: определять успех заранее, критически оценивать результаты. — Готовность работать с полным пайплайном, включая данные и оценку. — Опыт оптимизации систем инференса для продакшна, желательно с LLM или речевыми моделями. — Глубокое понимание распределённого обучения, современных систем глубокого обучения и ML-инфраструктуры. — Знание современных техник оптимизации инференса: непрерывная пакетизация, управление KV-кэшем, стратегии шардирования, квантование. — Энтузиазм к рефакторингу и улучшению существующих систем. — Сильные навыки Python; опыт C++ или Rust для низкоуровневой работы — плюс. — Отличные коммуникативные навыки и умение работать в команде. Бонус: — Знания в области Speech-to-Text: архитектуры ASR, аудиопроцессинг, стриминговый инференс. Условия: — Удалённая работа. — Полная занятость. — Зарплата в США от $270,000 до $310,000 в год. Компания AssemblyAI — лидер в области Voice AI, обрабатывает миллионы часов аудио и API вызовов ежедневно, работает в режиме meritocracy без бюрократии, с высокой степенью ответственности и влияния каждого сотрудника.

Задачи: — Проводить проактивный поиск угроз и анализ цепочек атак (kill chain) в системах Stripe и внешних данных. — Применять и развивать многоуровневую таксономию FT3 для стандартизации разведданных об угрозах. — Сотрудничать с командами Fraud Intelligence, Fraud Ops, Strategy, Risk, Onboarding и Security для интеграции и автоматизации потоков разведданных. — Переводить результаты исследований в практические рекомендации по контролю угроз, включая политику, технические системы, рабочие процессы поддержки и механизмы обнаружения. — Использовать агентные автоматизированные тестовые фреймворки для моделирования поведения злоумышленников, проверки эффективности контролей и создания регрессионных сценариев. Требования: — Опыт не менее 5 лет в области разведки угроз, поиска угроз или технического реагирования на инциденты в кибербезопасности, борьбе с мошенничеством или доверительных доменах. — Опыт анализа больших и сложных наборов данных с использованием аналитических инструментов для выявления аномалий, анализа поведенческих трендов и решения сложных задач мошенничества. — Высшее образование (бакалавр или магистр) в области компьютерных наук, кибербезопасности или смежной технической области, либо эквивалентный практический опыт. — Экспертное владение Python и SQL, опыт автоматизации рабочих процессов, создания инструментов для расследований и запросов к большим данным. — Практический опыт анализа логов (приложений, API, сетевых событий), цифровой криминалистики и методологий киберрасследований. — Сильные коммуникативные навыки для перевода сложных технических исследований в понятные и применимые рекомендации для межфункциональных команд. Предпочтительные квалификации: — Глубокое понимание мотиваций, инфраструктуры и тактик злоумышленников, специфичных для финансового мошенничества (например, ATO, Card Testing, Credential Stuffing). — Знакомство с таксономиями FT3 или MITRE ATT&CK. — Владение платформами для обработки и анализа данных, такими как Databricks, Trino, PySpark, Pandas, Scikit-Learn. — Опыт работы с платформами разведки угроз (TIPs), тактическими потоками угроз, OSINT и разведданными о взломах. — Навыки создания или использования агентных LLM-инструментов, систем автоматизированного тестирования и фреймворков валидации контролей для моделирования поведения злоумышленников в масштабе. — Участие в отраслевых конференциях, вебинарах, группах обмена угрозами и наличие профессиональных сертификатов (например, GCTI, GCFA, OSCP).

Задачи: — Полный цикл исследований по краткосрочной микроструктуре, LP/хеджированию, исполнению, межплощадочным сигналам, распределению риска и смежным задачам crypto/DeFi — Дизайн исследования: гипотеза, target/разметка, baseline, метрики, причинно корректная OOS-проверка, анализ режимов и явные критерии GO/NO-GO — Работа с данными: поиск источников, выравнивание времени, контроль качества, построение признаков и воспроизводимые контракты данных — ML/DL по структуре задачи — RL или другие методы последовательного управления - только когда они действительно оправданы — Реалистичная симуляция и торговая экономика: комиссии, проскальзывание, ликвидность, задержки, adverse selection, impact, fill assumptions, хеджирование и синхронизация DEX/CEX — Paper/shadow-стратегии, анализ sim-to-real и совместная работа с quant developer при переносе доказанных решений в production — Инженерный стандарт исследования: воспроизводимые эксперименты, чистый Python, критичные проверки, мониторинг и дисциплинированное использование LLM/агентов Требования: — Проверяемый опыт 2-3 Quant/ML проектов, где кандидат лично отвечал за существенную часть цепочки от постановки задачи и данных до проверки, кода и решения о внедрении либо GO/NO-GO — Отличный Python и сильная инженерная база: кандидат сам пишет, отлаживает и ревьюит исследовательский код — Сильная статистика, вероятность, оптимизация и работа с временными рядами: временную зависимость, leakage, selection bias, uncertainty и честную OOS-проверку — Глубокое практическое понимание микроструктуры и исполнения: order book/flow, liquidity, latency, fees, slippage, adverse selection, impact, sizing и hedging — Сильное практическое владение ML/DL для последовательных финансовых данных и способность отличать реальный сигнал от оптимизации метрики — Высокая самостоятельность: структурировать неясную проблему, выбирать наиболее информативный эксперимент, быстро останавливать слабые гипотезы и принимать решения на основании доказательств — Рабочее понимание структуры crypto-рынка — Прямой опыт DeFi/DEX будет плюсом, но сильный CEX/microstructure опыт может его компенсировать при быстрой адаптации

Задачи: — Поиск и сбор релевантной информации по заданиям команды: компании, конкуренты, лиды, поставщики, партнёры, форумы, отзывы, объявления и другие источники. — Поиск кандидатов, поставщиков и партнёров через LinkedIn, job boards, доски объявлений, профильные форумы и специализированные площадки. — Поиск информации на тематических форумах, в Telegram, Discord и других нишевых сообществах. — Проведение первичного outreach: установление контакта, ведение диалога и получение необходимой информации. — Создание и ведение рабочих аккаунтов на необходимых площадках под конкретные задачи. — Использование инструментов массового поиска, фильтрации, парсинга и базовой автоматизации. — Работа с AdsPower и аналогичными инструментами при необходимости. — Работа с proxy и инструментами для доступа к региональным ресурсам в рамках законодательства и внутренних правил. — Проверка и верификация найденной информации по нескольким источникам. — Работа с большими массивами информации: из большого количества источников выделять 3–7 действительно релевантных и обосновывать выбор. — Подготовка структурированных отчётов: ссылка → краткая выжимка → приоритет → рекомендация. — Передача команде найденных контактов, информации и рекомендаций по дальнейшим действиям. Требования: — Опыт в internet research / sourcing от 1–2 лет. — Сильные навыки поиска информации в интернете, в том числе через нестандартные и нишевые источники. — Уверенная работа с LinkedIn, job boards, досками объявлений, форумами и другими профильными площадками. — Опыт поиска информации через Telegram, Discord и тематические сообщества. — Умение создавать и вести рабочие аккаунты на различных платформах. — Базовое владение инструментами парсинга и автоматизации. — Опыт или понимание работы с AdsPower и аналогичными инструментами. — Понимание принципов работы proxy. — Умение проверять достоверность и актуальность найденной информации. — Сильные аналитические навыки: способность из большого массива данных выделять действительно ценную информацию. — Умение самостоятельно устанавливать контакт, вести диалог и получать необходимую информацию. — Английский — Intermediate+ для работы с англоязычными источниками и ведения переписки. — Ответственность, самостоятельность, внимательность к деталям и соблюдение конфиденциальности. — Гибкость и хорошие коммуникативные навыки.

Location: United States Bring your monitoring expertise to the forefront of innovation. We are looking for a Clinical Research Associate (CRA) to join our A-team (remote; with travel). As a CRA at Allucent, you will independently control and monitor investigational sites, pro-actively detect issues, provide solutions to ensure clinical studies are performed according to the trial protocol and in compliance with SOPs, applicable regulations, and the principles of Good Clinical Practice. At Allucent™, we are dedicated to helping small-medium biopharmaceutical companies efficiently navigate the complex world of clinical trials to bring life-changing therapies to patients in need across the globe. At Allucent we are big enough to deliver and small enough to care. We know all our colleagues by name and work together as a team to make everyone SHINE. The role As a CRA , you will take the lead on monitoring activities, owning your sites and relationships with investigators. You’ll be the bridge between sponsor and site, ensuring trial integrity while being supported by a collaborative, experienced team. You will be a mentor for junior team members and contribute to grow our success. You will travel throughout the United States. Therapeutic Areas: Oncology, Cell & Gene Therapy, and/or Neuroscience Preferred location: West Coast, USA Main responsibilities: Independently manage all aspects of monitoring for assigned clinical trials across study phases, including site initiation, routine monitoring, and close-out visits. Ensure compliance with protocols, GCP, ICH, SOPs, and applicable regulatory requirements. Build strong site relationships , acting as a primary point of contact and ensuring high-quality data collection. Identify and resolve site issues , escalating as needed to maintain trial integrity and patient safety. Mentor and support junior CRAs , sharing knowledge and best practices. Requirements Who are we looking for: You’re an experienced CRA / monitor ready to take the next step with a company that truly values your expertise and independence. You thrive in an agile, collaborative environment and want to be part of a team making a real impact. Preferred Location: West Coast, United States Therapeutic Areas:Oncology, Cell & Gene Therapy, and/or Neuroscience Experience leading monitoring activities in complex trials with a minimum of at least 3-5 years’ experience. Experience in complex therapeutic areas such as cell and gene therapy, radiopharmaceutical preferred. Strong understanding of ICH-GCP and regulatory guidelines. Clear and confident communicator in English. Comfortable working independently and managing priorities. Willingness to travel throughout the United States, as required. Effective clinical monitoring skills (all kind of visits mastered) Benefits Benefits of working at Allucent include: Comprehensive benefits package per location Competitive salaries per location Departmental Study/Training Budget for furthering professional development Flexible Working hours (within reason) Opportunity for remote/hybrid* working depending on location Leadership and mentoring opportunities Participation in our enriching Buddy Program as a new or existing employee Internal growth opportunities and career progression Financially rewarding internal employee referral program Access to online soft-skills and technical training via internal platforms Eligibility for our Spot Bonus Award Program in recognition of going above and beyond on projects Eligibility for our Loyalty Award Program in recognition of loyalty and commitment of longstanding employees Disclaimers: *Our policy encourages a dynamic work environment, prescribing a minimum of 2 days in office per week for employees within reasonable distance from one of our global offices. “The Allucent Talent Acquisition team manages the recruitment and employment process for Allucent (US) LLC and its affiliates (collectively “Allucent”). Allucent does not accept unsolicited resumes from third-party recruiters or uninvited requests for collaboration on any of our open roles. Unsolicited resumes sent to Allucent employees will not obligate Allucent to the future employment of those individuals or potential remuneration to any third-party recruitment agency. Candidates should never be submitted directly to our hiring managers, employees, or human resources.” #LI-TCW #LI-remote

Location: Yerevan, Armenia Description The Market Research Case & Strategy Analyst evaluates iGaming markets, competitors, and target operators to support Digitain's market entry decisions, commercial strategy, and sales engagement, delivering actionable insights for Sales, Product, and Leadership teams. As Market Research Case & Strategy Analyst, you will assess priority markets, analyze competitor strategies and industry success stories, and identify operators that are strong migration opportunities for Digitain. Your work will directly shape which markets we enter, when and how, and which operators we pursue. Responsibilities Evaluate target markets for product launches, sales activation, and strategic entry, analyze competitors’ entry experiences, launches, regulatory adaptations, and operational challenges to identify practical lessons Analyze market growth, trends, and maturity across macroeconomic, demographic, fiscal, regulatory, competitive, and operational indicators; assess industry success stories for applicable drivers of revenue growth, player acquisition, and innovation adoption Monitor industry investment and expansion trends; evaluate business strategies, growth models, and partnerships across the iGaming market Track existing and upcoming regulations, B2B licensing frameworks—including those in planning—and licensing tenders to identify entry requirements, timely sales opportunities, and operators needing platform solutions Assess market attractiveness through size, growth, competition, regulatory stability, and business impact; benchmark key competitors’ entry strategies, operations, pricing, and long-term positioning Develop forward-looking market assessments to identify early-entry advantages ahead of leading competitors Evaluate market-specific product readiness across platform capabilities, sportsbook, casino content, payments, and compliance tools; research operators’ integrations, supported languages, content providers, and technical partners to identify gaps and adaptations Collaborate with Market Intelligence, Product Innovation, and Product teams to integrate insights into actionable analyses and document required adaptations or new tool recommendations in execution-ready formats Work with Legal and Compliance teams to assess regulatory gaps and market-specific technical compliance obligations Produce market entry reports, opportunity briefs, gap analyses, case studies, and strategic briefs for Sales, Product, and Leadership; recommend entry timing, product priorities, and investment focus, contributing to BRDs, PRDs, budgets, and financial documentation Continuously monitor researched markets for regulatory and competitive changes, updating assessments and recommendations Source and validate research using industry intelligence, regulatory publications, financial reports, competitor filings, databases, interviews, and historical performance metrics Identify potential platform migration targets and conduct pre-engagement research using operator news, public announcements, and industry publications Analyze target operators’ financial and performance indicators, including GGR, traffic, and player base Assess operators’ geographic footprint and technology stack, distinguishing between in-house and third-party platforms Requirements Bachelor's or Master's degree in Economics, Business, Finance, International Relations, or a related field 3+ years of experience in market research, market entry strategy, strategy consulting, competitive or sales intelligence, or business analysis, ideally within iGaming. Experience in fintech, telecom, or other heavily regulated multi-market industries will also be considered Demonstrated experience conducting market entry assessments and in-depth case studies of companies, ideally with direct influence on go/no-go or timing decisions Strong analytical and strategic thinking skills, with the ability to synthesize complex multi-source data into clear, executive-ready reports and presentations Experience working with company financials and performance metrics (GGR, traffic, revenue). Familiarity with iGaming operator economics and platform/technology stacks is a strong advantage Exposure to product readiness or gap analysis work across platform, sportsbook, casino, or payment capabilities is a strong plus Understanding of compliance, licensing, and regulatory tender processes, or the ability to work closely with Compliance and Legal teams Excellent research and written communication skills, with a proven ability to produce decision-ready reports, case studies, and strategic briefs Proficiency in Excel/data analysis tools. Experience with BI, market intelligence, or CRM platforms is a plus Fluent English (written and spoken). Knowledge of additional languages is a plus

Location: Yerevan, Armenia Description The Product Innovation & Research Analyst is responsible for deep-diving into the iGaming product ecosystem — evaluating competitor features, identifying innovation opportunities, and translating market insights into structured product intelligence that drives Digitain's roadmap and competitive positioning. This role sits at the crossroads of product thinking and competitive intelligence. You will work closely with Digitain's cross-functional product teams — Platform, Sportsbook, Live Casino, Relum, and Paydrom — to evaluate what competitors are building, what operators need, and where the greatest opportunities for differentiation lie. Responsibilities Conduct deep-dive product research across the iGaming ecosystem: platforms, aggregators, sportsbooks, live casino providers, virtual sports, and payment solutions Analyze product innovations, features, and technologies identified through both independent research and insights from the Market Intelligence Expert Identify product features and capabilities with proven or potential business impact: revenue uplift, player engagement, retention, or market differentiation Initiate proposals for new product sets or market-specific solutions when research identifies them as critical to competitive success or regional growth Evaluate potential impact, scalability, and strategic value of identified features — including quantitative assessments of revenue growth, user engagement, technical complexity, and time-to-market Conduct market- and region-specific product gap analyses, including local player preferences, regulatory requirements, and localization complexity assessments Assess Digitain's platform readiness for localization in specific countries; prepare initial resource estimations to support full market entry Identify and document operator pain points via social media, conference content, podcasts, and industry news; translate findings into structured insights for the Sales team Collaborate with cross-functional product teams to validate research, gather expert input, and present detailed analyses Track adoption and performance of competitor product features post-launch to validate impact assumptions and refine future recommendations Prepare comprehensive documentation of research outcomes to support BRD, PRD, and strategic planning processes Stay current on emerging product and technology trends (AI personalization, gamification, new betting formats, alternative payments) and proactively surface high-potential opportunities Requirements Bachelor's degree in Business, Economics, Marketing, Computer Science, or a related field 3+ years of experience in product research, product management, business analysis, or competitive intelligence — ideally within iGaming (platforms, sportsbook, live casino, payments); experience in fintech, SaaS, or gaming related industries also considered Demonstrated experience conducting deep-dive product research and translating findings into structured, actionable recommendations Strong analytical skills, with the ability to assess product features for scalability, technical complexity, and time-to-market Experience evaluating product features for business impact (revenue, engagement, retention) and presenting findings to cross-functional stakeholders Familiarity with the iGaming product landscape: platforms, aggregators, sportsbook providers, live casino, virtual sports, and payment solutions Excellent research skills, with the ability to source and analyze insights from conferences, podcasts, social media, industry news, and direct product testing Ability to prepare clear documentation to support BRD/PRD and strategic planning processes Ability to work closely with cross-functional product teams to validate research and gather expert input Ability to proactively propose and advocate for new product ideas and market-specific solutions based on research findings Fluent English (written and spoken); additional languages are a plus

Location: West Palm Beach, Florida, United States Graham Capital Management, L.P. (collectively with its affiliates, "Graham") is an alternative investment manager founded in 1994 by Kenneth G. Tropin. Specializing in discretionary and quantitative macro strategies, Graham is dedicated to delivering strong, uncorrelated returns across a wide range of market environments. As one of the industry’s longest-standing global macro and trend-following managers, Graham remains committed to innovation, evolving its strategies through a robust investment, technology, and operational infrastructure. Graham harnesses the synergies between its discretionary and quantitative trading businesses to offer a broad suite of complementary alpha strategies, each built on the principles of thoughtful portfolio construction, active risk management, and diversification by design. Graham invests significant proprietary capital alongside its clients – including global institutions, endowments, foundations, family offices, sovereign wealth funds, investment management advisors, and qualified individual investors – reinforcing alignment of interests across all strategies. The foundation of Graham’s sustainability and success is the experience and contributions of its people. The firm seeks to cultivate talent, encourage the diversity of ideas, and respect the contributions of all. In turn, each employee shares in the responsibility of strengthening those around them. Description Graham is seeking a Quantitative Research Developer to join our Quantitative Strategies team. The individual will research and develop ways to improve Graham’s current trading code base and create new systematic trading signals to complement and diversify the firm’s main strategies. The individual will maximize performance and competitiveness by utilizing advanced methods in quantitative analysis and software development. Responsibilities Create production code for new trading signals Ensure the efficiency and accuracy of existing production code Research and develop methods to make existing trading signals more efficient, profitable and robust Research and develop new signals that trade futures and FX markets that complement and diversify production strategies in terms of style, source of alpha and markets traded Follow robust research and development procedures to reduce differences between simulated and actual performance Work collaboratively in a research team environment, using common development tools to facilitate robust implementation of research as well as production versions of trading systems Interact with other departments – technology, operations, trading, marketing, and accounting - to ensure current and proposed ideas are implemented, monitored and executed efficiently and accurately Regularly present findings and ideas to management and investment committee Complete other projects as requested by senior management Requirements PhD or MS in a quantitative field 2+ years related experience in systematic futures and/or FX trading on the buy side Exposure to securities and derivatives markets and investment processes with knowledge in mid-to-low frequency systematic strategies Advanced programming experience in languages suited for quantitative research and strategy implementation – python and/or MATLAB preferred Ability to synthesize complex topics into easily digestible written commentary Strong communication skills – both written and verbal This role requires commuting into our West Palm Beach, FL office Mondays through Fridays. Please note that our central Quantitative Research team sits out of Rowayton, CT but the manager for this new hire will be based out of West Palm Beach, FL. Base Salary Range The anticipated base salary range for this position is $200,000 to $275,000. The anticipated range is based on information as of the time this post was generated and does not include any discretionary bonus or benefits (see eligibility below). The applicable annual salary or hourly rate paid to a successful applicant will be determined based on multiple factors, including without limitation the nature and extent of prior experience, skills, and qualifications.  This wage range may be modified in the future. In addition, the employee who fills this role will be eligible for a discretionary annual bonus, as well as a wide array of benefit programs, such as medical and life insurance, 401(K) plans, and access to other healthcare programs. Notes: No amount of pay is considered to be wages or compensation until such amount is earned, vested, and determinable. The amount and availability of any bonus, benefits, or any other form of compensation that a particular employee may be eligible for remains in the Company's sole discretion unless and until paid and may be modified at Graham’s sole discretion, consistent with the law. Other Graham is committed to providing equal employment opportunity to all employees and applicants for employment without regard to their race, color, religious creed, gender, age, national origin, ancestry, alienage, citizenship status, handicap, disability, marital status, sexual orientation, gender identity, pregnancy, childbirth or other related conditions, military status, genetic information, or any other personal characteristics protected by applicable law. This policy applies to all terms and conditions of employment, including hiring, placement, promotion, layoff, termination, transfer, leave of absence and compensation. Graham offers health benefits to eligible full-time U.S. employees, including medical, dental, vision, life and disability insurance, as well as other voluntary benefits such as spending accounts (HSA and FSA), and a profit contribution retirement plan.  All employees also receive paid vacation and sick leave.  Benefits eligibility is dependent upon a variety of factors, including employee classification and applicable waiting periods.  Benefits are subject to change and may differ for non-U.S. roles.

Предлагается стипендия Post-lauream research scholarships для иностранных студентов, планирующих обучение в Университете Палермо. Стипендия полностью покрывает расходы на обучение. Крайний срок подачи заявок — как можно скорее.

Задачи: — Решать сложную исследовательскую задачу на стыке probabilistic ML, sequential decision making и моделирования поведения агентов. — Моделировать поведение участников в стохастической многоагентной среде с частичной наблюдаемостью и использовать эти прогнозы для принятия решений в условиях неопределённости и ограниченного inference budget. — Проводить эксперименты и доказывать, почему новый подход лучше сильного baseline. Требования: — Опыт самостоятельной разработки или существенного улучшения ML/математических методов. — Умение работать с неопределённостью. — Опыт в одном или нескольких направлениях: RL / sequential decision making / POMDP / world models; probabilistic & Bayesian ML, latent-variable models, uncertainty estimation; behavior / opponent modeling, multi-agent systems; off-policy evaluation, importance sampling, doubly robust estimation, variance reduction; Monte Carlo / Markov models / stochastic optimization; algorithmic trading / HFT / pricing / decision systems с реальными ограничениями на latency. — Сильная математическая база, опыт собственных R&D-разработок и публикации либо сильный applied research опыт. — PhD будет плюсом, но не обязателен. — Важна способность подробно разбирать свой проект: проблема → baseline → ваша идея → альтернативы → эксперимент → измеримый результат. Условия: — Формат работы: remote. — Контракт от 3 месяцев. — Приоритетно full-time, но возможен part-time для сильных кандидатов. — Данные и вычислительная инфраструктура предоставлены, высокая исследовательская свобода. Стек для прототипирования: Python, PyTorch.

Задачи: — искать информацию о компаниях, конкурентах, лидах, поставщиках и кандидатах; — собирать данные на LinkedIn, Telegram, Discord, джоб-бордах, форумах и нишевых ресурсах; — находить контакты и информацию, которую не всегда удаётся получить обычным поиском; — самостоятельно выходить на контакт, работать с аккаунтами, использовать AdsPower, прокси, базовый парсинг и автоматизацию; — проверять информацию по нескольким источникам и превращать большие объёмы данных в понятные структурированные отчёты. Требования: — опыт пользователя поисковых инструментов и нестандартных источников; — опыт Internet Research / Sourcing от 1–2 лет; — внимательность и самостоятельность, умение проверять достоверность информации; — английский от Intermediate+; — опыт в OSINT будет большим плюсом. Условия: — полностью удалённая работа; — график 5/2, 9:00–18:00 по Киеву; — полная занятость, онлайн-офис через Compass Space; — зарплата до $1500 в месяц; — работа в Армении. Требования к языкам: русский — свободно, английский — Intermediate+, желательно свободный.

Location: San Francisco, CA | New York City, NY | Seattle, WA About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role Reinforcement learning is how Claude learns to reason, write code, and act autonomously over long horizons. The RL Scaling team works on how RL scales: what happens to throughput, stability, and learning efficiency as models get larger, episodes get longer, and compute grows by orders of magnitude, and what has to change in our algorithms and systems to keep getting returns from that scale. This role sits squarely across research and engineering. You'll develop next-generation architectures and RL algorithms, take them from a small-scale result to a frontier-scale run, and understand every place they behave differently along the way. You'll build the systems that set how fast the team can iterate: how many experiments, at what scale, and how quickly we can trust the results. And you'll work on Anthropic's largest and fastest RL runs, where the gap between a good idea and a working one is often a problem no one has solved yet. Key responsibilities Study how RL training and sampling scale with model size, context length, and compute, and find the algorithmic and systems changes that keep scaling efficient Develop next-generation model architectures and RL algorithms, and make them run efficiently at frontier scale Take promising small-scale results to frontier-scale runs, and diagnose why they behave differently when they get there, whether the cause is numerical, algorithmic, or systemic Build the experimental infrastructure that sets research velocity: fast, reproducible comparisons of architecture and algorithm variants at meaningful scale Own end-to-end performance of our largest RL runs, from research code down to the hardware Build performance and cost models for proposed architecture and algorithm changes, and use them to decide which ideas get scaled Investigate training dynamics at scale, including instabilities, divergence, and throughput regressions, and trace them to root cause Minimum qualifications Deep familiarity with modern transformer language models, including their architecture, training dynamics, and the behavior of large-scale optimization Hands-on experience training large models in a distributed setting, including the tradeoffs between data, tensor, and pipeline parallelism A track record of original technical work in ML training or systems, such as new methods, architectures, or optimizations, demonstrated through research, open-source, or production impact Ability to design rigorous experiments at scale, including baselines, ablations, and enough statistical care to trust a result that costs real compute Ability to reason quantitatively about the compute, memory, and communication costs of a model or algorithm Strong programming skills in Python and JAX or PyTorch, and comfort reading and changing code at every layer of the stack Preferred qualifications Research experience in reinforcement learning, optimization, or large-scale training, published or otherwise Experience developing RL algorithms for language models Experience with scaling laws or other quantitative models of training efficiency Experience designing or modifying transformer architectures beyond standard configurations Experience scaling training to large fleets of accelerators and debugging the problems that only appear at scale Deep understanding of numerics in large-scale training, including low-precision formats and sources of instability Familiarity with how GPU or TPU performance characteristics shape architecture and algorithm choices Experience with C++ or Rust Representative projects Characterize how a new RL algorithm's throughput and learning efficiency change from small models to frontier scale, and fix what breaks Develop a new attention variant, get it working at full scale, and measure how its quality and throughput compare to the baseline Prepare our next largest-ever RL run: find what breaks when model size, context length, and compute all grow at once, and fix it before launch Trace a loss instability that only appears past a certain scale to its root cause, and work out whether the fix belongs in the algorithm, the numerics, or the system Build a model that predicts the throughput and cost of a proposed architecture change before anyone writes the kernel 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 | New York City, NY | Seattle, WA About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role Reinforcement learning is how Claude learns to reason, write code, and act autonomously over long horizons. At frontier scale, an RL run is an unusually demanding distributed system. Training, sampling, and environment execution run concurrently across a large fleet of accelerators and hosts, exchange data continuously, and have to keep making progress while hardware fails, load shifts, and the research changes underneath them. How well that system holds together determines how much of our compute turns into learning, and how quickly the team can try the next idea. As a Research Engineer on the Distributed Systems team within RL Engineering, you'll work on whatever part of that system is the current limit. That might be scheduling and placement, data movement between components, running large numbers of sandboxed environments, storage and checkpointing, networking, fault tolerance, autoscaling, or the observability that tells us what a run is actually doing. We're looking for generalists: engineers who can move between these layers, reason from first principles about a system they haven't seen before, and pick the problem that matters most rather than the one closest to their prior experience. Our system changes as fast as the research does, correctness under failure matters as much as throughput, and the best solutions often come from understanding the ML workload well enough to know which guarantees it actually needs. Strong candidates have built and run large distributed systems, care about getting the details right, and want to apply that experience to a workload that is very large, very heterogeneous, and changing quickly. Key responsibilities Design, build, and operate the distributed systems that run RL at scale, across training, sampling, and environment execution Find and remove whatever currently limits the system, whether it's scheduling, data movement, storage, networking, or coordination Build fault tolerance into every layer: failure detection, isolation, and recovery that keep long-running jobs making progress without human intervention Design resource management and autoscaling so that compute follows demand as a run's needs shift Build observability that makes it possible to understand what a run is doing and why it slowed down, stalled, or produced unexpected results Build automation that detects and remediates common problems, and design interfaces that let engineers and automated tools operate runs safely Work with researchers and performance engineers to make sure systems changes preserve training correctness and don't introduce subtle nondeterminism Remove classes of failure at their source through incident review, testing, and redesign, and write clear design documents for what you build Minimum qualifications Strong software engineering skills in Python and at least one systems language such as Rust, C++, or Go Experience designing, building, and operating large-scale distributed systems in production Deep understanding of distributed systems fundamentals, including consistency, coordination, consensus, failure modes, and recovery Ability to reason quantitatively about throughput, latency, and resource costs across compute, memory, storage, and network Experience debugging complex failures across many hosts and services, including failures you can't reproduce locally Strong written communication, including design documents and incident writeups Preferred qualifications Experience running ML training or inference infrastructure at scale Experience across several layers of the stack, such as scheduling, storage, networking, and orchestration Experience building schedulers, autoscalers, or resource management systems Experience with container orchestration such as Kubernetes, and with sandboxed or virtualized code execution at scale Experience with high-performance networking, RDMA, or collective communication libraries Experience building observability or automated remediation for large fleets Experience with async Python frameworks such as Trio or asyncio Familiarity with reinforcement learning or large language model training workloads Representative projects Design a scheduler that places training, sampling, and environment work across a heterogeneous cluster while respecting network topology and failure domains Build a failure detection and recovery system that lets a long-running job survive host and network failures with minimal lost work Scale environment execution substantially without increasing tail latency for the training step Design an autoscaling policy that rebalances compute across components as a run's bottleneck shifts Build a diagnostics system that explains why a run's throughput dropped and proposes a fix Trace a rare data corruption bug across many services to a race condition in a recovery path, and redesign the path so the class of bug can't recur Design the operational interface for a run so that automated tools can safely diagnose and adjust it under human oversight 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 About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role We are seeking an experienced public opinion researcher to build and expand Anthropic's in-house polling and research function. This role will be fundamental to understanding how key audiences — from policymakers and third-party groups to the general public — perceive and respond to Anthropic's policy positions, messages, and campaigns. This is a foundational role: you'll establish the research function from the ground up, with high autonomy and limited precedent. The Central Policy team sits at the operational heart of Anthropic's policy function — distinct from government relations and communications, but essential to enabling both. You'll work cross-functionally to ensure our policy and communications strategy is grounded in a rigorous understanding of stakeholder sentiment. The polling insights you generate will directly feed into high-visibility campaigns and will be a key asset in our policy efforts in Washington and beyond. Anthropic is equal parts research lab, policy think-tank, and technology startup. We care deeply about the safe development of AI systems and build partnerships with governments through proactive, opinionated, substantive policy conversations. This role offers a unique opportunity to be at the forefront of AI policy, bringing rigorous research discipline to policy strategy. Success in this role means establishing trusted relationships with leading polling firms around the world, delivering high-quality research on a regular cadence, and becoming the internal expert on how key audiences perceive AI, Anthropic, and Anthropic's positions and messages. You'll translate polling insights into strategic recommendations that measurably improve campaign effectiveness, and build scalable processes that allow the function to grow efficiently as demand increases. Key responsibilities Develop a comprehensive strategy for our in-house political research function that aligns with Anthropic's policy goals Identify, evaluate, and establish relationships with leading research and polling firms, ensuring coverage across demographics, geographies, and political perspectives Conduct detailed, proactive research to test core messages, policy proposals, and campaign themes, and to evaluate Anthropic's brand perception and performance Execute rapid-response polling to quickly assess Anthropic's messaging strategy in response to external events, policymaker actions, competitive developments, or emerging opportunities Work with external affairs colleagues (federal, state, and international) and policy communications to identify research priorities, interpret findings, and translate insights into actionable strategic recommendations Develop and maintain a knowledge management system for polling data, insights, and recommendations that ensures findings are accessible and actionable Manage polling vendors at scale, including vendor performance management Participate in strategy sessions and provide thought partnership on the design of messages, policy proposals, and campaigns ahead of polling and testing Create clear, compelling deliverables that distill polling findings into insights and recommendations Identify opportunities to build and scale the polling function over time, including staffing needs, process improvements, and capability expansion Track and report on the impact and utility of research, measuring how insights translate into more effective campaigns and strategic decisions Minimum qualifications Experience designing and executing public opinion research, including survey and questionnaire design, sampling approaches, and message testing Ability to analyze quantitative and qualitative polling data and translate findings into clear, decision-ready written recommendations Experience selecting, briefing, and managing external polling or research vendors Strong writing and presentation skills, including distilling complex research for senior, non-technical audiences Preferred qualifications 8+ years of professional experience in political or public opinion research, ideally spanning multiple campaign cycles and policy environments Corporate public affairs experience, ideally in a highly scrutinized or regulated industry Strategic thinking that spots emergent issues, identifies and tests the right messages, and translates those insights into tactical recommendations A track record of building relationships with vendors, external experts, and complex stakeholder ecosystems Strong organizational skills, with the ability to manage multiple projects simultaneously, maintain clear priorities, and deliver on tight timelines Comfort operating in ambiguous environments, taking ownership of problems, and proactively driving initiatives with limited precedent A resourceful, collaborative working style and genuine commitment to being a strong teammate Passion for AI policy and alignment with Anthropic's safety mission and commitment to responsible AI development 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: $230,000 — $265,000 USD Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. 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: New York, New York, United States About the Role iCapital is looking to hire a Head of Research and Market Development to build and lead iCapital's market intelligence function, delivering actionable insights across private markets, wealth management, and financial technology. This role combines market research, client and advisor insights, competitive intelligence, and business analysis to inform strategic decision-making across Product, Marketing, Sales, Distribution, Corporate Development, and executive leadership. This individual will translate complex market dynamics into clear recommendations that strengthen iCapital's competitive position, identify growth opportunities, and support long-term business expansion. Working across global teams and external partners, this individual will establish the frameworks, governance, and reporting capabilities needed to scale a best-in-class market intelligence function. This role will serve as a trusted advisor to senior leadership while advancing iCapital's position as a thought leader in the evolving wealth and alternatives landscape. Responsibilities Build and lead a centralized market intelligence function, including its vision, operating model, governance framework, roadmap, and long-term team structure. Develop scalable research, reporting, dashboards, executive briefings, and knowledge-sharing resources that support enterprise-wide decision-making. Serve as the firm's central resource for market research, client and advisor insights, competitive intelligence, market development initiatives, and external research relationships. Monitor market, competitive, regulatory, and industry trends across investment management, wealth management, financial technology, private markets, hedge funds, structured investments, insurance, retirement, and advisor distribution. Produce market landscape assessments, opportunity analyses, adoption studies, and business intelligence that inform strategic planning and commercial priorities. Partner with Product, Marketing, Sales, Distribution, Corporate Development and Strategy, and international business leaders to inform product strategy, positioning, messaging, growth initiatives, and expansion opportunities. Identify emerging market opportunities, competitive whitespace, partnership opportunities, and evolving client needs that support innovation and platform differentiation. Design and lead primary research initiatives while delivering executive presentations, strategic recommendations, and thought leadership that promote market awareness and data-driven decision-making across the organization. Qualifications 15+ years of experience in market research, market intelligence, strategy, consulting, business insights, or related functions within financial services, wealth management, asset management, alternatives, or fintech MBA or equivalent advanced degree is preferred Proven experience building, scaling, and leading market intelligence, market development, strategic insights, or research organizations Deep understanding of private markets, alternative investments, advisor distribution, wealth management, portfolio construction, and related investment products and platforms Strong analytical, strategic, and problem-solving skills with the ability to synthesize complex data, market dynamics, and business trends into actionable recommendations Demonstrated success influencing senior executives, advising cross-functional stakeholders, and driving enterprise-wide initiatives Exceptional written, verbal, and presentation skills with experience delivering executive-level insights and recommendations Experience leveraging market research, competitive intelligence, analytics platforms, and external industry relationships to support business growth and decision-making Benefits The base salary range for this role is $190,000 to $215,000.  iCapital offers a compensation package which includes salary, equity for all full-time employees, and an annual performance bonus. Employees also receive a comprehensive benefits package that includes an employer matched retirement plan, generously subsidized healthcare with 100% employer paid dental, vision, telemedicine, and virtual mental health counseling, parental leave, and unlimited paid time off (PTO). We believe the best ideas and innovation happen when we are together. Employees in this role will work in the office Monday-Thursday, with the flexibility to work remotely on Friday. For additional information on iCapital, please visit https://www.icapitalnetwork.com/about-us Twitter: @icapitalnetwork | LinkedIn: https://www.linkedin.com/company/icapital-network-inc | Awards Disclaimer: https://www.icapitalnetwork.com/about-us/recognition/ iCapital is proud to be an Equal Employment Opportunity and Affirmative Action employer. We do not discriminate based upon race, religion, color, national origin, gender, sexual orientation, gender identity, age, status as a protected veteran, status as an individual with a disability, or other applicable legally protected characteristics.

Location: Andorra la Vella, Andorra; Buenos Aires, Argentina; Brasilia, Brazil; Rio de Janeiro, Brazil; Barcelona, Spain; Madrid, Spain; Milano, Italy; Rome, Italy; Abu Dhabi, United Arab Emirates; London, United Kingdom; Lugano, Switzerland; Warsaw, Poland; Amsterdam, Netherlands; Lisbon, Portugal; Dublin, Ireland; Bucharest, Romania; Bangalore, India; Kuala Lumpur, Malaysia Workplace: remote Join Tether and Shape the Future of Digital Finance At Tether, we’re not just building products, we’re pioneering a global financial revolution. Our cutting-edge solutions empower businesses—from exchanges and wallets to payment processors and ATMs—to seamlessly integrate reserve-backed tokens across blockchains. By harnessing the power of blockchain technology, Tether enables you to store, send, and receive digital tokens instantly, securely, and globally, all at a fraction of the cost. Transparency is the bedrock of everything we do, ensuring trust in every transaction. Innovate with Tether Tether Finance: Our innovative product suite features the world’s most trusted stablecoin, USDT , relied upon by hundreds of millions worldwide, alongside pioneering digital asset tokenization services. But that’s just the beginning: Tether Power: Driving sustainable growth, our energy solutions optimize excess power for Bitcoin mining using eco-friendly practices in state-of-the-art, geo-diverse facilities. Tether Data: Fueling breakthroughs in AI and peer-to-peer technology, we reduce infrastructure costs and enhance global communications with cutting-edge solutions like KEET , our flagship app that redefines secure and private data sharing. Tether Education : Democratizing access to top-tier digital learning, we empower individuals to thrive in the digital and gig economies, driving global growth and opportunity. Tether Evolution : At the intersection of technology and human potential, we are pushing the boundaries of what is possible, crafting a future where innovation and human capabilities merge in powerful, unprecedented ways. Why Join Us? Our team is a global talent powerhouse, working remotely from every corner of the world. If you’re passionate about making a mark in the fintech space, this is your opportunity to collaborate with some of the brightest minds, pushing boundaries and setting new standards. We’ve grown fast, stayed lean, and secured our place as a leader in the industry. If you have excellent English communication skills and are ready to contribute to the most innovative platform on the planet, Tether is the place for you. Are you ready to be part of the future? About the job As a member of our AI model team, you will drive innovation in model serving and inference architectures for advanced AI systems. Your work will focus on optimizing model deployment and inference strategies to deliver highly responsive, efficient, and scalable performance across real-world applications. You will work on a wide spectrum of systems, ranging from resource-efficient models designed for limited hardware environments to complex, multi-modal architectures that integrate data such as text, images, and audio. We expect you to have deep expertise in designing and optimizing model serving pipelines and inference frameworks as well as a strong background in advanced model architectures. You will adopt a hands-on, research-driven approach to develop, test, and implement novel serving strategies and inference algorithms. Your responsibilities include engineering robust inference pipelines, establishing comprehensive performance metrics, and identifying and resolving bottlenecks in production environments. The ultimate goal is to enable high-throughput, low-latency, low-memory footprint, and scalable AI performance that delivers tangible value in dynamic, real-world scenarios. Responsibilities Design and deploy state-of-the-art model serving architectures that deliver high throughput and low latency while optimizing memory usage. Ensure these pipelines run efficiently across diverse environments, including resource-constrained devices and edge platforms. Establish clear performance targets such as reduced latency, improved token response, and minimized memory footprint. Build, run, and monitor controlled inference tests in both simulated and live production environments. Track key performance indicators such as response latency, throughput, memory consumption, and error rates, with special attention to metrics specific to resource-constrained devices. Document iterative results and compare outcomes against established benchmarks to validate performance across platforms. Identify and prepare high-quality test datasets and simulation scenarios tailored to real-world deployment challenges, specifically those encountered on low-resource devices. Set measurable criteria to ensure that these resources effectively evaluate model performance, latency, and memory utilization under various operational conditions. Analyze computational efficiency and diagnose bottlenecks in the serving pipeline by monitoring both processing and memory metrics. Address issues such as suboptimal batch processing, network delays, and high memory usage to optimize the serving infrastructure for scalability and reliability on resource-constrained systems. Work closely with cross-functional teams to integrate optimized serving and inference frameworks into production pipelines designed for edge and on-device applications. Define clear success metrics such as improved real-world performance, low error rates, robust scalability, optimal memory usage and ensure continuous monitoring and iterative refinements for sustained improvements. A degree in Computer Science  or related field. Ideally PhD in NLP, Machine Learning, or a related field, complemented by a solid track record in AI R&D (with good publications in A* conferences). Must have knowledge of Metal Shading Language (MSL). You should be comfortable writing custom compute shaders from scratch. Proven experience in low-level kernel optimizations and inference optimization on mobile devices is essential. Your contributions should have led to measurable improvements in inference latency, throughput, and memory footprint for domain-specific applications, particularly on resource-constrained devices and edge platforms. A deep understanding of modern model serving architectures and inference optimization techniques is required. This includes state-of-the-art methods for achieving low-latency, high-throughput performance, and efficient memory management in diverse, resource-constrained deployment scenarios. Must have strong expertise in writing GPU kernels for mobile devices (i.e., smartphones) as well as a deep understanding of model serving frameworks and engines. Practical experience in developing and deploying end-to-end inference pipelines, from optimizing models for efficient serving to integrating these solutions on resource-constrained devices is required. Demonstrated ability to apply empirical research to overcome challenges in model serving, such as latency optimization, computational bottlenecks, and memory constraints. You should be proficient in designing robust evaluation frameworks and iterating on optimization strategies to continuously push the boundaries of inference performance and system efficiency. Distributed Inference Systems: Designing and optimizing high-performance inference engines using techniques like Tensor Parallelism, Pipeline Parallelism, and Expert Parallelism to handle massive models on GPU clusters. Deep understanding of the math and structure behind Diffusion Models and Vision Transformers Understanding of Pruning, Quantization, Flash attention, KV Cache, Speculative Decoding (Eagle) etc. Important information for candidates Recruitment scams have become increasingly common. To protect yourself, please keep the following in mind when applying for roles: Apply only through our official channels. We do not use third-party platforms or agencies for recruitment unless clearly stated. All open roles are listed on our official careers page: https://tether.recruitee.com/ Verify the recruiter’s identity. All our recruiters have verified LinkedIn profiles. If you’re unsure, you can confirm their identity by checking their profile or contacting us through our website. Be cautious of unusual communication methods. We do not conduct interviews over WhatsApp, Telegram, or SMS. All communication is done through official company emails and platforms. Double-check email addresses. All communication from us will come from emails ending in @ tether.to or @ tether.io We will never request payment or financial details. If someone asks for personal financial information or payment at any point during the hiring process, it is a scam. Please report it immediately. When in doubt, feel free to reach out through our official website.