Удалённая работа

Задачи: — Вести подбор «под ключ»: от снятия заявки до выхода сотрудника на рабочее место. — Быть активным в поиске: использовать все доступные инструменты — от классических джоб-бордов до прямого поиска и нетворкинга. — Оценивать людей: проводить интервью (телефонные и видео), глубоко анализировать компетенции и софт-скиллы, собирать рекомендации. — Заботиться о кандидатах: сопровождать их на всех этапах, чтобы у каждого оставалось приятное впечатление о компании. — Развивать процессы: вести базу, работать с кадровым резервом, помогать новичкам в адаптации и готовить аналитику для HR-директора. Требования: — Опыт в рекрутменте от 1 года, понимание полного цикла найма. — Умение грамотно говорить и писать, легко находить общий язык с людьми, не теряться в потоке задач. — Организованность: умение планировать день, соблюдать дедлайны, работать с цифрами и отчетами. — Высшее образование обязательно. — Приветствуется опыт закрытия сложных вакансий, работы с удаленным наймом и выстраивания системы адаптации. Условия: — Достойный доход: на испытательный срок (1 месяц) — 150 000 руб. «на руки», после успешного прохождения — 200 000 руб. «на руки». — Официальное оформление по ТК РФ со всеми гарантиями. — Удаленный формат работы по московскому времени. — График 5/2, с 9:00 до 19:00 (понедельник-пятница). — Возможность развития, предлагать идеи, улучшать процессы и расти вместе с компанией. — Руководство открыто к диалогу и поддержке инициатив.

Задачи: — Настройка и оптимизация Яндекс Директ: Поиск, РСЯ, брендовые кампании. — Настройка и ведение таргетированной рекламы VK. — Avito — аналитика объявлений, звонков, эффективности и курирование канала. — Контроль и анализ SEO: понимание принципов продвижения, постановка задач подрядчикам. — Работа с call-tracking, источниками обращений, лидами и стоимостью привлечения. — Подготовка отчётности и анализ эффективности рекламных каналов. — Работа с нейросетями: тексты, изображения, видео, отчёты, создание AI-ассистентов. — При необходимости — ведение контента в группе VK. Требования: — Опыт работы интернет-маркетологом / таргетологом / директологом от 2 лет. — Уверенный практический опыт с Яндекс Директ и VK Ads. — Опыт работы с Avito и аналитикой канала. — Понимание SEO и работы с call-tracking. — Уверенный Excel. — Активное использование нейросетей в работе. — Наличие успешных кейсов и портфолио с цифрами. — Самостоятельность, высокая скорость работы и стрессоустойчивость. — Опыт в сфере автозапчастей будет преимуществом. Условия: — Стабильный уровень дохода - оклад + KPI. — Удалённая работа, график 5/2, с 9:00 до 18:00. — Стабильный объём задач и рекламный бюджет для реализации своих идей. — Возможность влиять на маркетинговые показатели и предлагать собственные решения. — Работа с несколькими digital-каналами, без рутинного ведения только одного направления. — Оформление в течение первой недели.

Компания IMPRODUCT — эксклюзивный дистрибьютор препаратов в области эстетической медицины, представляет на российском рынке бренды Promoitalia (Италия) и Dermaceutic (Франция). Целевая аудитория — врачи-косметологи, гинекологи, клиники, дистрибьюторы и конечные покупатели космецевтических средств. Задачи аккаунта IMPRODUCT (B2P и B2B): — Создание экспертного аккаунта для косметологов с практическими знаниями о продуктах: составы, свойства, показания, безопасность и протоколы применения — Демонстрация эффективности через кейсы и результаты «до / после» — Создание экспертного контента, интересного для применения в работе — Формирование премиального образа бренда через визуал и качество подачи — Создание профессионального сообщества через мероприятия, обучение, вебинары и контент с них Задачи аккаунта Dermaceutic (70% B2C, 30% B2P и B2B): — Повышение доверия к бренду и стимулирование продаж через понятный, экспертный и визуально привлекательный контент — Рассказы о продуктах, их действии, показаниях к применению — Раскрытие протоколов применения при различных проблемах — Поднятие распространённых проблем и ошибок в уходе — Создание познавательного и lifestyle-контента — Демонстрация эффективности продуктов через форматы до/после — Поддержка премиального визуального позиционирования бренда — Регулярная адаптация коммуникации под сезонные потребности кожи Обязанности в обоих аккаунтах: — Разработка и реализация SMM-стратегии: анализ конкурентов и трендов, тестирование новых механик соцсетей — Планирование контента: ежемесячное формирование контент-плана с учётом мероприятий, новинок и акций — Создание и публикация контента: написание текстов, разработка дизайна, съемка и монтаж видео, генерация реалистичного AI-контента, соблюдение фирменного стиля и сроков — Оформление и поддержка аккаунтов: био, ссылки, Highlights, обложки, оформление канала ВКонтакте, UTM-метки — Развитие партнёрских размещений: поиск и коммуникация с блогерами, креаторами, экспертами, амбассадорами и клиниками; ведение базы и планирование размещений — Сопровождение интеграций: подготовка ТЗ, сценариев и контента для партнёров, отслеживание публикаций, обеспечение кросс-промо — Поддержка коммуникации с аудиторией: ответы на комментарии и сообщения, работа с обратной связью — Анализ результатов: ежемесячная подготовка отчёта по динамике подписчиков, охватам, лидам и эффективности контента с рекомендациями — Участие в офлайн-съёмках: выезды в офис, к врачам, амбассадорам и на мероприятия в Москве Площадки ведения: — Instagram — Telegram — ВКонтакте — Дзен Требования к количеству и формату публикаций: — Stories: ежедневно — Посты: 5-6 в неделю (Reels: 80%, графические посты/карусели: 20%) — Не менее 20 постов в месяц на каждой площадке Требования к кандидату: — Опыт работы в SMM от 2 лет — Умение разрабатывать стратегию и анализировать результаты — Понимание особенностей и специфики разных рекламных площадок — Умение эффективно организовывать работу при высокой нагрузке и многозадачности — Навык грамотной постановки ТЗ подрядчикам и контроля их выполнения — Опыт работы с проектами в сфере beauty и/или медицины будет плюсом — Знание процессов работы с рекламными размещениями (маркировка рекламы, отчетность в ОРД и др.) будет плюсом Условия: — Стабильная оплата: оклад 120 000 руб. gross после испытательного срока (3 месяца) — Удалённый график работы — Скидка 40% на продукцию компании — Возможность профессионального и финансового роста Тестовое задание: — Перед финальным этапом отбора необходимо выполнить тестовое задание: создать 1 Reels для любого из аккаунтов на основе предоставленных материалов — Оценка будет по идее, наличию хука, качеству визуальной подачи, работе с текстом, монтажу и вниманию к деталям В сопроводительном письме необходимо начать с фразы: «Я точно подхожу, потому что...» и прикрепить ссылки на аккаунты и примеры постов.

Задачи: — Подготовка и запуск регулярных рассылок по базе с учётом сегмента и задачи — Настройка повторных касаний и сценариев возврата для пользователей, которые не ответили или не совершили целевое действие — Поддержание порядка в базе и актуальности сегментов — Создание и поддержка сегментов пользователей — Email-маркетинг: подготовка и запуск email-рассылок через специализированные сервисы — Контроль доставляемости и ключевых показателей: открываемость, клики, конверсии — Реализация CRM-гипотез: запуск тестовых рассылок и новых механик — Настройка выдачи материалов через CRM-цепочки и ботов — Контроль всей цепочки — от первого касания до получения материала пользователем — Настройка меток и регулярный анализ эффективности рассылок Требования: — Опыт в CRM-маркетинге или digital-маркетинге от 1 года — Практический опыт работы с Salebot: создание рассылок, сегментов и воронок — Опыт работы с email-сервисами — Навыки работы с базами, сегментами и CRM-воронками — Понимание ключевых CRM-метрик и умение анализировать открытия, клики и конверсии — Умение самостоятельно делать выводы из данных и предлагать решения — Грамотная письменная речь Условия: — Доход 40 000 ₽ фикс + до 10 000 ₽ за выполнение KPI — Оформление по самозанятости — Полностью удалённый формат — Задачи в промежутке с 10:00 до 19:00, пн–пт, с гибким распределением нагрузки — Проектная занятость с возможностью перехода на постоянную основу

Задачи: — Создавать вирусный контент, от которого невозможно оторваться — Монтировать 2 Reels в день, которые собирают тысячи просмотров — Оформлять 2 продающие карусели, превращающие просмотры в заявки — Разрабатывать контент-стратегию для двух направлений: турагентство и онлайн-школа турагентов — Анализировать эффективность и масштабировать успешные форматы Требования: — Опыт 1–3 года в SMM — Портфолио с успешными кейсами по лидогенерации — Умение создавать вирусный контент — Reels с тысячами просмотров, карусели с сотнями реакций — Знание трендов Instagram, Telegram, YouTube, VK и Max — Навыки монтажа видео (CapCut, InShot, Adobe Premiere Pro и др.) и базового дизайна (Canva, Figma, Photoshop) — Умение работать с аналитическими инструментами (встроенные статистики соцсетей, Popsters, DataFan и др.) — Креативность, визуальное мышление, аналитический склад ума, ответственность, инициативность Условия: — Оплата от 80 000 рублей (фикс + KPI) — Гибкий график 9:00–18:00 (МСК), возможность удалённой работы — Два направления для творчества — туризм и образование — Скидки на туры для сотрудников Ключевые метрики успеха: — Рост охватов и просмотров контента — Увеличение вовлечённости (ER, реакции, репосты) — Рост количества входящих заявок — Выполнение контент-плана по объёму и качеству

Задачи: — Детальное проектирование сервиса (СУБД/back/front/API) с учетом требований безопасности, отказоустойчивости, времени отклика и т.п. — Разработка бэкэнд-части высоконагруженной системы. — Разработка интеграционных сервисов и микросервисных компонентов. — Рефакторинг и оптимизация кода, code review. — Участие в тестировании разработанных компонентов и исправлении дефектов. — Участие в принятии архитектурных решений и исследовательских задачах. — Участие в разработке и согласовании API front-back. — Взаимодействие с разработчиками, дизайнерами и другими командами. — Наставничество, развитие технической экспертизы в команде. Требования: — Опыт работы в роли разработчика от 7 лет в проектах по разработке и/или модификации и/или внедрению ПО с использованием инструментов и технологий Java. — Опыт работы со всеми указанными технологиями по направлению Java: Spring, Hibernate, SQL, Gradle\Maven, Git, Jenkins, Docker, Kubernetes, Openshift, Postgres. — Опыт командной разработки с использованием программных продуктов Bitbucket, Confluence и Jira. — Опыт работы, как в каскадных (waterfall), так и в гибких методологиях (Agile и/или Scrum) разработки. Условия: — Оформление по ИП. — Стабильный и прозрачный доход. — Удаленная работа.

Location: United States (Remote) Workplace: Remote ABOUT ONEPAY OnePay is the consumer fintech trusted by millions of Americans to make money better. Our financial system is broken. High fees, low rates, and too few ways to actually grow your money. We’re fixing it. And we’re moving fast. We’re an all-in-one financial services platform that brings together banking, high-yield savings, credit cards, point-of-sale lending, investing, and crypto in one place. We also partner with employers, HCM providers, gig platforms, and others to deliver embedded financial services to millions of employees and frontline workers. We’re backed by Walmart, the world’s largest retailer, and Ribbit Capital, one of fintech’s most respected investors, giving us rare scale, distribution, and the opportunity to build something truly category-defining. But what really sets OnePay apart is how we move. Our customers don’t have time to wait… and neither do we. This place moves fast, and we’re looking for people who are: - Ready to run - Hungry and driven by urgency - Exceptional at what they do, with low ego - Comfortable operating in motion THE ROLE The @Work team is responsible for OnePay's employer benefit features, including Instapay, savings, and budgeting. As a Full Stack Engineer at OnePay, you will build simple and beautiful experiences for people's crucial daily interactions with their money, earnings, and pay schedules. WHAT YOU’LL DO - Lead technical design and implementation for projects powering our @work platform - Set architectural standards and ensure best practices in code quality, performance, and scalability - Partner closely with product, design, and operations to shape and deliver @Work product strategy - Oversee optimization of systems for reliability, security, and scalability - Own technical decision-making, balancing rapid delivery and sustainable solutions YOU BRING - 7+ years of experience building and scaling distributed systems, high-throughput APIs, or data-intensive services - Solid hands-on full-stack engineering skills, with proficiency in backend architecture and modern frontend frameworks - Experience designing or working on reliable, secure, and high-performance systems -ideally in payments, EWA, or other regulated consumer finance domains - Clear communication skills and a collaborative working style - Initiative and growth mindset - eager to learn and contribute as a team player TOOLS WE USE We use Typescript front to back, with node and NestJS frameworks for server code and React Native for our iOS, Android, and Web clients. We run on a highly distributed cloud-native stack with Kubernetes and AWS, and scale and resiliency are at the heart of everything we build. We also embrace agentic development, so engineers have their choice of a variety of models and agent harnesses, including Claude Code, Cursor, Codex, and more to fit their workflow. While you don't need experience with our exact stack, familiarity with modern software engineering practices will help you ramp up quickly. WHAT WE OFFER - Competitive base salary, stock options, and health benefits from Day 1 - 401(k) plan with company match - Remote-friendly (US), flexible time off (FTO), and opportunities for growth - A high-growth, mission-driven, inclusive culture where your work has real impact STANDARD INTERVIEW PROCESS Our process varies by role. Most candidates go through: - AI-assisted initial screen - Interview with Talent Partner - Technical or Hiring Manager Interview - Team Interview - Executive Interview - Offer! EQUAL EMPLOYMENT OPPORTUNITY To build technology and products that are used and loved by people and solve real-world problems, we need to build a team with many different perspectives and experiences. We are an equal opportunity employer. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status. We encourage candidates from all backgrounds to apply. Applicants in need of special assistance or accommodation during the interview process or in accessing our website may contact us at talent@onepay.com.

Location: Remote - US About the Job: We’re looking for an Engineering Manager to lead LaunchDarkly’s Cloud Engineering team, the team responsible for the foundational infrastructure our products and engineering teams depend on. Cloud Engineering owns LaunchDarkly’s AWS accounts and networking, multi-region Kubernetes infrastructure, Terraform and GitOps workflows, CockroachDB operations, disaster-recovery capabilities, and FedRAMP infrastructure posture. The team is small, senior, and recently rebuilt. This is a frontline leadership role for a manager who treats operational health as a top priority, leads calmly through incidents, and creates leverage through software, automation, and reliable paved paths. In your first year, you’ll lead a marquee infrastructure insourcing program from planning through completion. The program’s cost savings will help fund additional team growth, giving you the opportunity to deliver meaningful business impact while strengthening the infrastructure behind LaunchDarkly’s next stage of scale. This role combines people leadership, operational ownership, and technical judgment. You do not need to be the most senior individual contributor in the room, but you should be comfortable partnering with experienced engineers, guiding tradeoffs, and creating an environment where the team can do its best work. Responsibilities: Lead and develop a senior Cloud Engineering team, providing clear expectations, coaching, feedback, performance management, and career-development support. Own the operational health of LaunchDarkly’s cloud infrastructure, including incident response, on-call health, service-level objectives, resilience, and disaster recovery. Launch and deliver a multi-quarter infrastructure insourcing program, including sequencing work, managing dependencies, communicating milestones, and ensuring a successful transition. Guide technical priorities and tradeoffs across AWS infrastructure, networking, multi-region Kubernetes, infrastructure as code, GitOps, datastore operations, and compliance-related infrastructure. Build leverage through software, automation, and paved paths that help product engineering teams use infrastructure safely and independently. Support migrations by providing guidance, patterns, and enablement while maintaining clear ownership boundaries with partner teams. Establish team norms for effective AI-assisted engineering and identify opportunities to reduce operational toil through AI and automation. Maintain team health and continuity through change, fostering an inclusive, collaborative, and accountable environment. Partner with Service Frameworks, Core Platform, Revenue Platform, Data Platform, and product engineering teams on shared infrastructure priorities and cross-team dependencies. Communicate roadmaps, operational risks, investment needs, and progress clearly to engineering leadership and cross-functional stakeholders. Participate in hiring and team planning as the organization grows. Qualifications: 2+ years of experience managing an infrastructure, platform engineering, or site reliability engineering team at a product company, including people management of an on-call team. Demonstrated ownership of incident response, on-call health, and service-level objective frameworks for critical production systems. Experience leading at least one multi-quarter infrastructure migration or insourcing program from planning through completion. Technical fluency with AWS, Kubernetes, and infrastructure-as-code practices, with the judgment to guide discussions and tradeoffs with senior engineers. Experience taking over an established team and maintaining engagement, performance, and retention through a leadership transition. A clear point of view on team norms and practices for AI-assisted engineering. Strong written and verbal communication skills, with the ability to explain technical and operational priorities to a range of audiences. A thoughtful, calm, and accountable leadership approach, particularly during incidents and periods of change. Nice to Haves Experience with vendor negotiations or cloud cost management. Experience supporting FedRAMP or other compliance-related infrastructure and operations. Experience operating large-scale datastores or OLAP systems. Experience growing a team through funded expansion. Pay: Target pay ranges based on Geographic Zones* for Level M2: Zone 1: San Francisco/Bay Area or NYC Metropolitan Area, Boston, Seattle - $191,800 - $263,670** Zone 2: Irvine, LA, Monterey, Santa Barbara, Santa Rosa, Austin, Portland, Philadelphia, Chicago - $172,600 - $237,270** Zone 3: All other US locations - $163,000 - $224,070** All Zones Inclusive of a 10% Bonus Target LaunchDarkly operates from a place of high trust and transparency; we are happy to state the pay range for our open roles to best align with your needs. Exact compensation may vary based on skills, experience, and location. *Within the United States, our geographic pay zones are defined by counties surrounding major metropolitan areas. **Restricted Stock Units (RSUs), health, vision, and dental insurance, and mental health benefits in addition to salary. About LaunchDarkly: Modern software delivery was supposed to be the foundation for a thriving digital business but reality has proven otherwise. Slow, inefficient development cycles, costly outages, and fragmented customer experiences are preventing developers from building their best software. The LaunchDarkly platform helps developers innovate on new features faster while protecting them with a safety valve to instantly rewind when things go wrong. Developers can target product experiences to any customer segment and maximize the business impact of every feature. And by gradually rolling out new application components, they escape nightmare "big-bang" technology migrations. The LaunchDarkly platform was built to guide engineers to the next frontier of DevOps by: Improving the velocity and stability of software releases, without the fear of end customer outages Delivering targeted experiences by easily personalizing features to customer cohorts Maximizing the business impact of every feature through the ability to experiment and optimize Coordinating the release and optimization of software to provide consistent experiences across mobile platforms and device types Improving the effectiveness and productivity of engineering teams, by providing insights into engineering cadence and stability At LaunchDarkly, we believe in the power of teams. We're building a team that is humble, open, collaborative, respectful and kind. We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, gender identity, sexual orientation, age, marital status, veteran status, or disability status. LD invites any applicant to review our written Affirmative Action Plan. To do so, contact People Ops at hr@launchdarkly.com . Do you need a disability accommodation? Fill out this accommodations request form and someone from our People Operations team will contact you for assistance. Your safety matters to us. To protect yourself from potential scams, LaunchDarkly recruiters will only contact you from @ launchdarkly.com email addresses or via LinkedIn from "Verified Recruiter" accounts. Be cautious of emails from other domains.  Legitimate LaunchDarkly recruiters will never ask for money, fees, or banking information before making a job offer. LaunchDarkly will never make a job offer without conducting a formal interview process. Our interview process does not involve asking detailed questions by email. If you are ever unsure about a communication that you receive, don't click any links—visit Careers | LaunchDarkly directly for confirmed job openings and links to apply. Please notify us of any fraudulent representation by sending an email to careers@launchdarkly.com .

Location: United States (Remote) Workplace: Remote About Teleport Don't wait for the future of infrastructure. Be part of it. Teleport is the AI Infrastructure Identity Company. We're solving one of the hardest problems in security: giving every human, machine, workload, and AI agent a cryptographically secured identity, improving engineering velocity while maintaining security. We make trusted computing simple. This gives you the freedom, power, and autonomy to build and innovate with confidence. Remote-first and globally distributed, we work with companies like Nasdaq, DoorDash, and Elastic to secure infrastructure for an AI world. About the Role: Our Teleport Account Development Mission: Build a consistent pipeline. Provide relevant value to future customers. Enable the next Teleport leaders.   What You'll Do: - Support multiple Enterprise sales territories - Schedule meetings with key stakeholders from three sources - Outbound prospecting to good-fit accounts showing intent signals - Qualifying Marketing Qualified Leads - Qualifying Inbound requests Collaborate with your Account Executive(s) on enterprise accounts and to refine call and messaging strategies - True multi-channel outreach (call/email/social) with a heavy emphasis on using the phone to drive quality conversations - Establish best practices and efficient processes for a modern Outbound sales motion - Work closely with Account Executive(s) to refine account strategiesIdentify trends from prospective customers to share with the product, marketing, and customer success teams - Track all sales activity in our CRM (Salesforce) and Outreach - Achieve or exceed monthly quotas for New Meetings Completed and quarterly quotas for Pipeline Opportunities   What We're Looking For: - Experienced: have 1 -2 years experience as an Enterprise Account Development Representative (ADR / SDR / BDR), preferably at a SaaS company with a technical product and a heavy emphasis on growing Outbound pipeline. - Strategic: able to strategize with Sales peer to schedule conversation with new Ideal Customer Profile Enterprise accounts - Quick Learner: able to grasp new technical concepts quickly in a diverse SaaS space - Collaborative Team Player: able to collaborate with your team and other teams daily - Passionate: loves the craft/process and providing the best experience for the prospect - Organized: able to manage pipeline of multiple prospects within multiple accounts in a consistent, repeatable way - Adaptable: understands that start ups can change quickly and able to adapt to changes quickly - Coachable: able to take constructive criticism and turn it into something great to further your career goals   What Success Looks Like: - Month 1: 4 week structured onboarding that helps you learn the market, the personas, the tools, the problems that Teleport helps solve; start reaching out to prospects before the end of the month - Month 2: On a half quota, figuring out the best way to use the tools at your disposal to ramp up your territory - Month 3: Now on a full quota, your process is up and running and you are on your way to building your personal prospecting pipeline machine   Tools We Have: - Salesforce - Outreach - LinkedIn SalesNavigator - 6sense - Qualified Chat - Qualified Signals - G2 - AltiSales - Common Room   How We Hire: Our process is designed to be straightforward and respectful of your time. We skip performative rounds and focus on what matters: understanding how you think and what you can do. Why Teleport You're joining a company where the problem is real, the team is small, and your work shows up directly in the product. We're not a big company, you won't get lost in a crowd. You'll have the freedom and autonomy to do what you're great at, alongside teammates who care about doing it right and want to see you succeed. The work is collaborative, there's real room to grow, and the mission is one worth showing up for. Remote-first and globally distributed, we're genuinely passionate about what we're building. The Benefits At Teleport, we believe your career is more important than a list of perks. That's why we focus on making your day-to-day the best it can be — giving you the autonomy, access, and support to do the best work of your career. - Extensive health coverage - Annual expense budget - Rest and recovery policies that maximize your ability to recharge - Investment in your future with retirement savings plans - Professional development opportunities Teleport is an equal opportunity employer and does not discriminate against any employee or applicant on the basis of age, color, disability, gender, national origin, race, religion, sexual orientation, veteran status, or any classifications protected by federal, state, or local law. Candidate Privacy Notice: For information about our collection and processing of job applicant personal data for this position, please see our Job Applicant Privacy Policy and Notice of Collection at goteleport.com/legal/apply/job-applicant/ http://goteleport.com/legal/apply/job-applicant/

Location: USA - Remote Workplace: Remote 💫 This job description was written by a person ABOUT US Since 2013, we’ve been building a CRM that gets out of your way and helps your team sell more, faster. Now we’re building AI into every part of it, so Close does the busywork and your team does the selling. No manual data entry, no 10-click workflows. Just communication-first, AI-powered sales software designed to help you succeed and scale. We're bootstrapped and profitable which means we answer to our customers and play by our rules. We're proud of our 130-person, 100% remote team, focused on building Close so that no small, scaling business fails because it can't figure out sales. ABOUT THE ROLE One of our values is “Build a house you want to live in.” In this metaphor, Close.com http://Close.com is the front door. Our website is where people learn about who we are, what we do and, even more importantly, it’s a critical touch point on their journey to becoming a Close customer. We’re dedicated to making it best-in-class. We’ve recently migrated from Webflow to our own codebase (Astro, Sanity, Vercel) so a new and improved foundation is in place. We believe building in-house gives us the flexibility, creativity, and speed to make our website a competitive advantage - this is where our first Sr. Frontend Engineer - Marketing comes in. As the technical owner of the website, you’ll be responsible for maintaining, optimizing, and instrumenting Close.com http://Close.com. You’ll work closely with Marketing, including a Marketing Designer, but report up through Engineering. YOU ARE - A marketing website engineer. You’ve built beautiful, conversion-focused websites for a while now - probably 5+ years. You may have worked in an agency, but you’ve also had to build, own, and collaborate on websites long-term - seeing your work through impact and likely many iterations. Motion, ambitious CSS, and rich media are second nature, and you sweat page speed and how the site reads to both search engines and AI agents as much as how cool it looks. A modern stack like Astro paired with a headless CMS (Sanity is ours) is familiar territory to you. - Fluent in measurement. The sites you build aren’t only beautiful, but they’re well-measured too. You understand cookies, conversion events, analytics, and experimentation tooling, and you've worked with tools like Segment, Amplitude, or Customer.io http://Customer.io. - Focused on conversion and fueled by impact. We’re primarily a self-service SMB business and that means that Close.com http://Close.com isn’t just a brand experience. It’s a critical part of our acquisition funnel. This means that your work will have a direct effect on some of our most important business metrics - traffic, conversion, revenue. You’re the type who finds this motivating and is eager to partner with Marketing and Growth to move these numbers. - Energized by empowering non-technical team members. You’ve built systems that allow Marketers or Designers to self-serve and solve problems quickly and independently. You’re comfortable thinking through guardrails, automations, and tooling. - Already building with AI. You use AI to move faster and empower your team but are equally discerning about which moments require craftsmanship and taste. - Resourceful and collaborative. You’ll bridge two teams (Marketing and Engineering) and will need to easily work with folks across departments and levels. No tweak or task is too small (or too big) for you to tackle. YOU WILL - Build and continuously improve a world-class marketing website. We want close.com http://close.com to be responsive, highly interactive, and polished. The kind of site that brings the product to life through animation and interactivity. You'll ship new pages and continue raising the bar on core site functionality. - Make the site fast and findable. You’ll own performance, technical SEO, and how the site shows up to search engines and AI agents. - Manage the infrastructure behind the site. The platform (Sanity), components, integrations, and deployment are for you to maintain and evolve. - Own the technical integrity of site measurement and partner on experiments. Make sure tracking, attribution, conversion events, and funnel data are implemented correctly and reliably flow into our marketing and data systems. - Build tools that let Marketing self-serve...and help the site self-improve. You’ll build automations and flows to help the team ship pages, campaigns, and experiments, but you’ll also help the website be a partner in its own development - be it catching regressions or flagging performance problems and SEO issues. The website of the future is a surface for collaboration between you, the marketing team, and AI agents. - Stretch beyond the site and act as an engineering resource to marketing. The website is your focus, but there’s a lot of work to be done! You’ll be a resource for solving tricky problems, across the website and in other areas of marketing - sometimes that means hopping in yourself, or just knowing when to loop in the right person. BENEFITS - Compensation: Competitive pay plus an organization-wide goal-based bonus - Paid Time Off: ~5 weeks of PTO to start. Plus a 1-week all-company Winter Holiday Break and paid US holidays. You'll earn 2 extra days for every year you're with Close. - 80% Work Option: Work with your manager to choose between a standard 5-day week or a 4-day week at 80% pay - Parental Leave: Paid leave for primary and secondary caregivers - Sabbatical: A 1-month paid sabbatical every 5 years with the team - Healthcare (US residents): Best-in-class health coverage with the majority of employee and dependent premiums covered. Two medical plans, plus Dental, Vision, HSA, FSA, and company-paid Long-Term Disability. - 401k (US residents): We match your contributions up to 6%, vested immediately OUR VALUES Build a house you want to live in - Examine long-term thinking and action No BS - Practice transparency and honesty, especially when it’s hard Invest in each other - Build successful relationships with your coworkers and customers Discipline equals freedom - Keep your word to yourself and others Strive for greatness - Constantly challenge yourself and others LEARN MORE Listen to our CEO and Founder, Steli Efti, tell the story of Close’s journey in the $0-30m Blueprint https://www.close.com/blueprint.  Watch our culture video https://www.youtube.com/watch?v=maXKi1GN2MI from our 2023 team retreat in Milan. Every year our entire team gathers in person to build connection, foster cross-functional collaboration, and have fun. In 2027, we're headed to Dusseldorf, Germany! Explore our product. Check out a demo https://www.close.com/demo-site! OUR HIRING PROCESS We ask a few role-specific questions as part of our application process. These questions are designed to help us learn more about you from the start, so please answer each one thoughtfully. We see this as an opportunity to get to know you beyond your resume. We use AI tools daily at Close and expect candidates to do the same. In evaluating your application, we aim to get a sense for you - the way you think, how you communicate, the work you've done. Applications that read as fully AI-generated will not be considered. Regardless of fit, you can expect to hear back from our team with an update on the status of your candidacy. If you progress to the interview process, you'll receive a full outline of the role-specific steps in your first touchpoint with us. We do our best to make the hiring process clear and human.

Location: Remote - United States Innodata (Nasdaq: INOD) is a global data engineering company. We believe that data and Artificial Intelligence (AI) are inextricably linked. Our mission is to enable the responsible advancement of artificial intelligence by providing the data, evaluation frameworks, and human expertise required to build AI systems that can be trusted at scale. We provide a range of transferable solutions, platforms, and services for Generative AI / AI builders and adopters. In every relationship, we honor our 36+ year legacy delivering the highest quality data and outstanding outcomes for our customers. Scope of the Role: Video is where multimodal models are weakest and hardest to grade. Temporal reasoning, long-form understanding, grounding events in time, and holding audio, video, and text together do not fall out of image benchmarks — and the evaluations for them are still immature. Closing that gap is gated as much by how we design data and evaluation as by architecture. Innodata builds that data and those evaluations for the customers and frontier labs advancing video and multimodal models, and we are hiring a Research Scientist to own the science behind it. You will partner directly with the customers and frontier labs building video understanding, video-language, and video-generation models, as interested in the data behind them as in the models themselves. Video spans two model families judged in completely different ways: models that understand video — answering questions, localizing events, grounding language in time — where the question is whether the answer is correct; and models that generate it, where fidelity, temporal coherence, and physical plausibility matter and no automatic metric is settled. You own the evaluation science for both, and knowing when model-based scoring can stand in for a human versus when it can't. Your conclusions shape what our partners measure and collect next. What You’ll Own: You will define how Innodata designs, structures, and evaluates video data for video and multimodal models, and you will validate those choices experimentally. Concretely, you will: Translate the requirements of video and multimodal models — video understanding, temporal and event localization, action recognition, long-form video, video-language models, video generation, cross-modal reasoning, and multimodal retrieval and grounding — into concrete data specifications: modalities, annotation schemas, sampling, and evaluation criteria. Build evaluation methodology for video understanding — temporal grounding accuracy, long-context and long-horizon reasoning, and dynamic multi-turn, cross-modal, and retrieval-and-grounding evaluation — clear about when model-based scoring is trustworthy and when a human is needed. Build evaluation methodology for video generation — fidelity, temporal coherence, and physical plausibility, including generative video used as a world model — the regime where automatic metrics are weakest and human judgment matters most. Decide how existing and incoming video should be structured, enriched, and sampled to extract the most model value from it, including from messy, domain-specific footage. Run experiments that prove data decisions matter: fine-tune and evaluate models on Innodata data, with ablations tying specific data choices to measurable improvement. Design adversarial and stumping evaluations that surface where video and multimodal systems fail, and turn those failures into better data. Publish. Turn what you learn into benchmarks, methodology, and papers that advance the field and earn the trust of the customers and frontier labs we partner with. Work with annotation teams, subject-matter experts, and the synthetic-data pipeline to turn specifications into operational collection and labeling plans. You’ll Thrive in This Role If You Have: Roughly 5+ years of hands-on industry experience in video understanding or multimodal ML. We weight practical experience over formal credentials; a PhD with a compelling, current research agenda can offset the lower end. A Bachelor's degree in computer science, electrical engineering, or a related technical or quantitative field is required; an advanced degree (MS or PhD) in a relevant field is preferred. Trained and evaluated video or multimodal models yourself, with strong PyTorch fundamentals. Fluency in the formats and tooling video work runs on: ffmpeg and decord pipelines, temporal and COCO-style annotation, WebDataset, Parquet and Arrow, and HuggingFace datasets. Experience fine-tuning large video or vision-language models with the modern toolchain (HuggingFace transformers, PEFT, efficient inference), and with long-form video, streaming, temporal segmentation, or synthetic video generation. A way of thinking in datasets and benchmarks: you have built evaluation sets, calibrated difficulty, and argued about what makes video data good for a given objective. A track record the field recognizes: first-author publications or strong open-source contributions at venues such as CVPR, ICCV, ECCV, NeurIPS, or ICLR. The ability to work directly with the research scientists at the customers and frontier labs we partner with, and to explain data and modeling decisions clearly to both expert and non-expert audiences, backed by a rigorous, reproducible approach to experiments and documentation. Bonus: interest or hands-on experience in responsible-AI evaluation and red-teaming — safety and robustness testing for video and multimodal systems. The expected salary range for this position is $160,000 - $185,000 p/year, based on experience, skills, and qualifications. Please be aware of recruitment scams involving individuals or organizations falsely claiming to represent employers. Innodata will never ask for payment, banking details, or sensitive personal information during the application process. To learn more on how to recognize job scams, please visit the Federal Trade Commission’s guide at https://consumer.ftc.gov/articles/job-scams. If you believe you’ve been targeted by a recruitment scam, please report it to Innodata at verifyjoboffer@innodata.com and consider reporting it to the FTC at ReportFraud.ftc.gov .

Location: Remote - United States Innodata (Nasdaq: INOD) is a global data engineering company. We believe that data and Artificial Intelligence (AI) are inextricably linked. Our mission is to enable the responsible advancement of artificial intelligence by providing the data, evaluation frameworks, and human expertise required to build AI systems that can be trusted at scale. We provide a range of transferable solutions, platforms, and services for Generative AI / AI builders and adopters. In every relationship, we honor our 36+ year legacy delivering the highest quality data and outstanding outcomes for our customers. Scope of the Role: Where models actually differ now is robustness across accents, noise, and code-switching; speaker diarization; the naturalness of generated speech; and latency under streaming. Measuring those honestly, and building the data that trains for them, is gated as much by data and evaluation design as by architecture. Innodata builds that data and those evaluations for the customers and frontier labs advancing speech and audio models, and we are hiring a Research Scientist to own the science behind it. You will partner directly with the customers and frontier labs building ASR, text-to-speech, speech-to-speech and conversational voice, diarization, and audio-language models, as interested in the data behind them as in the models themselves. Your work is judgment: which conditions and languages a benchmark must cover to be honest, what a transcription convention should be for a given objective, and when an automated metric can be trusted versus when a human ear is required. You will also partner closely with our transcription and linguistics lead, whose standards directly shape what the models learn. What You’ll Own: You will define how Innodata designs, structures, and evaluates audio data for speech and audio models, and you will validate those choices experimentally. Concretely, you will: Translate the requirements of speech and audio models — ASR, text-to-speech and speech generation, speech-to-speech and conversational voice, speaker diarization and verification, audio-language models, and streaming systems — into concrete data specifications: modalities, transcription and annotation schemas, sampling, and evaluation criteria. Build evaluation methodology that goes past word error rate — semantic accuracy, robustness to noise and accent, code-switching, diarization error rate (DER), naturalness and intelligibility of generated speech, and streaming latency — and know when automated metrics hold and when they don't. Decide how existing and incoming audio should be structured, enriched, and sampled for coverage that fits the model objective — across languages, accents, and acoustic conditions (studio, real-world, telephonic), speaker demographics, emotional and paralinguistic range, scripted versus spontaneous speech, and single- versus multi-speaker settings, including low-resource and code-switched speech. Partner with the transcription and linguistics lead to turn model objectives into transcription specifications, and to quantify how transcription conventions and quality move ASR and speech-model results. Partner with the audio solutions and engineering team so the audio we collect is built for the model objective: you specify what good data and evaluation require, and they scope programs with customers and capture audio to spec. Run experiments that prove data decisions matter: fine-tune and evaluate models on Innodata data, with ablations tying specific data choices to measurable improvement. Design adversarial and stumping evaluations — noisy, accented, and adversarial audio — that surface where speech systems fail, and turn those failures into better data. Publish. Turn what you learn into benchmarks, methodology, and papers that advance the field and earn the trust of the customers and frontier labs we partner with. Work with annotation teams, subject-matter experts, and the synthetic- and augmented-audio pipeline to turn specifications into operational plans. You’ll Thrive in This Role If You Have: Roughly 5+ years of hands-on industry experience in speech or audio ML. We weight practical experience over formal credentials; a PhD with a compelling, current research agenda can offset the lower end. A Bachelor's degree in computer science, electrical engineering, or a related technical or quantitative field is required; an advanced degree (MS or PhD) in a relevant field is preferred. Trained and evaluated speech or audio models yourself — ASR, TTS, speech-to-speech, speaker, or audio-language models — with strong PyTorch fundamentals. Fluency in the toolchains and metrics speech work runs on: ESPnet, NeMo, SpeechBrain, or Kaldi, HuggingFace, forced alignment, and WER/CER and the metrics that go beyond them. Hands-on experience with multilingual, accented, dialectal, low-resource, or code-switched speech, and with synthetic or augmented audio (TTS pipelines, noise and room-response simulation). A way of thinking in datasets: you have built evaluation sets, reasoned about coverage across conditions, and argued about what makes speech data good for a given objective. A track record the field recognizes: first-author publications or strong open-source contributions at venues such as Interspeech, ICASSP, ASRU, SLT, or NeurIPS. The ability to work directly with the research scientists at the customers and frontier labs we partner with, and to explain data and modeling decisions clearly to both expert and non-expert audiences, backed by a rigorous, reproducible approach to experiments and documentation. Bonus: interest or hands-on experience in responsible-AI evaluation and red-teaming — for example spoofing and voice-cloning robustness, or bias across accents and languages. The expected salary range for this position is $160,000 - $185,000 p/year, based on experience, skills, and qualifications. Please be aware of recruitment scams involving individuals or organizations falsely claiming to represent employers. Innodata will never ask for payment, banking details, or sensitive personal information during the application process. To learn more on how to recognize job scams, please visit the Federal Trade Commission’s guide at https://consumer.ftc.gov/articles/job-scams. If you believe you’ve been targeted by a recruitment scam, please report it to Innodata at verifyjoboffer@innodata.com and consider reporting it to the FTC at ReportFraud.ftc.gov .

Location: Remote - United States Innodata (Nasdaq: INOD) is a global data engineering company. We believe that data and Artificial Intelligence (AI) are inextricably linked. Our mission is to enable the responsible advancement of artificial intelligence by providing the data, evaluation frameworks, and human expertise required to build AI systems that can be trusted at scale. We provide a range of transferable solutions, platforms, and services for Generative AI / AI builders and adopters. In every relationship, we honor our 36+ year legacy delivering the highest quality data and outstanding outcomes for our customers. Scope of the Role: Progress in physical AI is gated as much by data design as by model architecture. The best manipulation and locomotion policies, vision-language-action models, and world models are only as good as the trajectories, demonstrations, and environments they learn from — so the highest-leverage decisions are made long before a model trains: what to capture, what to synthesize, how to represent an action, and how to know whether any of it will transfer to a real robot. Innodata is building the data and evaluation practice behind the next generation of robotics foundation models, in direct partnership with the customers and frontier labs defining that frontier. We are hiring a Research Scientist to own the science of that data. You will partner directly with the customers and frontier labs building robot foundation models, as interested in the data those models learn from as in the models themselves. Your leverage is scientific judgment about data: deciding what is worth capturing in the real world versus generating in simulation, how to weight a training mix across embodiments and sensors, and how to measure whether a policy will hold up outside the lab. Your conclusions will shape what our partners collect next. What You’ll Own: You will define how Innodata designs, structures, and evaluates data for robot foundation models, and you will validate those choices experimentally. Concretely, you will: Translate the requirements of robotics foundation models — vision-language-action models, world models, and manipulation and locomotion policies — into concrete data specifications: modalities, action representations (tokenization, chunking, diffusion and flow-matching action experts), sampling, annotation schemas, and evaluation criteria. Decide what is worth capturing in the real world versus generating in simulation, and curate and weight training mixes across heterogeneous robot datasets that span different embodiments, action spaces, and sensor setups. Guide collection across capture modalities — motion capture, egocentric, exocentric, teleoperation, and multi-sensor — and across synthetic pipelines, so that what we produce maps cleanly to model needs rather than accumulating for its own sake. Build evaluation and benchmarking methodology that predicts real-world transfer — coverage, discriminative power, reliability, and sim-to-real fidelity — including world-model evaluation (rollout quality and action-conditioned prediction) and the domain-randomization and system-identification choices that close the sim-to-real gap. Run the experiments that prove it: fine-tune and evaluate foundation models on Innodata data, with data-quality ablations and scaling studies that demonstrate specific data decisions produce measurable model improvement. Design adversarial and long-horizon evaluations that surface where policies and world models break, and turn those failure modes into better data. Publish. Turn what you learn into benchmarks, methodology, and papers that advance the field and earn the trust of the customers and frontier labs we partner with. Collaborate with the capture lab, annotation teams, and the synthetic-data pipeline to turn specifications into operational collection and labeling plans. You’ll Thrive in This Role If You Have: Roughly 4+ years of hands-on industry experience in robot learning or robotics ML. We weight practical experience over formal credentials; because the field is young, a PhD with a compelling, current research agenda can offset the lower end, and we will also consider very senior candidates with the right background. A Bachelor's degree in computer science, electrical engineering, robotics, or a related technical field is required; an advanced degree (MS or PhD) in a relevant field is preferred. Trained and evaluated robot policies yourself — manipulation or locomotion via imitation learning or reinforcement learning — with strong PyTorch fundamentals. You build and measure models, you don't just call them. A way of thinking in datasets: you have curated, filtered, and weighted robot data across embodiments and sensors, and you have real opinions about what makes it good for a given objective. Fluency in robotics data formats and standards — the LeRobot dataset format, RLDS, and Open X-Embodiment — along with common motion and sensor formats. Hands-on experience with simulation and synthetic data — NVIDIA Isaac Sim, Isaac Lab, and Omniverse, or MuJoCo and comparable engines — including domain randomization, system identification, and sim-to-real transfer. Experience with teleoperation or egocentric data collection, and comfort adapting VLM backbones for control and fine-tuning large VLA models with the modern toolchain (HuggingFace transformers, PEFT). A track record the field recognizes: first-author publications or strong open-source contributions at venues such as CoRL, ICRA, IROS, RSS, or NeurIPS. The ability to work directly with the research scientists at the customers and frontier labs we partner with, and to explain data and modeling decisions clearly to both expert and non-expert audiences, backed by a rigorous, reproducible approach to experiments and documentation. Bonus: interest or hands-on experience in responsible-AI evaluation and red-teaming for embodied systems, for example safety and robustness testing. The expected salary range for this position is $160,000 - $185,000 p/year, based on experience, skills, and qualifications. Please be aware of recruitment scams involving individuals or organizations falsely claiming to represent employers. Innodata will never ask for payment, banking details, or sensitive personal information during the application process. To learn more on how to recognize job scams, please visit the Federal Trade Commission’s guide at https://consumer.ftc.gov/articles/job-scams. If you believe you’ve been targeted by a recruitment scam, please report it to Innodata at verifyjoboffer@innodata.com and consider reporting it to the FTC at ReportFraud.ftc.gov .

Location: Remote - United States Innodata (Nasdaq: INOD) is a global data engineering company. We believe that data and Artificial Intelligence (AI) are inextricably linked. Our mission is to enable the responsible advancement of artificial intelligence by providing the data, evaluation frameworks, and human expertise required to build AI systems that can be trusted at scale. We provide a range of transferable solutions, platforms, and services for Generative AI / AI builders and adopters. In every relationship, we honor our 36+ year legacy delivering the highest quality data and outstanding outcomes for our customers. Scope of the Role: As speech and audio models get better, the human role gets harder, not easier — it moves from producing transcripts to defining what a correct one is, adjudicating the cases models still get wrong, and designing the human-in-the-loop workflows that keep improving them. Innodata runs high-volume segmentation and transcription workflows for the customers and frontier labs building these models, and we are hiring a principal-level linguist to own the linguistic standards and quality behind that work — today, and as the workflows evolve alongside the models over the next two years. This is the applied-expert counterpart to our Speech & Audio Research Scientist. You set the standards the models are trained and measured against, and you understand the big picture: how different transcription and segmentation methods change what a model learns, and how that ripples into the speech and content-understanding systems our partners are building. You know the research and you know the tools — from IPA and acoustic analysis to forced alignment and the ASR engines our partners benchmark against — but your leverage is linguistic judgment and standard-setting at scale, not building models yourself. What You’ll Own: You will own the linguistic foundation of Innodata's segmentation and transcription work across languages, domains, and use cases. Concretely, you will: Define transcription and segmentation standards, style guides, and annotation conventions — verbatim and clean/intelligent verbatim, IPA and phonetic transcription, timestamping and boundary segmentation, speaker labeling and diarization labels, disfluencies and non-speech events, code-switching, and orthographic conventions. Establish and run the quality frameworks behind that work: rubrics, error taxonomies, adjudication processes, inter-annotator agreement, and human QA at scale. Own the quality lifecycle for transcription and segmentation deliverables end to end — pre-processing and normalization of incoming data, quality checks at the point of acceptance, post-processing and pre-delivery validation against spec, and report creation and packaging for delivery — partnering with delivery operations on execution at scale. Design the human-in-the-loop workflows themselves — deciding where human review, correction, and adjudication add the most value as ASR quality rises, so our experts spend their time on what the models still can't do rather than on what they already can. Handle the linguistically hard cases models fail on — accented and dialectal speech, low-resource and multilingual audio, overlapping speech, domain jargon (medical, legal, technical), and noisy acoustic conditions. Partner with the Speech & Audio Research Scientist to turn model objectives into transcription and segmentation specifications, and to work out how different transcription methods — verbatim versus clean, phonetic versus orthographic, and how audio is segmented and labeled — affect the training and evaluation of ASR, TTS, and speech and content-understanding models. Train, calibrate, and mentor expert transcribers and reviewers, and build the onboarding and calibration that keep quality consistent as the work scales. Represent Innodata's transcription and segmentation approach to the customers and frontier labs we partner with, and contribute to the methodology and best-practice documentation that make our work legible to their teams. You’ll Thrive in This Role If You Have: Substantial industry experience (typically 8+ years) in transcription, segmentation, and speech-data quality — enough that you have authored standards, not only followed them. This is a principal-level role, and we weight practical depth heavily. A Bachelor's degree in linguistics, phonetics, or computational linguistics, or a closely related field, is required — with a strong foundation in phonetics, phonology, and sociolinguistics so that IPA, prosody, disfluency, dialect, and register are native concepts. An advanced degree is preferred. A big-picture grasp of how transcription and segmentation choices flow downstream into modeling — how different methods change what speech and content-understanding models learn, and therefore which method fits which modeling objective. You can explain to a model builder why a transcription decision matters. Fluency in phonetic transcription and IPA, plus hands-on experience with acoustic and phonetic analysis of speech — spectrograms, formants, pitch and prosody, and segment boundaries — applied to real, messy speech data at scale (for example in Praat). Deep experience with audio segmentation and its conventions — utterance and turn boundaries, timestamping, and speaker and diarization labeling — across real-world audio. Hands-on fluency with the modern speech stack: Whisper and the commercial ASR engines your partners benchmark against (such as AssemblyAI, Deepgram, Rev, and Speechmatics), forced alignment (for example the Montreal Forced Aligner), and annotation tools such as ELAN. Comfort scripting for speech-data work — Python for batch processing, QA, and metrics such as inter-annotator agreement and WER, plus regular expressions and Praat scripting — enough to work fluently with data and pipelines without needing an engineer for every task. Practical data-management skills across the delivery lifecycle — pre-processing, acceptance-stage quality checks, post-processing, pre-delivery validation, and report creation and packaging — so deliverables leave the door correct, consistent, and well documented. Multilingual capability and hands-on experience with accented, dialectal, and code-switched speech; low-resource languages a strong plus. A point of view on how human-in-the-loop workflows should evolve as models improve — where humans stay in the loop, where they move up to adjudication and standard-setting, and how to measure the difference. Strong written and verbal communication, comfortable working directly with research scientists and interfacing with the customers and frontier labs we partner with. Bonus: responsible-AI considerations for speech, such as bias across accents and dialects and privacy and consent in voice data. The expected salary range for this position is $160,000 - $185,000 p/year, based on experience, skills, and qualifications. Please be aware of recruitment scams involving individuals or organizations falsely claiming to represent employers. Innodata will never ask for payment, banking details, or sensitive personal information during the application process. To learn more on how to recognize job scams, please visit the Federal Trade Commission’s guide at https://consumer.ftc.gov/articles/job-scams. If you believe you’ve been targeted by a recruitment scam, please report it to Innodata at verifyjoboffer@innodata.com and consider reporting it to the FTC at ReportFraud.ftc.gov .

Location: Halifax Workplace: Remote WHO ARE WE? Cohere is the leading security-first enterprise AI company. We build cutting-edge foundation AI models and end-to-end products that are designed to solve real-world business problems. We’re training and deploying frontier models for enterprises who are building AI systems. We believe that our work is instrumental to the widespread adoption of AI and we are looking for folks that want to be part of that. We obsess over what we build. Each one of us is responsible for contributing to increasing the capabilities of our models and the value they drive for our customers. Cohere is a team of researchers, engineers, designers, and more, who are all passionate about their craft. We are a global technology company headquartered in Toronto with key offices in London, New York City, San Francisco, Montreal, Paris, Berlin and Seoul. Join us! Why this role? In this role, you will have ownership of the full sales cycle - from identifying leads to closing deals with Global 2000, large enterprises within Eastern Canada. We’re looking for an approachable and compelling communicator who loves working with prospects to uncover their needs and feels comfortable developing tailored value propositions around how Cohere’s platform can help them achieve their business goals. You’ll lay the foundation for Cohere’s growth by owning your territory and collaborating with teammates across customer success, sales development, marketing, and solution architecture. You’ll be the voice of the field and help our product and engineering teams prioritize the Cohere roadmap with customer-centric care. It’s a highly self-directed role, so you should be someone who thrives in an unstructured and quickly evolving environment. And your opportunity for impact will be astronomical — Cohere has skies-the-limit potential, and you’ll help us reach it.   As an Account Executive you will: - Focus on net-new logo acquisition via outbound activity and relationship building with key stakeholders while also bringing a strong network of key decision-maker and influencer contacts to drive strategic partnerships and win sales opportunities - Work closely with customers and prospects as a consultative, trusted advisor who deeply understands their challenges and goals, their technology ecosystem, and will tailor solutions to drive measurable impact for their businesses - Work in close partnership with channel partners to find opportunities to scale outreach and customer satisfaction in your region - Collaborate with product and engineering teams as well as customer success on strategic motions to deliver solutions to large enterprise customers - Collaborate with Sales Development Representatives to drive top of funnel activity - Own the full sales cycle – from initial outreach through proof-of-concept, deal close, and deployment ; this is not a transactional sale that you can walk away from after the contract is signed but rather requires ongoing oversight of the project to ensure success You may be a good fit if you have: - 8-12+ years of previous B2B sales experience with Global 2000 and large enterprises negotiating and closing transformational multi-year (2-5 year) SaaS deals in the 7 figure range, and a track record of high performance and exceeding quota - Previous experience as a technical consultative salesperson, selling complex products, such as developer tools, API products, or AI / NLP solutions, are a plus. - Previous experience working with customers during the deployment phase of the engagement, aligning on how best to configure and customize the solution that supports success in production and builds trust to set up for expansion and growth - Previous experience working with channel partners such as cloud hyperscalers and system integrators to drive sales cycles and hit shared revenue goals - High tolerance for ambiguity - as an early sales hire, you’ll have to be a self-starter, doer and a strategist who is capable of wearing many hats and doing what it takes to figure out a path to success - Curiosity - you want to go deep on NLP and become an expert on our technology while considering how to fit into a large organization’s technology landscape - Fantastic communication skills - you are a great listener, have a knack for understanding what matters most to others, build strong relationships, can speak to the c-suite, and feel comfortable speaking to both technical and non-technical audiences Additional Requirements: - Deep understanding of the enterprise landscape, including key trends, challenges, and opportunities - Ability to articulate the value of AI and NLP solutions in the context of Fortune 2000 operations. FULL-TIME EMPLOYEES AT COHERE ENJOY THESE PERKS: - A weekly lunch stipend of $75/£75 or equivalent in your local currency for lunch. - Full health and dental benefits, including a separate budget for mental health. - RRSP matching, 401K, Pension Scheme. - 100% Parental Leave top-up for up to 6 months, for either parent. - Annual enrichment benefits: Arts & culture, fitness/wellness, quality time, and a workspace improvement credit. Education & learning stipend for conferences, courses, and coaching. - 6 weeks of paid vacation (30 working days!) - Budget for traveling to other offices if you are remote, plus an annual company offsite. HOW AND WHERE WE WORK: - Cohere is remote-friendly, but we also have offices in Toronto, London, New York City, San Francisco, Montreal, Paris, Berlin and Seoul with more opening soon. - For those in the office: a daily lunch program, plenty of snacks, and regular community and social events. - For those not near an office: a co-working benefit so you can work alongside others in your city. - Everyone receives a $500 home office stipend to set up your workspace properly. If any of the above doesn’t line up exactly with your experience, we still encourage you to apply. We strive to create an inclusive work environment for all; we welcome applicants from all backgrounds and are committed to providing equal opportunities. Should you require any accommodations during the recruitment process, please submit an Accommodations Request Form https://docs.google.com/forms/d/12a6IrLdF3kI2nonKSr4tiFuz18rLQbaeYV-JM9L4o9Q/edit, and we will work together to meet your needs. We may use AI-enabled tools to screen and assess applicants against the criteria for this position. This helps our recruiters identify potentially qualified candidates, but it doesn't limit the applications our recruiters may review or consider. Beware of Scams: Cohere will never ask for payment or third-party services (e.g., CV writing) as part of our hiring process. All legitimate roles are listed on the Cohere careers page and LinkedIn only, with all communications from Cohere employees coming from an @cohere.com or @cw.cohere email alias. If jobs are viewed on other sites then please verify these through our official careers https://cohere.com/careers page.

Задачи: — исследовать новые инструменты и платформы; — искать причины технических проблем; — тестировать growth-гипотезы; — создавать автоматизации и внутренние инструменты; — работать с API, AI, no-code, low-code; — собирать MVP и прототипы; — иногда выполнять найденный процесс вручную. Требования: — умение самостоятельно искать информацию; — настойчивость после неудачных гипотез; — интерес к устройству процессов; — готовность работать с незнакомыми технологиями; — способность самостоятельно собирать решения из разных инструментов. Плюсом будут знания и опыт с n8n, Make, API, GitHub, Linux, Docker, SQL, Python, JS, scraping, browser automation, AI tools, Telegram bots и собственными проектами. Опыт и образование вторичны. Наличие собственного проекта, бота или автоматизации приветствуется. Условия: — удалённая работа; — зарплата $1,200 первый месяц, с последующим ростом до $2,400–3,500 в месяц в зависимости от результатов и ответственности; — хороший уровень английского языка; — рабочий день около 8 часов, 5–6 дней в неделю, гибкий график; — процесс отбора: анкета, голосовой звонок, практическое задание, финальное интервью.

Задачи: — исследовать новые инструменты и платформы; — искать причины технических проблем; — тестировать growth-гипотезы; — создавать автоматизации и внутренние инструменты; — работать с API, AI, no-code, low-code; — собирать MVP и прототипы; — иногда выполнять найденный процесс вручную. Требования: — умение самостоятельно искать информацию; — настойчивость после неудачных гипотез; — интерес к устройству процессов; — готовность работать с незнакомыми технологиями; — способность самостоятельно собирать решения из разных инструментов. Плюсом будут знания и опыт с n8n, Make, API, GitHub, Linux, Docker, SQL, Python, JS, scraping, browser automation, AI tools, Telegram bots и собственными проектами. Опыт и образование вторичны. Наличие собственного проекта, бота или автоматизации приветствуется. Условия: — удалённая работа; — зарплата $1,200 первый месяц, с последующим ростом до $2,400–3,500 в месяц в зависимости от результатов и ответственности; — хороший уровень английского языка; — рабочий день около 8 часов, 5–6 дней в неделю, гибкий график; — процесс отбора: анкета, голосовой звонок, практическое задание, финальное интервью.

Задачи: — исследовать новые инструменты и платформы; — искать причины технических проблем; — тестировать growth-гипотезы; — создавать автоматизации и внутренние инструменты; — работать с API, AI, no-code, low-code; — собирать MVP и прототипы; — иногда выполнять найденный процесс вручную. Требования: — умение самостоятельно искать информацию; — настойчивость после неудачных гипотез; — интерес к устройству процессов; — готовность работать с незнакомыми технологиями; — способность самостоятельно собирать решения из разных инструментов. Плюсом будут знания и опыт с n8n, Make, API, GitHub, Linux, Docker, SQL, Python, JS, scraping, browser automation, AI tools, Telegram bots и собственными проектами. Опыт и образование вторичны. Наличие собственного проекта, бота или автоматизации приветствуется. Условия: — удалённая работа; — зарплата $1,200 первый месяц, с последующим ростом до $2,400–3,500 в месяц в зависимости от результатов и ответственности; — хороший уровень английского языка; — рабочий день около 8 часов, 5–6 дней в неделю, гибкий график; — процесс отбора: анкета, голосовой звонок, практическое задание, финальное интервью.