Вакансии

Location: United States; Colombia; Sacramento; Los Angeles; San Francisco; Canada; Costa Rica; Mexico Workplace: Remote Location: San Francisco or Singapore (on-site), or remote (Singapore or US Pacific time zones) Start Date: ASAP Languages: English Team: Engineering ABOUT THE ROLE Our client is a fast-growing AI company building reliable, fair and open infrastructure for AI training data. Their marketplace helps the people who create data get value for it, and gives AI labs trustworthy data to train on. It is already used by startups, Fortune 500 companies and frontier labs, and the company is backed by leading venture investors, including Y Combinator. They are scaling quickly to meet strong demand. We are looking for a Research Engineer, Synthetic Data to build the synthetic data pipeline. You will turn domain-specific workflows into synthetic training tasks that are realistic enough to teach useful behaviour, structured enough to generate at scale, and difficult enough to expand model capabilities. This is a hands-on research engineering seat, not a commercial or privacy-systems role. You will design how synthetic tasks are created, mutated and validated, and you will analyse what those tasks actually teach models and agents. This is a full-time role, starting ASAP. Offices are in San Francisco and Singapore; remote candidates are welcome if their working hours can overlap about 70–80% with either San Francisco (US Pacific) or Singapore business hours. The exact daily schedule will be agreed during hiring. WHAT YOU'LL DO - Work with subject-matter experts to create synthetic tasks for training AI agents across a range of professional and technical domains. - Design synthetic task-generation methods that produce diverse, realistic and learnable tasks. - Build systems and tooling to mutate, validate and improve synthetic tasks. - Analyse model and agent performance on synthetic tasks to understand what the tasks are teaching and where they fail. - Develop metrics to quantify and understand synthetic task diversity, realism, learnability and related qualities. - Take synthetic data pipelines from early research through to reliable systems, including in areas where the roadmap is not fully prescribed. WHAT WE'RE LOOKING FOR - Proficiency in Python, Docker and Linux environments. - Experience with synthetic data research methods — please be ready to describe this in your application. - A strong understanding of what “good synthetic data” means, and of its limitations. - Experience building synthetic data pipelines end to end, including where there was no fully prescribed roadmap. - Experience working on environments, evals or benchmarks. - Clear written and spoken English, and comfort collaborating remotely across time zones. - Strong candidates may also be detail-oriented enough to catch subtle inconsistencies or edge cases in synthetic data; able to reason from first principles about task design, scoring and failure modes; and comfortable in unstructured problem spaces or early-stage environments. - Technical aptitude and learning potential matter more than years of experience. Motivated candidates are encouraged to apply even if they don't meet every point above. WHY THIS ROLE MATTERS FOR YOU - Build the synthetic data pipeline: You will design the tasks and systems that generate training data at scale, not only run experiments on someone else's pipeline. - Research that has to work in production: Turn messy, domain-specific workflows into tasks that are realistic, measurable and usable for training agents. - See what the data actually teaches: Analyse model and agent performance, and improve the tasks when they fail. - High ownership, unstructured problems: You will build without a fully prescribed roadmap, from method design through tooling and metrics. - San Francisco, Singapore, or remote: Work on-site in San Francisco or Singapore, or remotely, with hours that overlap about 70–80% with either US Pacific or Singapore business hours. The exact daily schedule will be agreed during hiring. Pragmatike is an Equal Opportunity Employer and is committed to providing equal employment opportunities to all applicants without discrimination. We recruit on behalf of our clients and prohibit discrimination and harassment based on race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state, or local laws. This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation, and training. We are committed to a fair and inclusive hiring process. We process your personal data solely for recruitment purposes, in accordance with applicable privacy laws, and maintain reasonable safeguards to protect your information. Your data may be shared with our client(s) for hiring consideration, but will not be disclosed to third parties outside of the recruitment process.

Location: Singapore; Philippines; Albania; Ukraine; Indonesia; Australia; Serbia; Thailand; Khazakstan; Armenia; Türkiye; Romania Workplace: Remote Location: San Francisco or Singapore (on-site), or remote (Singapore or US Pacific time zones) Start Date: ASAP Languages: English Team: Engineering ABOUT THE ROLE Our client is a fast-growing AI company building reliable, fair and open infrastructure for AI training data. Their marketplace helps the people who create data get value for it, and gives AI labs trustworthy data to train on. It is already used by startups, Fortune 500 companies and frontier labs, and the company is backed by leading venture investors, including Y Combinator. They are scaling quickly to meet strong demand. We are looking for a Research Engineer, Privacy and Anonymization to build the systems that make sensitive, real-world data safe and still useful for AI training. You will develop methods to detect and remove personally identifiable information, secrets and other sensitive content from raw data before it enters processing and synthetic data pipelines — protecting privacy without destroying the structure and signal that make the data valuable for training agents. This is a privacy and data-protection engineering seat, not a synthetic-task design role. You will own the pipeline that stands in front of downstream processing, training, evaluation and synthetic generation. This is a full-time role, starting ASAP. Offices are in San Francisco and Singapore; remote candidates are welcome if their working hours can overlap about 70–80% with either San Francisco (US Pacific) or Singapore business hours. The exact daily schedule will be agreed during hiring. WHAT YOU'LL DO - Build systems to detect PII, quasi-identifiers, credentials and other sensitive information, and design transformations based on the data type and the downstream use case. - Develop and benchmark detection approaches that combine rules, statistical models, classifiers and LLM-based methods. - Build production pipelines that anonymize raw data before it enters downstream processing, training, evaluation or synthetic data generation. - Create evaluation frameworks that measure privacy risk and retained data utility, including recall-weighted metrics, leakage tests and adversarial re-identification attempts. - Design systems that stay robust to new data sources, schema drift, unusual formats and sensitive information embedded in unexpected fields. - Work with engineering, research, operations and customers to translate privacy requirements into practical technical policies and safeguards. WHAT WE'RE LOOKING FOR - Strong proficiency in Python, and experience building reliable production data or machine-learning systems. - Experience with information extraction, named-entity recognition, classification, or related methods for detecting rare or sensitive content. - Strong experimental instincts, and the ability to compare approaches across recall, precision, latency, cost and downstream data utility. - An understanding of the difference between redaction, masking, pseudonymization, anonymization and synthetic data — and when each is appropriate. - High attention to detail, and the ability to reason about subtle leakage paths, edge cases and adversarial failure modes. - Experience building data-processing pipelines end to end, including where there was no fully prescribed roadmap. - Clear written and spoken English, and comfort collaborating across teams and time zones. - Strong candidates may also have hands-on experience with privacy-enhancing technologies (such as differential privacy, k-anonymity, secure aggregation or format-preserving encryption); have worked with sensitive data in healthcare, finance or security; have built low-latency or high-throughput ML inference or data-processing systems; or have taken work from early research through to production. - Technical aptitude and learning potential matter more than years of experience. Motivated candidates are encouraged to apply even if they don't meet every point above. WHY THIS ROLE MATTERS FOR YOU - Make real-world data safe to train on: You will build the systems that let sensitive data be used for AI training without exposing people or secrets. - Privacy without wrecking utility: The job is not only to redact, but to keep the structure and signal that downstream training and synthetic pipelines need. - From detection to production: You will own the full path: detection methods, transformations, evaluation, and the pipeline that sits in front of training and generation. - Hard, high-stakes edge cases: Work on leakage paths, schema drift and adversarial failure modes, not only clean benchmark datasets. - San Francisco, Singapore, or remote: Work on-site in San Francisco or Singapore, or remotely, with hours that overlap about 70–80% with either US Pacific or Singapore business hours. The exact daily schedule will be agreed during hiring. Pragmatike is an Equal Opportunity Employer and is committed to providing equal employment opportunities to all applicants without discrimination. We recruit on behalf of our clients and prohibit discrimination and harassment based on race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state, or local laws. This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation, and training. We are committed to a fair and inclusive hiring process. We process your personal data solely for recruitment purposes, in accordance with applicable privacy laws, and maintain reasonable safeguards to protect your information. Your data may be shared with our client(s) for hiring consideration, but will not be disclosed to third parties outside of the recruitment process.

Location: United States; Colombia; Sacramento; Los Angeles; San Francisco; Canada; Costa Rica; Mexico Workplace: Remote Location: San Francisco or Singapore (on-site), or remote (Singapore or US Pacific time zones) Start Date: ASAP Languages: English Team: Engineering ABOUT THE ROLE Our client is a fast-growing AI company building reliable, fair and open infrastructure for AI training data. Their marketplace helps the people who create data get value for it, and gives AI labs trustworthy data to train on. It is already used by startups, Fortune 500 companies and frontier labs, and the company is backed by leading venture investors, including Y Combinator. They are scaling quickly to meet strong demand. We are looking for a Research Engineer, Privacy and Anonymization to build the systems that make sensitive, real-world data safe and still useful for AI training. You will develop methods to detect and remove personally identifiable information, secrets and other sensitive content from raw data before it enters processing and synthetic data pipelines — protecting privacy without destroying the structure and signal that make the data valuable for training agents. This is a privacy and data-protection engineering seat, not a synthetic-task design role. You will own the pipeline that stands in front of downstream processing, training, evaluation and synthetic generation. This is a full-time role, starting ASAP. Offices are in San Francisco and Singapore; remote candidates are welcome if their working hours can overlap about 70–80% with either San Francisco (US Pacific) or Singapore business hours. The exact daily schedule will be agreed during hiring. WHAT YOU'LL DO - Build systems to detect PII, quasi-identifiers, credentials and other sensitive information, and design transformations based on the data type and the downstream use case. - Develop and benchmark detection approaches that combine rules, statistical models, classifiers and LLM-based methods. - Build production pipelines that anonymize raw data before it enters downstream processing, training, evaluation or synthetic data generation. - Create evaluation frameworks that measure privacy risk and retained data utility, including recall-weighted metrics, leakage tests and adversarial re-identification attempts. - Design systems that stay robust to new data sources, schema drift, unusual formats and sensitive information embedded in unexpected fields. - Work with engineering, research, operations and customers to translate privacy requirements into practical technical policies and safeguards. WHAT WE'RE LOOKING FOR - Strong proficiency in Python, and experience building reliable production data or machine-learning systems. - Experience with information extraction, named-entity recognition, classification, or related methods for detecting rare or sensitive content. - Strong experimental instincts, and the ability to compare approaches across recall, precision, latency, cost and downstream data utility. - An understanding of the difference between redaction, masking, pseudonymization, anonymization and synthetic data — and when each is appropriate. - High attention to detail, and the ability to reason about subtle leakage paths, edge cases and adversarial failure modes. - Experience building data-processing pipelines end to end, including where there was no fully prescribed roadmap. - Clear written and spoken English, and comfort collaborating across teams and time zones. - Strong candidates may also have hands-on experience with privacy-enhancing technologies (such as differential privacy, k-anonymity, secure aggregation or format-preserving encryption); have worked with sensitive data in healthcare, finance or security; have built low-latency or high-throughput ML inference or data-processing systems; or have taken work from early research through to production. - Technical aptitude and learning potential matter more than years of experience. Motivated candidates are encouraged to apply even if they don't meet every point above. WHY THIS ROLE MATTERS FOR YOU - Make real-world data safe to train on: You will build the systems that let sensitive data be used for AI training without exposing people or secrets. - Privacy without wrecking utility: The job is not only to redact, but to keep the structure and signal that downstream training and synthetic pipelines need. - From detection to production: You will own the full path: detection methods, transformations, evaluation, and the pipeline that sits in front of training and generation. - Hard, high-stakes edge cases: Work on leakage paths, schema drift and adversarial failure modes, not only clean benchmark datasets. - San Francisco, Singapore, or remote: Work on-site in San Francisco or Singapore, or remotely, with hours that overlap about 70–80% with either US Pacific or Singapore business hours. The exact daily schedule will be agreed during hiring. Pragmatike is an Equal Opportunity Employer and is committed to providing equal employment opportunities to all applicants without discrimination. We recruit on behalf of our clients and prohibit discrimination and harassment based on race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state, or local laws. This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation, and training. We are committed to a fair and inclusive hiring process. We process your personal data solely for recruitment purposes, in accordance with applicable privacy laws, and maintain reasonable safeguards to protect your information. Your data may be shared with our client(s) for hiring consideration, but will not be disclosed to third parties outside of the recruitment process.

Location: Paris ABOUT MISTRAL Mistral provides full-stack AI solutions: from frontier models to developer tools, applications, and compute. We partner with enterprises tackling the hardest problems across high-stakes industries like finance, manufacturing, defense, healthcare, and the public sector, co-creating customized AI systems that they can run on their terms. We are a dynamic, collaborative team passionate about AI and its potential to transform society. Our diverse workforce thrives in competitive environments and is committed to driving innovation. Our teams are distributed between Europe, North America, Asia and the Middle East. We are creative, low-ego and team-spirited. ABOUT THE ROLE Between a conversation and a model sit the formats and tokenizers that every part of our stack relies on. You will help own them, and the tooling around them, so that training, evaluation and production all speak exactly the same language. You will work across the entire stack, spanning pretraining, post-training and deployment, and your decisions will reach every model we ship. WHAT YOU WILL DO - Design, build and maintain core libraries for tokenization and model interfaces. - Evolve our formats and tokenizers as models and use cases change. - Turn research needs into robust, shared tooling. WHAT WE'RE LOOKING FOR - Strong software engineering background, with excellent Python. - Excellent understanding of tokenizers: BPE and other tokenization algorithms, special tokens, chat templates, and tokenizer training; ideally hands-on experience with libraries such as tiktoken or Hugging Face tokenizers. - Experience building production-grade libraries used by many people. - A taste for correctness and attention to detail. - Good knowledge of how LLMs are trained and served. - Clear communication, low ego, and a collaborative mindset. - Nice to have - Open-source maintenance experience. - Experience with inference engines. LOCATION AND REMOTE This role is primarily based at one of our European offices (Paris and London). We will prioritize candidates who either reside there or are open to relocating. We strongly believe in the value of in-person collaboration to foster strong relationships and seamless communication within our team. In certain specific situations, we will also consider remote candidates based in one of the countries listed in this job posting — currently France & UK. In that case, we ask all new hires to visit our local office for the first week of their onboarding (accommodation and travelling covered), then at least 4 days per month WHAT WE OFFER We offer a comprehensive benefits package designed to support your well-being, growth, and work-life balance. Benefits vary by country and may include healthcare coverage, parental leave, retirement plans, relocation support, wellness programs, meal and transportation allowances, and other location-specific perks. For the most up-to-date details on benefits available in your location, please refer to our Benefits page https://app.notion.com/p/mistralai/Benefits-at-Mistral-36e6ba59a7fe836b93dd01737fcc27ef?source=copy_link. PRIVACY POLICY Your privacy matters to us. You can learn more about how we handle your personal data in our Applicant Privacy Policy https://legal.mistral.ai/terms/applicant-privacy-policy.

Responsibilities: — Begin playing GTA VI at midnight on November 19 and continue until the story is completed. — Stream the entire gameplay journey on Twitch or YouTube with camera on. — Investigate the in-game economy of Leonida and analyze its impact on the real economy during the game's launch. — Provide an official State of the Leonida Economy address based on your study. — Analyze real-world fallout such as who called in sick and how services like DoorDash managed during launch week. — Optimize and min-max gameplay strategies to find the best money-making methods. — Produce weekly research reports on the Leonida economy. Requirements: — Established gamer and streamer with your own channel. — At least 18 years old with 10+ years of gaming experience and 3+ years of streaming. — Own streaming setup including equipment, internet, and software. — Ability to understand and optimize game systems and challenge common guides. Benefits: — Fixed project fee between $100,000 and $120,000. — Additional $15,000 project completion fee. Additional Information: — Remote work format. — Kalshi is an equal opportunity employer committed to inclusion and diversity. — The role involves streaming and research related to the launch and economy of GTA VI. — The position is a fixed-term project starting November 19 at midnight. Kalshi is a company that operates prediction markets, allowing trading on outcomes of various events, aiming to build the largest financial market on the planet.

Aleph Alpha is looking for an AI Researcher in Pre training to design and run foundation-model experiments, improve training recipes, and support large-scale training runs.

Location: Tel Aviv-Yafo, Israel 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. We are looking for a Clinical Research Associate (CRA) to join our A-team (hybrid*/remote). 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. In this role your key tasks will include: Provide functional assistance to the project team members with administrative, logistical and practical issues, including the tracking, collection, distribution and filing study documentation. Track and supervise collection of ongoing study data for purpose of regular project status reporting as required. Assist the Clinical Operations Manager in conducting feasibility assessment for potential studies. Govern minimum quality standards for trial monitoring activities, with respect to documentation, punctuality of reporting, compliance with objectives, and co-operation with other project team members. Ensure adequate tracking is in place for all activities and reports formatted as required for submission within agreed timelines. Monitor activities at clinical study sites to assure adherence to GCP, ICH, SOPs, and study protocols. Collect and review regulatory documents as required. Prepare site visit and telephone reports. Responsible for multiple projects and must work both independently and in a team environment. May participate in the study development and start-up process including reviewing protocols, drafting of the Monitoring Plan, designing and/or reviewing CRFs, preparing Informed Consent forms, developing study documents, organizing and presenting at investigator meetings, working with management on monitoring strategy, and/or developing project-specific CRA training. Resolve site issues and determine status for IP shipment. Work with Project Manager (PM), Clinical Team Leader (CTL) and/or Lead Clinical Research Associate (LCRA), regulatory team members or Sponsor to secure authorization of regulatory documents and contracts. May translate, coordinate translations or review completed translations of critical documents. Participate in feasibility and/or site identification activities. Assist the Project Team with the day-to-day management of clinical studies as required. Monitoring Visit Report (MVR) review, management, resolution and escalation as required. Train, mentor and/or supervise junior staff. May be assigned as LCRA to a regional or global study. May be assigned as a reviewer of essential documents (GLP) as a 2nd line or Independent Reviewer (IR). Conducts project co-monitoring, assessment visits and team training. Site contact for protocol clarifications and subject enrolment if CRA unavailable. Participate in the development of study newsletters communication as required. Assist with the development of project-specific training materials for team. Liaise with Business Development and make presentations to potential clients as required. May be required to manage the preparation of local clinical trial applications (for regulatory submissions for new drugs, biologics, or devices). Requirements To be successful you will possess: At least 2 years experience as CRA (on-site monitoring). Skills to mentor and train other CRAs in a positive and effective manner. In-depth knowledge of clinical trials and the critical elements for success in clinical trials. Ability to travel for up to 8 days on site per month, depending on the project needs. Strong therapeutic background. Has shown ability to successfully manage people/project issues. Mature management skills demonstrated by calm and thorough review of situations. Proactively identifies and addresses problems. Seeks to understand all contributing factors. Proposes, implements, and evaluates appropriate resolutions. Demonstrates the ability to define and meet project requirements. Can demonstrate flexibility for improvement and creating solutions. Proven organizational abilities, and excellent written and oral communication and presentation skills. Excellent team player with team building skills. Strong customer focus. A thorough knowledge of regulatory submission and reporting requirements and guidelines. Excellent understanding of the drug development process. 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) Participation in our enriching Buddy Program as a new or existing employee Internal growth opportunities and career progression Financially rewarding internal employee referral program 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 in-office work policy encourages a dynamic work environment, prescribing 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.”

Location: Tel Aviv Office Workplace: OnSite Chainalysis is building the data, intelligence, and technology that enable organizations to investigate and prevent crypto-related crime. Our teams combine deep blockchain expertise with advanced technology to help make the digital asset ecosystem safer and more trusted. This team is a small, highly technical group focused on exploring emerging threats and technologies, turning insights into practical solutions, and bringing new ideas from early concepts through production. We are looking for a talented Researcher who is excited by new and emerging technologies and can help move the organization forward by developing new capabilities, from design and proof of concept through production. The position is office-based in Tel Aviv. In this job, you'll: - Design, implement, and improve threat detection models and frameworks - Be an expert on bleeding-edge web3 technologies - Write excellent code and be an integral part of engineering efforts - Solve sophisticated technical problems and "make hard things work" - Work in a small, agile, fast-moving team that is best in class We’re looking for candidates who have: - Proven experience in research in highly technical environments / low level R&D - Ability to excel in a dynamic environment with rapidly changing requirements - Previous experience in software development or R&D style teams Nice to have experience: - Blockchain prior experience / interest - Experience with Python, Go, or Rust - Experience with classic data science / machine learning - Experience with building agentic LLM-powered products Technologies we use: - Python - Go - Rust - Agentic workflows AI at Chainalysis AI is not a feature at Chainalysis - it is a new way of working. One that turns instructions into work done, and helps us move faster than the threats we're built to counter, and we expect our employees to take ownership of the output and ensure quality. As the world's most trusted blockchain analytics platform, Chainalysis sits at a rare intersection of proprietary data, regulatory relationships and crypto expertise that makes it uniquely placed to shape and lead the next era of AI-driven intelligence - and we expect everyone here, regardless of role, to be an active part of it. AI fluency is tied directly to how we measure performance and how we plan to win. There is no substitute for your own curiosity. We provide the tools, workflows, and space to experiment - but the expectation is that you develop these capabilities yourself, bring ideas, and collaborate across teams to reinvent the way work gets done. We are not using AI to do less. We are using it to do what was never possible before. About Chainalysis Chainalysis is the blockchain data platform, making it easy to connect the movement of digital assets to real-world services. Powered by deep blockchain data and AI, organizations can investigate illicit activity, manage risk exposure, and develop innovative market solutions built on the industry's most trusted blockchain intelligence. Our mission is to build trust in blockchains, blending safety and security with an unwavering commitment to growth and innovation. You belong here.  At Chainalysis, we believe that diversity of experience and thought makes us stronger. With both customers and employees around the world, we are committed to ensuring our team reflects the unique communities around us. We’re ensuring we keep learning by committing to continually revisit and reevaluate our diversity culture. We encourage applicants across any race, ethnicity, gender/gender expression, age, spirituality, ability, experience and more. If you need any accommodations to make our interview process more accessible to you due to a disability, don't hesitate to let us know. You can learn more here https://go.chainalysis.com/rs/503-FAP-074/images/Interview%20Accommodations%20Request.pdf. We can’t wait to meet you.

Location: Москва Данные — главный рычаг качества фронтирной модели. Станьте тем, кто его держит ------------------------------------------------------------------------------ GigaChat обучает одна из крупнейших ML-команд страны. Мы знаем то же, что знают OpenAI и Anthropic: качество следующей версии модели определяют претрейн-данные. Поэтому мы ищем не «инженера по датасетам», а исследователя, который будет владеть данными как рычагом роста модели — от стратегии сбора до измеримого прироста на бенчмарках. Контур влияния короткий и прямой: ваша гипотеза → эксперимент → обучающий прогон → прирост метрик модели, которой пользуются миллионы людей. Обязанности * Вести программу экспериментов с данными. * Планировать, запускать и анализировать ablation-эксперименты: сравнивать версии смеси данных, фильтров и порядка обучения при фиксированном вычислительном бюджете. Вы автор эксперимента, а не исполнитель. * Оптимизировать смесь и порядок использования данных. * Отвечать на главный вопрос претрейна: какие данные, в какой пропорции и в каком порядке должна видеть модель. Применять интуицию scaling laws к распределению токенов. * Измерять качество данных. * Превращать расплывчатые «хорошие данные» в конкретные измеримые сигналы: классификаторы для отбора документов, насыщенных рассуждениями, кодом и знаниями. * Курировать корпус на масштабе триллионов токенов. * Проводить точную и fuzzy-дедупликацию, фильтрацию, очистку от утечек бенчмарков, html2text. * Собирать данные для agentic-pretrain. * Проводить поиск, сбор и генерацию траекторий использования инструментов, многошаговых рассуждений и взаимодействия со средой — чтобы агентные способности закладывались в модель уже на этапе претрейна, а не только в пост-обучении. * Создавать рецепты подготовки синтетических данных. * Генерировать синтетику под дефицитные способности модели (длинный контекст, рассуждения, код) — с количественной проверкой через эксперименты. * Замыкать петлю: «данные → обучение → оценка». * Развивать контур бенчмарков и сетапов экспериментов для измерений, чтобы каждое изменение данных имело измеримый ответ. Требования * Нам нужны сильные исследователи, а не исполнители. Главное — умение самостоятельно задавать правильные вопросы, проектировать эксперименты и доводить гипотезы до измеримого результата. * Опыт именно с LLM не обязателен, если у вас сильная экспериментальная практика в любой области ML — компьютерное зрение, рекомендательные системы, классический ML, наука — специфике больших языковых моделей мы научим. * Сильные основы ML и статистики, культура контролируемого эксперимента: гипотеза → дизайн → измерение → вывод. * Уверенный Python и опыт обучения моделей на PyTorch или аналогах. * Самостоятельность в исследованиях: умение вести направление от вопроса до результата, а не ждать постановки задачи. * Понимание цикла обучения LLM хотя бы на уровне теории: токенизация, scaling laws, влияние состава данных на качество. * Знакомство с распределённой обработкой данных (Spark, Dask, Airflow, Kubernetes) и открытыми data-проектами (FineWeb, OLMo, Dolma) — как минимум представление о том, как это устроено. Не обязательно закрывать все вышеперечисленные пункты. Если вы сильный экспериментатор и хотите дорасти до владения всем циклом данных фронтирной модели — давайте поговорим. Условия * Вычислительные ресурсы под ваши эксперименты. * Один из крупнейших GPU-кластеров в стране. * ablation-эксперименты — это обучающие прогоны, и вы сможете запускать их регулярно, а не «раз в квартал, если повезёт». * Претрейн с нуля, а не дообучение чужих весов. * Мы обучаем собственную модель с первого токена — это значит, что ваши решения по данным определяют фундамент модели, а не косметику поверх чужого претрейна. Сейчас мы учим модели размером 400–700B параметров, и у нас есть амбиции выйти на масштаб 1.5–2T. * Опенсорс и мировая планка. Мы выкладываем модели в открытый доступ, и наша цель — достичь мирового уровня среди опенсорс-моделей. Ваша работа будет видна всему сообществу и будет сравниваться с лучшими открытыми моделями мира. * Полная ориентация на результат. * Минимум бюрократии, максимум ответственности и свободы. Работу меряем не количеством задач, а приростом модели на бенчмарках при том же бюджете. Подтверждённая гипотеза попадает в обучение следующей модели. * Развитая инфраструктура. * Зрелый стек для работы с данными и экспериментами: S3, YTsaurus, Airflow, DataLens, GitLab CI, wandb. * Вы приходите не строить платформу с нуля, а исследовать — пайплайны, оркестрация, трекинг экспериментов и визуализация уже работают.

Location: Remote, Israel; Remote, Poland; Remote, United Kingdom GitLab is the intelligent orchestration platform for DevSecOps. GitLab enables organizations to increase developer productivity, improve operational efficiency, reduce security and compliance risk, and accelerate digital transformation. More than 50 million registered users and more than 50% of the Fortune 100* trust GitLab to ship better, more secure software faster. The same principles built into our products are reflected in how our team works: we embrace AI as a core productivity multiplier, with all team members expected to incorporate AI into their daily workflows to drive efficiency, innovation, and impact. GitLab is where careers accelerate, innovation flourishes, and every voice is valued. Our high-performance culture is driven by our values and continuous knowledge exchange, enabling our team members to reach their full potential while collaborating with industry leaders to solve complex problems. Co-create the future with us as we build technology that transforms how the world develops software. * Fortune 500® is a registered trademark of Fortune Media IP Limited, used under license. Claim based on GitLab data. Fortune 100 refers to the top 20% ranked companies in the 2025 Fortune 500 list, published in June 2025. Fortune and Fortune Media IP Limited are not affiliated with, and do not endorse products or services of GitLab. An overview of this role As a Threat Research Engineer, you’ll take ideas from the first question to a working proof of concept, deploy it, and analyze the results yourself. This is a full-stack role, with AI central to how we work. You’ll build agentic workflows for research, analysis, and detection, and use AI tools every day to move faster. What you’ll do Research new attack surfaces and threat vectors, including AI coding agents, agent plugins, and package ecosystems. Find and responsibly disclose vulnerabilities in open source projects and developer tools. Analyze software supply chain incidents and events. Build AI-powered workflows and agents that automate research, analysis, and detection. Build and maintain the cloud-based systems behind our detection and advisory work. Improve the quality and coverage of our vulnerability and malware advisories. Write up your findings as blog posts and present them at conferences. Take part in on-call or operational support where your team's ownership model requires it. What you’ll bring Hands-on security experience in research, malware analysis, penetration testing, or security tooling, with both a developer mindset and an adversarial mindset: you can build systems and think about how to break them. Demonstrated experience using AI in real work, such as building with large language model APIs or agent frameworks like LangChain, LangGraph, or agent SDKs. Strong programming skills in Go or Python, with good software engineering practices including testing, code review, continuous integration and continuous delivery, and maintainable code. Experience deploying and running production services on a public cloud. Understanding of how package ecosystems work and how software supply chain attacks happen. Ability to map possible attack vectors, name trade-offs, and make data-driven decisions, balancing quality and speed with customer outcomes in mind while keeping up with security and AI industry trends. Clear writing that explains findings to both engineers and a general audience. Helpful, but not required: published vulnerability findings; security research on AI agents, large language model applications, or Model Context Protocol (MCP) servers; Google Cloud Platform experience; or infrastructure as code experience with Terraform or OpenTofu. You don’t need deep knowledge of compilers or static application security testing engines. A high-level understanding is enough. About the team The Threat Research group researches security threats as they emerge and turns what we learn into protection for GitLab customers. We maintain the GitLab Advisory Database, which powers GitLab’s dependency scanning. We also build detection capabilities, do original vulnerability research, and publish what we find. Recent work includes the Shai-Hulud npm worm and a critical remote code execution vulnerability in Serena, a popular MCP coding agent. You can read more on the Security Labs blog . The base salary range for this role’s listed level is currently for residents of the EU, applicable in the specified country or countries only. Sales roles are also eligible for incentive pay targeted at up to 100% of the offered base salary. Grade level and salary ranges are determined through interviews and a review of education, experience, knowledge, skills, abilities of the applicant, equity with other team members, alignment with market data, and geographic location and having regard to any applicable collective agreements. The base salary range does not include bonuses, equity, or benefits. Poland base salary range: 272,000 - 408,000 PLN How GitLab Supports Full-Time Employees Benefits to support your health, finances, and well-being Flexible Paid Time Off Team Member Resource Groups Equity Compensation & Employee Stock Purchase Plan Growth and Development Fund Parental Leave Please note that we welcome interest from candidates with varying levels of experience; many successful candidates do not meet every single requirement. Additionally, studies have shown that people from underrepresented groups are less likely to apply to a job unless they meet every single qualification. If you're excited about this role, please apply and allow our recruiters to assess your application. Country Hiring Guidelines: GitLab hires new team members in countries around the world. All of our roles are remote, however some roles may carry specific location-based eligibility requirements. Our Talent Acquisition team can help answer any questions about location after starting the recruiting process. Privacy Policy: Please review our Recruitment Privacy Policy. Your privacy is important to us. GitLab is proud to be an equal opportunity workplace and is an affirmative action employer. GitLab’s policies and practices relating to recruitment, employment, career development and advancement, promotion, and retirement are based solely on merit, regardless of race, color, religion, ancestry, sex (including pregnancy, lactation, sexual orientation, gender identity, or gender expression), national origin, age, citizenship, marital status, mental or physical disability, genetic information (including family medical history), discharge status from the military, protected veteran status (which includes disabled veterans, recently separated veterans, active duty wartime or campaign badge veterans, and Armed Forces service medal veterans), or any other basis protected by law. GitLab will not tolerate discrimination or harassment based on any of these characteristics. See also GitLab’s EEO Policy and EEO is the Law . If you have a disability or special need that requires accommodation , please let us know during the recruiting process .

Responsibilities: — Develop foundation-model pre-training through architecture training objectives and data mixes. Requirements: — Expertise in machine learning, model architecture, generative modelling, foundation models, pre-training, and related fields such as molecular interaction, molecular structure, physics, and mathematics. Benefits: — Work onsite in the San Francisco office.

Responsibilities: — Deliver strategic initiatives — Align stakeholders — Improve operational efficiency — Communicate technical developments to executives

Responsibilities: — Improve foundation-model performance through data generation campaigns, evaluations, and post-training recipes. Requirements: — Experience or knowledge in machine learning, ProteinMPNN, statistics, small-molecule modelling, structure prediction, protein language models, antibodies, probability, software engineering, protein science, ESM, protein design, AlphaFold, and RFdiffusion. Benefits: — Not specified.

Responsibilities: — Develop production models for factory planning, pricing, forecasting, inventory, and capacity decisions. Requirements: — Experience with OR-Tools, SQL, Snowflake, PostgreSQL, dbt, Python, Dagster. — Knowledge of forecasting, linear programming, simulation, integer programming, optimisation, constraint programming, and heuristics. Benefits: — Remote work opportunity.

Responsibilities: — Build agentic tools — Develop evaluation systems — Create engineering workflow infrastructure The role involves working with infrastructure, data pipelines, LLMs, failure analysis, multi-agent orchestration, RAG, tool calling, evaluation, MCP, software engineering, DevOps, chip development, fine-tuning, agents, GPU kernels, and context management. Location: San Jose, CA, onsite.

Задачи: — Работа с базами данных — Автоматизация финансовых моделей (Data Analyst) Требования: — Уверенное владение SQL — Продвинутый Excel (таблицы, формулы и др.)

Location: Москва Мы занимаемся pretrain'ом больших языковых моделей в GigaChat: проектируем архитектуру, подбираем рецепт обучения и поддерживаем весь инженерный контур вокруг него. Недавно мы обучили MoE-модель на 700 миллиардов параметров — и на этом не собираемся останавливаться. Обучение идёт на кластерах H100 и B200. GigaChat — самый быстрорастущий проект Сбера, и pretrain — его ядро. **Чем занимается команда** * Архитектура и законы масштабирования. * Рецепт обучения: оптимизаторы, расписание learning rate, нормализация, точность вычислений. * Устойчивость больших прогонов и ускорение сходимости. * Диагностика обучения и оценка изменений с опорой на математический аппарат. * Инженерный контур: воспроизводимость, тесты, CI/CD. * Роль с акцентом на модель, оптимизацию и инфраструктуру обучения, а не на данные. Главная цель — делать обучение быстрее, надёжнее и предсказуемее. **Какие задачи стоят перед командой** * Ускорение цикла «идея → эксперимент → вывод → внедрение». * Снижение количества ручных прогонов и неочевидных сбоев, повышение воспроизводимости и прозрачности результатов. * Повышение надёжности больших прогонов. * Ранняя диагностика деградаций и отделение реальных улучшений от ложных сигналов (расхождение, NaN, коллапс энтропии, артефакты маршрутизации, ложное снижение функции потерь). * Обеспечение безопасного масштабирования при внедрении крупных архитектурных изменений. * Анализ влияния сложных архитектур (например, mixture of experts и маршрутизация) на качество, стабильность и скорость обучения. * Определение и развитие метрик, корректно отражающих изменения в обучении моделей. **Почему мы** Масштаб - 700B MoE уже обучена, дальше — больше. Кластеры на H100 и B200. Публикации. Можно и нужно писать статьи по результатам своей работы — это не ограничивается. Команда - в России нет другой команды, которая занимается pretrain'ом на таком масштабе. Коллеги — люди, которые глубоко разбираются в теме. Влияние. Вы берёте направление целиком. Это не «выполнять задачи из бэклога», а самостоятельно определять, что важно, и доводить до результата. Обязанности * Взять на себя целое направление внутри pretrain'а и развивать его: от постановки задач и планирования экспериментов до внедрения результатов в основное обучение. * Проектировать и проводить эксперименты: формулировать гипотезы, запускать абляции, сравнивать подходы, разбираться в результатах и превращать выводы в решения для основного обучения. * Разбираться с нестабильностью на больших прогонах: искать причины деградаций, строить диагностические метрики, предлагать изменения в оптимизаторе, расписании lr, нормализациях, инициализации, клиппинге, точности вычислений и маршрутизации. * Работать с архитектурой смеси экспертов (MoE): маршрутизатор, балансировка нагрузки, переполнение, артефакты маршрутизации, влияние на качество и производительность. * * Поддерживать большие прогоны и продолжения обучения с чекпоинтов: следить за дрейфом, проверять изменения в коде и конфигурации, снижать риск регрессий. * Улучшать инженерное качество контура обучения: ревью критичных изменений, стратегия тестирования, воспроизводимость экспериментов, профилирование и устранение узких мест. Требования * Глубокое понимание устройства обучения нейросетей: не на уровне обзоров и пересказов, а на уровне, где вы можете объяснить, почему конкретный прогон расходится, глядя на кривые функции потерь, нормы градиентов и энтропии. * Способность самостоятельно взять направление и довести его до результата: от чтения статей и постановки гипотез до внедрения в основной трейн. * Практический опыт с PyTorch и именно с обучением моделей, а не только с инференсом. * Умение доводить исследовательские идеи до надёжного инженерного решения: воспроизводимость, конфиги, тесты, автоматизация, понятные критерии качества. * Хорошую инженерную культуру: аккуратные PR, профилирование, внимание к качеству кода, понятные отчёты об экспериментах. Будет плюсом * Опыт со смешанной точностью и распределённым обучением. * Опыт построения систем оценки моделей или инфраструктуры для экспериментов. Условия * Удалённо. * Возможность оформления в аккредитованную IT-компанию. * Годовая премия по итогам работы до 6 окладов. * Регулярный пересмотр зарплат. * Корпоративный спортзал и зоны отдыха. * Более 400 программ СберУниверситета для роста. * Программа адаптации и помощь руководителя на старте. * Крупнейшее DS&AI community — более 600 DS банка, регулярный обмен знаниями, опытом и лучшими практиками, интерактивные лекции и мастер-классы от ведущих ВУЗов и экспертов технологических компаний, дайджест о самых последних разработках в области DS&AI и отчеты с крупнейших конференций мира, регулярные внутренние митапы. * Расширенный ДМС, льготное страхование для семьи, корпоративная пенсионная программа. * Ипотека для сотрудников по дисконтной программе. * СберПрайм+ и скидки у партнёров. * Бонус за рекомендации в команду

We're looking for someone who understands the Roblox niche and knows how to turn viral topics into engaging YouTube Shorts scripts. AI DEVELOPER – CLAUDE PIPELINE.