Вакансии

Обязанности: - Разрабатывать и внедрять ML-функции для платформы знакомств: рекомендации, поиск, безопасность и антифрод. - Строить рекомендательные системы и модели совместимости пользователей для повышения качества мэтчинга. - Разрабатывать NLP-решения для анализа анкет, извлечения структурированных признаков и семантического поиска. - Реализовывать CV-модели для модерации контента, проверки подлинности фото и выявления AI-generated изображений. - Создавать модели скоринга рисков, выявления ботов, фейков и мошеннической активности. - Проектировать полный ML lifecycle: подготовка данных, обучение, оценка качества, деплой и мониторинг моделей. - Работать в тесной связке с продуктом, backend-разработчиком и QA, участвуя в принятии ключевых технических решений. Требования: - Опыт работы ML Engineer от 3 лет и уверенное владение Python. - Практический опыт вывода ML-моделей в продакшн и поддержки их жизненного цикла. - Хорошее знание PyTorch или TensorFlow. - Опыт работы с рекомендательными системами, ранжированием или персонализацией. - Опыт решения NLP-задач: embeddings, классификация текста, semantic search, retrieval. - Понимание MLOps-практик: versioning, monitoring, evaluation, CI/CD для моделей. - Способность самостоятельно принимать технические решения и строить ML-направление практически с нуля. Будет плюсом: - Опыт в Computer Vision: модерация контента, face analysis, deepfake detection. - Опыт создания антифрод- и риск-моделей. - Опыт работы с LLM, RAG и современными open-source моделями. - Опыт работы с векторным поиском (Qdrant, FAISS, Weaviate и др.). - Опыт работы с графовыми моделями и рекомендательными системами. Условия: - Участие в развитии продукта с первых этапов; - Возможность влиять на технические решения и архитектуру системы; - Минимум бюрократии и быстрый цикл принятия решений; - Сильную и мотивированную команду; - Перспективы роста вместе с компанией; - Удалённый формат работы.

Задачи: — Проектировать и владеть сквозными ML и дата-системами от сбора данных до продакшн-деплоя — Архитектурно создавать масштабируемые дата-пайплайны для RAG, эмбеддингов и обработки в реальном времени — Строить и эксплуатировать ML-сервисы, API и инфраструктуру с использованием IaC, контейнеризации и оркестрации — Интегрировать ML-системы с внешними API, инструментами и платформами для автоматизации — Устанавливать мониторинг, оценку и наблюдаемость данных, моделей и систем — Наставлять инженеров и задавать техническое направление команды Требования: — Опыт проектирования и масштабирования продакшн ML-систем и дата-платформ — Глубокие знания в области дата-инжиниринга, ML пайплайнов, RAG и эмбеддингов — Опыт создания надежной инфраструктуры данных с гарантией качества — Навыки Python и SQL, Docker, Kubernetes, облачные среды — Опыт работы с IaC (Terraform) и воспроизводимыми масштабируемыми системами — Практический опыт интеграции ML с API/сервисами и продакшн-операциями с мониторингом Желательно: — Опыт работы с LLM, агентными архитектурами (LangGraph, AutoGen, CrewAI) — Знакомство с синтетическими данными, системами оценки, AI feedback loops — Инструменты наблюдаемости Agent/ML (Langfuse, LangSmith) — Обслуживание моделей, маршрутизация, оптимизация инференса — Опыт с open-source моделями (LLaMA, Mistral) или кастомным инференсом — Паттерны надежности и безопасности систем AI Условия: — Гибридный формат работы: 2 дня в неделю на кампусе в Берлине — Поддержка релокации в Берлин — 27 дней отпуска + дополнительные дни после 2-3 лет — Бюджет на образование €1,000, языковые курсы, Udemy, поддержка родителей — Медосмотры, медитация, субсидии на спорт и велосипед — План акций для сотрудников, банк саббатикала, транспортные скидки, страховка, пенсия — Ваучеры на питание, корпоративные скидки

Location: Tel Aviv, Israel About Nebius: Nebius is leading a new era in cloud infrastructure for the global AI economy. We are building a full-stack AI cloud platform that supports developers and enterprises from data and model training through to production deployment, without the cost and complexity of building large in-house AI/ML infrastructure. Built by engineers, for engineers. From large-scale GPU orchestration to inference optimization, we own the hard problems across compute, storage, networking and applied AI. Listed on Nasdaq (NBIS) and headquartered in Amsterdam, we have a global footprint with R&D hubs across Europe, the UK, North America and Israel. Our team of 1,500+ includes hundreds of engineers with deep expertise across hardware, software and AI R&D. The Product In a rapidly evolving world, trust in AI depends on AI agents being grounded in fresh, verified real-world data. Search is the foundation that makes this possible. We are building an agent-native search platform designed specifically for AI systems rather than human users. Our product provides programmatic, low-latency, and observable search APIs that AI agents use to retrieve, filter, and reason over real-world information at scale. The Role As a Senior ML Engineer focused on Search Optimization, you will work on improving the quality of our search product across the all parts of the search stack. You will work on problems such as query understanding, query reformulation and expansion, retrieval, ranking, reranking, and result selection. Your goal will be to identify where search quality is lost, develop better approaches, and turn them into measurable improvements in production. This is an applied ML and information-retrieval role combining experimentation with production impact. You will work with real-world queries, large-scale search systems, and evaluation signals to improve relevance, recall, freshness, and overall result quality. In this position, your responsibility will be to Design, implement, and operate the retrieval system for a search vertical Connect and tune the data pipeline, from ingestion to relevance tuning Build knowledge-graph and entity-resolution layers: entity linking / NER , ontologies, and graph databases (Neo4j or similar) Develop structured-extraction pipelines over messy, unstructured domain data Reason about freshness and trust: model how confident we are in a fact and how stale it has become before we serve it Define evaluation and quality metrics for relevance and drive measurable improvements Collaborate with crawling, indexing, and ML teams to ensure retrieval and ranking requirements are met Enable safe experimentation with retrieval, ranking, and extraction strategies You may be a good fit if you have: 6+ years of software engineering experience, some of it in search / information retrieval Strong IR fundamentals: inverted indexes, BM25 / TF-IDF , query understanding, ranking, and evaluation (nDCG/ MRR /recall@k) Experience with vector & hybrid retrieval: ANN, dense+sparse fusion, embeddings models Experience building structured extraction over messy/unstructured domain data Fluent in Python and comfortable with systems-level performance work Strong candidates may also have experience with: Knowledge graphs: entity resolution, entity linking / NER , graph DBs (Neo4j), ontologies / schema design Owning relevance / ranking for a real product and improving it against IR metrics Data quality, truth discovery, or systems that decide how much to trust a piece of information Published work on IR , ranking, or knowledge graphs Benefits & Perks: Competitive compensation Career growth and learning opportunities Flexibility and ownership Collaborative and innovative culture Opportunity to work on impactful AI projects International environment and talented teams What's it like to work at Nebius: Fast moving - Bold thinking - Constant growth - Meaningful impact - Trust and real ownership - Opportunity to shape the future of AI Equal Opportunity Statement: Nebius is an equal opportunity employer. We are committed to fostering an inclusive and diverse workplace and to providing equal employment opportunities in all aspects of employment. We do not discriminate on the basis of race, color, religion, sex (including pregnancy), national origin, ancestry, age, disability, genetic information, marital status, veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by applicable law. Applicants must be authorized to work in the country in which they apply and will be required to provide proof of employment eligibility as a condition of hire. If you need accommodations during the application process, please let us know.

Задачи: — Разработка и улучшение biometric и identity verification решений — Создание production-level Computer Vision и ML-систем — Разработка алгоритмов Face Verification, Liveness Detection, Anti-Spoofing и Deepfake Detection — Оптимизация моделей для высоконагруженных систем — Работа с большими датасетами и подготовка данных — Внедрение современных исследований SOTA в production — Разработка и поддержка полного ML/CV pipeline — Участие в подготовке решений к международным сертификациям и тестированиям — Работа над AI-продуктами для банков, финтех-компаний, криптоплатформ, телеком-операторов и государственных организаций Требования: — Опыт работы ML Engineer / Computer Vision Engineer от 3 лет — Опыт работы с Face Biometrics, KYC, Identity Verification или Anti-Fraud решениями — Глубокие знания Computer Vision и Deep Learning — Уверенное владение Python — Опыт работы с PyTorch и/или TensorFlow — Опыт построения production-grade ML/CV систем — Опыт работы с large-scale datasets — Опыт оптимизации inference, latency и производительности моделей — Понимание архитектуры enterprise biometric systems — Английский язык на уровне B2 — Обязательное преимущество: Опыт подготовки решений к сертификации iBeta Level 2 или Опыт прохождения / участия в сертификации iBeta Level 2 Будет большим плюсом: — Опыт работы в biometric, KYC или identity verification компаниях — Участие в NIST FRVT Evaluations — Опыт разработки Anti-Spoofing и Liveness Detection систем — Опыт работы с Deepfake Detection — Понимание международных biometric compliance и certification standards — Опыт внедрения SOTA research papers в production — Опыт teamleading Условия: — Работа в международной AI-компании — Участие в разработке production-grade AI-продуктов для международных рынков — Официальное трудоустройство по трудовому договору — График 5/2, 8-часовой рабочий день — Работа в офисе в Алматы — Финальный оффер зависит от уровня экспертизы — Возможность работать над сложными задачами в области Face Biometrics, Digital Identity, KYC, AML, Fraud Prevention и Identity Verification

Location: Singapore Our mission Constructor’s mission is to enable all educational organisations to provide high-quality digital education to 10x people with 10x efficiency. With strong expertise in machine intelligence and data science, Constructor’s all-in-one platform for education and research addresses today’s pressing educational challenges: access inequality, tech clutter, and low engagement of students. Please send your resume in English only. Brief Job Description: Builds and maintains the infrastructure and tooling that keeps machine learning systems reliable in production — from designing CI/CD pipelines and model deployment workflows to monitoring performance and managing model lifecycle at scale. The role works closely with ML, backend, and platform teams, contributes to automation frameworks and observability standards, and helps ensure AI models move seamlessly from experimentation to production. Requires 5+ years of MLOps or DevOps engineering experience, with a track record of operating robust ML infrastructure in production-grade environments. Mission: Make the path from model experiment to reliable production as fast, automated, and observable as possible. Education: Bachelor’s degree or higher in Computer Science, Engineering, or a related field — or equivalent practical experience. Duties and Responsibilities: Design and maintain CI/CD pipelines for ML models and services. Build and operate model deployment, serving, and rollback workflows. Implement monitoring, observability, and alerting for models in production. Manage the model lifecycle with MLflow: experiment tracking, versioning, registries, and reproducibility. Automate infrastructure and partner with ML and platform teams on standards. Qualifications & Experience: 5+ years of MLOps or DevOps engineering experience. Strong with containers and orchestration (Docker, Kubernetes). Experience with CI/CD (GitLab CI) and infrastructure-as-code (Terraform or similar). Hands-on with a major cloud platform (Azure, AWS, or GCP). Hands-on experience with MLflow for experiment tracking and model registry. Familiarity with ML frameworks (PyTorch), workflow orchestration (Kubeflow or similar), and monitoring stacks. Nice to Have (Not Obligatory): Experience serving LLMs or large models in production. Knowledge of feature stores and data pipeline tooling. Cost and latency optimisation for model serving. What We Offer 💻 Choice of work equipment (e.g., laptop, monitor, etc.) 🇬🇧 English classes (iTalki – $130 monthly) ⏰ Flexible schedule (we usually work between 09:00/10:00 and 18:00/19:00) 👶 Newborn bonus (€500 per child) 🧠 Patent remuneration 🌴 Paid leave 🧑‍💻 Remote work in locations without our offices Hybrid work in locations with offices (2 days in-office, 3 days remote) Constructor fosters equal opportunity for people of all backgrounds and identities. We are led by a gender-balanced board committed to building a diverse and inclusive organisation where everyone can become their best self. We do not discriminate based on age, disability, gender identity, sexual orientation, ethnicity, race, religion or belief, parental and family status, or other protected characteristics. We welcome applications from women, men and non-binary candidates of all ethnicities and socio-economic backgrounds. We encourage people belonging to underrepresented groups to apply.

Location: Europe We are toogeza, a Ukrainian recruiting company that is focused on hiring talents and building teams for tech startups worldwide. People make a difference in the big game, and we may help to find the right ones. Currently, we are looking for an experienced Senior ML Engineer to join one of toogeza’s clients. Professional qualifications - 7+ years’ experience in Machine Learning / Data Science, with 3+ years in credit-lending organisations. - Demonstrated delivery and productionisation of Probability-of-Default (PD) models, credit-limit strategies, fraud-detection, conversion-uplift, and collections-optimisation models. - Advanced Python proficiency and solid grasp of modern ML algorithms, feature engineering, and model-evaluation best practices. - Ability to write, structure, and optimise complex SQL queries. - Deep understanding of the credit lifecycle, especially online lending workflows. - Proven skill in sourcing, cleansing, and generating features from data sets. - Comfortable setting up and maintaining modelling environments (local, cloud, or on-prem). - Detail-oriented, accountable, and committed to both team and individual targets. - English: Intermediate (B1) or higher. Preferred / bonus qualifications - Practical experience with LLM solutions: - Using commercial APIs (e.g., OpenAI, Anthropic, etc.). - Self-hosting of open-source models - Fine-tuning of open-source models. - Building voice chatbots. - Building RAG chatbots. - Experience with Computer Vision models for document or image processing. - Building ML pipelines and deploying models to production. - Creating executive dashboards and model reports in Power BI. Main responsibilities - Design, train, and deploy probability of default models. - Build credit-limit strategies. - Discover and scope AI/ML opportunities that boost efficiency and revenue of the company, including collections optimisation, fraud control, conversion lift, etc. - Analyse data sources and engineer features for modelling. - Produce and update internal model documentation. - Implement model monitoring. - Plan and execute A/B tests. - Build Computer Vision pipelines to automate lending workflows. - Develop LLM-based solutions that streamline internal processes or enhance customer experience. Expected results - Implemented probability of default models and credit-limit strategies. - Launched A/B tests for models that potentially can boost the efficiency and/or revenue of the company. - Thorough, audit-ready documentation for models. What We Offer - Join a fast-scaling FinTech company where your decisions shape the business and your contributions truly matter. - Enjoy 20 paid days off annually, flexible scheduling, and a supportive, people-first culture. - Partial compensation for medical insurance, sports activities, and foreign language. - Work in an international, agile team with ambitious goals, modern tools, and a strong sense of purpose. Professional qualifications - 7+ years’ experience in Machine Learning / Data Science, with 3+ years in credit-lending organisations. - Demonstrated delivery and productionisation of Probability-of-Default (PD) models, credit-limit strategies, fraud-detection, conversion-uplift, and collections-optimisation models. - Advanced Python proficiency and solid grasp of modern ML algorithms, feature engineering, and model-evaluation best practices. - Ability to write, structure, and optimise complex SQL queries. - Deep understanding of the credit lifecycle, especially online lending workflows. - Proven skill in sourcing, cleansing, and generating features from data sets. - Comfortable setting up and maintaining modelling environments (local, cloud, or on-prem). - Detail-oriented, accountable, and committed to both team and individual targets. - English: Intermediate (B1) or higher. Preferred / bonus qualifications - Practical experience with LLM solutions: - Using commercial APIs (e.g., OpenAI, Anthropic, etc.). - Self-hosting of open-source models - Fine-tuning of open-source models. - Building voice chatbots. - Building RAG chatbots. - Experience with Computer Vision models for document or image processing. - Building ML pipelines and deploying models to production. - Creating executive dashboards and model reports in Power BI. Main responsibilities - Design, train, and deploy probability of default models. - Build credit-limit strategies. - Discover and scope AI/ML opportunities that boost efficiency and revenue of the company, including collections optimisation, fraud control, conversion lift, etc. - Analyse data sources and engineer features for modelling. - Produce and update internal model documentation. - Implement model monitoring. - Plan and execute A/B tests. - Build Computer Vision pipelines to automate lending workflows. - Develop LLM-based solutions that streamline internal processes or enhance customer experience. Expected results - Implemented probability of default models and credit-limit strategies. - Launched A/B tests for models that potentially can boost the efficiency and/or revenue of the company. - Thorough, audit-ready documentation for models. What We Offer - Join a fast-scaling FinTech company where your decisions shape the business and your contributions truly matter. - Enjoy 20 paid days off annually, flexible scheduling, and a supportive, people-first culture. - Partial compensation for medical insurance, sports activities, and foreign language. - Work in an international, agile team with ambitious goals, modern tools, and a strong sense of purpose.

Задачи: — Разработка экосистемы сервисов на базе ИИ для автоматизированного анализа контента и аудитории в мессенджерах. — Реализация RAG/LoRA и open source LLM для обработки TG-контента, семантического анализа, предсказаний, генерации контента и ИИ-отчетов. — Закрытие задач по подготовке данных, дообучению моделей и их эффективному развертыванию на собственных мощностях. Требования: — Практический опыт работы с bare-metal серверами и видеокартами Nvidia. Умение конфигурировать CUDA, драйверы, мониторить утилизацию памяти VRAM и решать проблемы OOM. — Способность самостоятельно «под ключ» развернуть open-source модель на собственном сервере - от настройки окружения до оптимизации инференса. — Глубокие знания и коммерческий опыт дообучения LLM (Llama, Mistral, Qwen и др.) с использованием LoRA / QLoRA / PEFT на доменных данных. — Экспертные знания пайплайнов Retrieval-Augmented Generation. Опыт интеграции и настройки векторных баз данных (PGVector, FAISS). — Свободное владение Docker и Docker Compose для локальной сборки и деплоя ML-сервисов. — Опыт работы с фреймворками высокопроизводительного инференса (vLLM, TGI, ONNX Runtime). — Уверенный Python (FastAPI, Pydantic), асинхронное программирование (asyncio, aiohttp) и батчинг запросов для минимизации latency. — Векторизация текста (Sentence Transformers), работа с большими массивами текстовых данных, интеграция с Postgres и Redis. Условия: — Стабильный доход: конкурентоспособная заработная плата. Выплаты 2 раза в месяц по 50% или 1 раз в месяц (согласовывается с сотрудником). — Гибкий формат: график 5;2. На старте (первые 2 месяца) - работа в офисе, далее возможен переход на гибридный формат. — Профессиональную среду: мотивированная команда, отсутствие бюрократии, возможность напрямую влиять на архитектуру ИИ-сервисов. — Реальные возможности для профессионального роста и обучения.

Задачи: - Интегрировать LLM в продукты и сервисы (API, backend, пайплайны обработки данных); - Разрабатывать и оптимизировать prompt’ы, CoT, системные инструкции; - Оценивать качество ответов (метрики + human eval); - Настраивать guardrails: безопасность, фильтрация, соответствие требованиям; - Строить RAG-системы: индексация данных; работа с векторными БД; оптимизация поиска и генерации; - Оптимизировать инференс (стоимость, latency, batching, caching, квантизация); - Участвовать в архитектурных решениях вместе с продуктовой и ML-командой; - Исследовать и внедрять новые подходы и модели; - При необходимости: работать с мультимодальными моделями (текст, изображения, документы). Требования: - Уверенный Python; - Опыт работы с LLM в проде (не только pet-проекты); - Понимание prompt engineering, RAG-подходов, оценки качества моделей; - Опыт деплоя ML/LLM решений (Docker, Kubernetes, cloud); - Работа с векторными базами данных. Будет плюсом: - Fine-tuning / PEFT (LoRA, adapters); - RLHF или synthetic data generation; - Опыт с LangChain, LlamaIndex, Hugging Face; - Оптимизация инференса (vLLM, TensorRT, ONNX); - Опыт с мультимодальными моделями; - Публикации / open-source / pet-проекты; - Участие в хакатонах; - Понимание продуктовых метрик. Условия: - Полная удалёнка; - Аккредитованная ИТ-компания; - Интересный продукт, современный стек, микросервисная архитектура; - Известные клиенты из ТОП-10 в своих сегментах; - Прокачка скиллов под руководством лидов с 8+ лет опыта; - Достойный оклад по итогам собеседования + ежеквартальный сбор ОС с премированием лучших сотрудников; - Возможность взять на себя дополнительные задачи за соответствующее вознаграждение; - Премии за менторство младших сотрудников; - Ежеквартальные бонусы и подарки лучшим сотрудникам; - Пригласите друга и получите щедрое вознаграждение за рекомендацию.

Location: Hybrid (Berlin, Germany). Must be based in or be able to relocate to Berlin, Germany (attendance at the Berlin campus 2 days a week). Company: A pioneering local delivery platform operating in over 70 countries, focusing on efficient, affordable, and sustainable last-mile delivery. What you will do: — Build and scale AI agents capable of reasoning, tool calling, and interacting with internal services and data systems. — Develop and maintain Retrieval-Augmented Generation (RAG) pipelines using vector databases and semantic retrieval systems. — Design and implement evaluation frameworks and safety guardrails for agent behavior and model performance. — Implement observability, monitoring, and cost visibility for LLM and agent workloads. — Collaborate with data scientists and platform engineers to integrate AI systems with the broader ML ecosystem. — Optimize systems for performance, reliability, and cost efficiency while operating AI workloads at scale. Requirements: — Proven experience shipping LLM-powered systems or AI agents into production. — 4+ years of experience in production machine learning infrastructure, specifically involving LLMs. — Strong programming skills in Python with experience building scalable backend services. — Experience with Cloud platforms, ideally AWS or GCP. — Strong communication skills and ability to work collaboratively in cross-functional teams. Nice to have: — Experience with Kubernetes, Terraform, Kafka, MLflow, or Kubeflow. — Experience with LLM application frameworks such as LangChain, LangGraph, or LlamaIndex. — Experience with ML frameworks such as Hugging Face or PyTorch. Culture & Benefits: — Hybrid working model with 2 days a week in the Berlin campus. — 27 days of holiday, increasing with years of service. — €1,000 Educational Budget, language courses, and access to Udemy Business. — Health checkups, meditation, and gym/bicycle subsidies. — Employee Share Purchase Plan, corporate pension plan, and life/accident insurance. — Relocation support and dedicated relocation hub for moving to Berlin.

Location: San Francisco; Palo Alto Perplexity is seeking experienced ML engineers to design, build, and optimize the recommendation systems that power core experiences on Perplexity. Perplexity builds AI for those who expect more. Our products are designed to help people find answers, make their most consequential decisions, and complete increasingly ambitious work. Across these use cases, the Perplexity experience must feel deeply personal. Great recommendations and user understanding are central to delighting and delivering for each user. To do this, we are reimagining recommendation systems for the LLM era. Our goal is to combine the intelligence of frontier LLMs, the personalization context that comes from real product usage, and the continual learning capabilities of modern recommendation systems. We build systems that draw on past context and connected data sources to deeply understand each user's needs and recommend the actions that help them get the most out of Perplexity. WHY PERPLEXITY IS DIFFERENT - Craftsmanship. We build high quality, tasteful products targeting both AI native and AI curious users. - Ownership. You identify the problem, design the solution and ship it. - Entrepreneurship. We think like founders, act with urgency, and hustle to deliver for each other and our users. - Scholarship. Work among highly talented peers, pursuing knowledge and truth, upleveling ourselves, our teams, and our products. - Partnership. We amplify each others’ strengths, break down silos, and give selflessly to help our colleagues deliver excellence. WHAT YOU'LL DO - Own the personalization and ranking behind key product surfaces to make Perplexity more useful and drive impact on core user and business metrics. - Build user modeling that captures intent, preference, and propensity, and powers more relevant, more personalized experiences. - Design the decision layer that balances competing objectives to produce the best overall experience for the user. - Build the data and evaluation foundations that let these systems learn and improve with usage. - Help shape the technical direction of ranking, recommendations, and personalization at Perplexity. WHAT WE'RE LOOKING FOR - Deep, hands-on experience building production recommendation, ranking, or personalization systems at scale. - Strong ML fundamentals, covering areas such as engagement modeling, model calibration, offline and online metrics, and online experimentation. - Experience integrating LLMs into ranking, retrieval, or personalization pipelines. - Taste and judgment for how personalization should work in an LLM-native product, and curiosity about reimagining it from first principles. - For tech leadership roles, we will also look for prior experience setting technical direction for recommendation/ranking projects. NICE TO HAVE - Experience with large-scale ranking and training infrastructure (multi-stage retrieval and ranking, feature stores, real-time serving). - Background in user understanding, feed ranking, notifications, growth, or lifecycle modeling. OUR MISSION Perplexity’s mission is to power curiosity. Curious people are the people who drive change in the world. Driving change is a continuous cycle of learning, building, and integrating. Learn: curious people constantly learn new things by asking more. They question the status quo in their own expertise and they constantly learn outside of it. Research is essential to them and never ending. Build: curious people make and create things, to show the world their new answers to problems no one else ever questioned. They take action on what they’ve learned. Makers need tools to create their products, their companies, their reality. Integrate: they must interact with the world as it is to drive change and adoption. True leaders do not simply build something and hope. They must have armies of agents and workers who can constantly work in millions of small ways. Repeat. For curious people this is a cycle that never ends.

Location: San Francisco; Palo Alto Perplexity is seeking experienced ML engineers to design, build, and optimize the recommendation systems that power core experiences on Perplexity. Perplexity builds AI for those who expect more. Our products are designed to help people find answers, make their most consequential decisions, and complete increasingly ambitious work. Across these use cases, the Perplexity experience must feel deeply personal. Great recommendations and user understanding are central to delighting and delivering for each user. To do this, we are reimagining recommendation systems for the LLM era. Our goal is to combine the intelligence of frontier LLMs, the personalization context that comes from real product usage, and the continual learning capabilities of modern recommendation systems. We build systems that draw on past context and connected data sources to deeply understand each user's needs and recommend the actions that help them get the most out of Perplexity. WHY PERPLEXITY IS DIFFERENT - Craftsmanship. We build high quality, tasteful products targeting both AI native and AI curious users. - Ownership. You identify the problem, design the solution and ship it. - Entrepreneurship. We think like founders, act with urgency, and hustle to deliver for each other and our users. - Scholarship. Work among highly talented peers, pursuing knowledge and truth, upleveling ourselves, our teams, and our products. - Partnership. We amplify each others’ strengths, break down silos, and give selflessly to help our colleagues deliver excellence. WHAT YOU'LL DO - Own the personalization and ranking behind key product surfaces to make Perplexity more useful and drive impact on core user and business metrics. - Build user modeling that captures intent, preference, and propensity, and powers more relevant, more personalized experiences. - Design the decision layer that balances competing objectives to produce the best overall experience for the user. - Build the data and evaluation foundations that let these systems learn and improve with usage. - Help shape the technical direction of ranking, recommendations, and personalization at Perplexity. WHAT WE'RE LOOKING FOR - Deep, hands-on experience building production recommendation, ranking, or personalization systems at scale. - Strong ML fundamentals, covering areas such as engagement modeling, model calibration, offline and online metrics, and online experimentation. - Experience integrating LLMs into ranking, retrieval, or personalization pipelines. - Taste and judgment for how personalization should work in an LLM-native product, and curiosity about reimagining it from first principles. - For tech leadership roles, we will also look for prior experience setting technical direction for recommendation/ranking projects. NICE TO HAVE - Experience with large-scale ranking and training infrastructure (multi-stage retrieval and ranking, feature stores, real-time serving). - Background in user understanding, feed ranking, notifications, growth, or lifecycle modeling. OUR MISSION Perplexity’s mission is to power curiosity. Curious people are the people who drive change in the world. Driving change is a continuous cycle of learning, building, and integrating. Learn: curious people constantly learn new things by asking more. They question the status quo in their own expertise and they constantly learn outside of it. Research is essential to them and never ending. Build: curious people make and create things, to show the world their new answers to problems no one else ever questioned. They take action on what they’ve learned. Makers need tools to create their products, their companies, their reality. Integrate: they must interact with the world as it is to drive change and adoption. True leaders do not simply build something and hope. They must have armies of agents and workers who can constantly work in millions of small ways. Repeat. For curious people this is a cycle that never ends.

Location: Metro Manila; Armenia; Kazakhstan; Georgia Workplace: Hybrid Salmon is a technology-driven financial company building a banking and lending platform across Southeast Asia, starting in the Philippines. We combine global fintech expertise with deep local market knowledge to make financial services simple, accessible, and useful for millions of people across the region. 7M+ app downloads. 2M+ monthly active users. 7,000+ partner stores. US$310M+ raised from leading global investors. Manila-based, globally distributed, and hybrid-first — our team spans 45+ countries. If you want to solve complex problems at scale and impact how millions of people access and manage money, come build with us. Southeast Asia's fintech moment starts here. About the role: This role requires relocation to Manila. We work in a hybrid format from our office in Bonifacio Global City (BGC). You'll own ML models across credit scoring, collection, and antifraud, turning data into systems that directly shape how Salmon lends and collects at scale. What you'll do: - You'll build and ship ML models across credit scoring, collection, antifraud, and sales — high-stakes, high-visibility work from day one. - You’ll work with teams across the business, including Risk, Anti-fraud, and Cross-sell - You'll take models from idea to production, then keep monitoring and improving them as real data comes in. What you'll own: - Own the end-to-end lifecycle of ML models — EDA, feature generation, hypothesis testing, production deployment, and monitoring. - Partner with Collection and Product to identify where a new or improved model would have the most leverage. - Evaluate model performance against business impact, and iterate based on what you find. - Explore and integrate new data sources, internal and external, to surface additional predictive signals. - Build models that are scalable, observable, and maintainable in production systems. What makes you a strong fit: - 2+ years of experience as a Machine Learning Engineer or Data Scientist, ideally in fintech or fast-paced product environments. - Solid foundation in classical ML techniques, fluent in Pandas, Scikit-learn, NumPy, SciPy, Matplotlib, Seaborn, Statsmodels, and gradient boosting libraries (XGBoost, LightGBM, CatBoost). - Strong Python and SQL, with experience using version control. - A strong sense of ownership and accountability. - Experience with graph neural networks or transformer models would be a plus. What we offer: Ownership and flexibility - Hybrid-first work environment - Company-provided tools and equipment - Office in Bonifacio Global City, Manila — a modern, walkable district at the centre of the Philippines' growing tech scene Health and time off - Medical insurance support for you and your family through co-funding or reimbursement, depending on your location and subject to policy limits - Access to an internal mental health support specialist - 22 vacation days, Philippine public holidays, and 15 sick days - 105 days of paid maternity leave and up to 14 days of paid paternity leave for employees relocating to Manila Relocation and family support - Relocation support for you, your family — including flights, two weeks of accommodation, visas, paperwork, and logistics - Reimbursement of eligible pet relocation expenses - USD 2,500 per year for flights home - Baby and family stipends for employees relocating to Manila: USD 1,000 per month for each child under two and USD 500 per month for each child aged two to 18 Growth and team experience - Opportunities to learn and share your expertise through internal expert meetups, external conferences, speaking opportunities, and industry publications - Company-sponsored trips to Manila to meet and work with your team in person - Regular team offsites, sports activities, and team events - High-performing teams can earn a dedicated beach house week in Southeast Asia

Задачи: — анализ банковских и крипто-транзакций для выявления мошенничества и AML-сценариев — разработка и улучшение ML-моделей для fraud detection — graph analysis / Graph ML (GNN, node embeddings, link prediction) — вывод моделей в production Требования: — опыт работы с transactional fraud / AML от 5 лет — strong Python + ML stack (PyTorch / TensorFlow) — опыт с Graph ML / Neo4j / NetworkX / GNN — production ML и ETL pipelines Будет плюсом: — MLOps — ClickHouse / Elasticsearch / Cassandra — опыт с real-time systems и асинхронным Python Условия: — релокация (Узбекистан / Дубай / ЕС) или B2B outside РФ/РБ/Украины — зарплата в валюте — relocation package — международная русскоговорящая команда — сложные ML/AI задачи и сильная technical culture

Основные направления: — Продуктизация ML-моделей и пайплайнов — Интеграция моделей в backend и бизнес-контуры — Разработка API и сервисов для ML — Работа со стримингом данных (Kafka) — Участие в RnD и пилотировании новых ML-решений Обязанности: — Продуктизация и внедрение ML-моделей в production — Рефакторинг кода от DS-команд под production-стандарты — Разработка backend-сервисов и REST API для ML-решений — Интеграция сервисов через Kafka и микросервисную архитектуру — Участие в построении и развитии ML-пайплайнов — Взаимодействие с DS, MLOps и аналитиками — Участие в архитектурных решениях и RnD-задачах Требования: — Опыт разработки на Python от 3–4 лет — Обязательно высшее образование (законченное) — Опыт работы с ML-проектами (CV, NLP, RAG) — Опыт разработки REST API (FastAPI / Flask) — Опыт работы с Kafka — Понимание микросервисной архитектуры и сетевого взаимодействия — Базовое понимание Kubernetes — Опыт работы с SQL на среднем уровне — Умение работать с кодом Data Science и адаптировать его под production Будет плюсом: — Опыт работы с Airflow — Опыт работы с Hadoop / Spark Условия: -ДМС со стоматологией -Компенсация фитнеса -Скидки на курсы английского (Skyeng) и в кино -Индексация зарплаты -Современная техника для работы -IT-аккредитация

Location: Amsterdam, Netherlands; Remote - Europe; Remote - United States About Nebius: Nebius is leading a new era in cloud infrastructure for the global AI economy. We are building a full-stack AI cloud platform that supports developers and enterprises from data and model training through to production deployment, without the cost and complexity of building large in-house AI/ML infrastructure. Built by engineers, for engineers. From large-scale GPU orchestration to inference optimization, we own the hard problems across compute, storage, networking and applied AI. Listed on Nasdaq (NBIS) and headquartered in Amsterdam, we have a global footprint with R&D hubs across Europe, the UK, North America and Israel. Our team of 1,500+ includes hundreds of engineers with deep expertise across hardware, software and AI R&D. The role We are seeking a highly skilled ML/AI Engineer to join our team to lead and support benchmarking of GPU platforms benchmarking of GPU platforms for machine learning and AI workloads. You will play a critical role in evaluating the performance of GPU-based hardware for various deep learning and AI frameworks, enabling data-driven decisions for platform optimisation and next-generation hardware development. Your responsibilities will include: Work closely with hardware, development teams to profile and analyse GPU performance at the system and kernel level. Evaluate and compare GPU performance across different platforms, architectures, and software stacks (e.g.,CUDA, ROCm). Debug and optimise ML workloads to run efficiently on GPU hardware, identifying and resolving performance bottlenecks. Perform acceptance testing acceptance testing for new GPU clusters, ensuring hardware and software meet performance, stability, and compatibility requirements for AI workloads. Perform experiments across diverse GPU system configurations to assess the impact of varying interconnect strategies and system-level optimisations on performance and scalability. Develop tools and dashboards to visualise performance metrics visualise performance metrics, bottlenecks, and trends. Contribute to internal tooling, frameworks, and best practices We expect you to have: A profound understanding of theoretical foundations of machine learning Deep understanding of performance aspects of large neural networks training and inference (data/tensor/context/expert parallelism, offloading, custom kernels, hardware features, attention optimisations, dynamic batching etc.) Deep experience with modern deep learning frameworks (PyTorch, JAX, Megatron-LM, Tensort-LLM) Good understanding of the GPU stack: CUDA,NCCL, drivers, and relevant libraries Familiarity with containerized environments (e.g., Docker, Kubernetes). Strong communication and ability to work independently Ways to stand out from the crowd: Familiarity with modern LLM inference frameworks (vLLM, SGLang, TensorRT) Experience in Python and performance profiling tools (e.g., Nsight, nvprof, perf). Familiarity with cloud ML platforms like AWS, GCP, Azure ML Contributions to open-source ML benchmarking tools Benefits & Perks: Competitive compensation Career growth and learning opportunities Flexibility and ownership Collaborative and innovative culture Opportunity to work on impactful AI projects International environment and talented teams What's it like to work at Nebius: Fast moving - Bold thinking - Constant growth - Meaningful impact - Trust and real ownership - Opportunity to shape the future of AI Equal Opportunity Statement: Nebius is an equal opportunity employer. We are committed to fostering an inclusive and diverse workplace and to providing equal employment opportunities in all aspects of employment. We do not discriminate on the basis of race, color, religion, sex (including pregnancy), national origin, ancestry, age, disability, genetic information, marital status, veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by applicable law. Applicants must be authorized to work in the country in which they apply and will be required to provide proof of employment eligibility as a condition of hire. If you need accommodations during the application process, please let us know.

Location: Ukraine; Spain; Poland; Portugal; Romania About Us At SPD Technology, we bring together a team of like-minded people who are driven by the desire to bring value through their work, united in their commitment to high performance and delivering custom, cutting-edge tech solutions that drive clients’ growth. We empower our people with a culture of excellence and enable them with the opportunity to uphold their accountability to contribute on each level. We value humanity and collaboration, encourage professional and personal growth, and foster a supportive and flexible work environment where everyone’s contribution is welcomed. Now we are looking for a Senior ML Operations Engineer to join us as part of our team. About the role As a Senior ML Operations Engineer, you will support teams building and deploying AI-driven solutions, helping them overcome common challenges in the ML development, deployment, and support lifecycle. This role offers autonomy in decision-making, encouraging a proactive approach to solving complex issues and finding optimal solutions for scalable systems. About the project PitchBook — a platform for investment professionals. Our software provides access to data and the analytical tools to get answers fast and discover promising opportunities. Uncovers actionable insights and trends hidden within the financial data of more than three million companies. Users all over the world include large corporations, start-ups, venture capital and private equity firms, investment banks, and many others. Features of PitchBook: Advanced search / Discovery & insights / Company profiles / Workflow & efficiency / Financials and many more. Team composition 1 Engineering Manager, 4 Senior ML Ops Engineers Work Environment The role offers a flexible work schedule, allowing you to adapt your working hours with the requirement to attend all team meetings. The team follows a Scrum-based Agile methodology. As a qualified expert, You will Serve as a force multiplier for development teams by creating golden paths that remove roadblocks and improve ideation and innovation. Collaborate with other engineers, product managers, and internal stakeholders in an Agile environment. Provide mentorship, technical guidance, and perform code reviews for team members. Design and deliver on projects end-to-end with little to no guidance. Provide support to teams building and deploying AI applications by addressing common pain points in the MLDLC. Learn constantly and be passionate about discovering new tools, technologies, libraries, and frameworks (commercial and open source), that can be leveraged to improve PitchBook’s AI capabilities. Support the vision and values of the company through role modeling and encouraging desired behaviors. Participate in various cross-functional company initiatives and projects as requested. Contribute to strategic planning in a way that ensures the team is building exceptional products that bring real business value. Evaluate frameworks, vendors, and tools that can be used to optimize processes and costs with minimal guidance. We’re looking for you if you have Degree in Computer Science, Information Systems, Machine Learning, or a similar field preferred (or equivalent practical experience). 5+ years of hands-on software development experience with Python (Java experience with strong Python proficiency also considered). 4+ years of experience designing and building distributed software systems and architectures. 3+ years of hands-on experience deploying and operating Machine Learning services in production. Experience supporting ML lifecycle operations including post-deployment monitoring and maintenance. Experience in cloud-native stack, with a practical understanding of containerization technologies such as Kubernetes and Docker. Demonstrated experience with SQL and NoSQL database design and implementation. Ability to decompose complex problems into iterative, well-defined solutions. Strong problem-solving abilities with focus on building scalable, efficient, and maintainable systems. Strong communication and collaboration skills, with the ability to engage effectively with internal customers across various cultures and regions. Ability to be a team player who can also work independently. Experience working across multiple development teams is a plus. Bonus points Experience with cloud platforms (AWS, Google Cloud Platform, or Azure). Proficiency in GitOps practices and CI/CD pipeline development and management. Observability and monitoring: Integration experience with observability tools (Prometheus, Grafana) and building instrumented, production-ready systems. LLM Infrastructure: Experience provisioning and managing Large Language Models through managed services (Azure OpenAI, Google Vertex AI, Amazon Bedrock). LLM Tooling: Hands-on experience with LLM gateways (LiteLLM) and agentic frameworks (LangGraph, LangSmith, or similar). Vector Systems: Practical experience with vector embedding models and vector databases (Pinecone, Weaviate, Milvus, pgvector). RAG Systems: Experience building Retrieval-Augmented Generation systems and evaluating both retrieval quality and generation performance. Cloud-native experience with services like Amazon SageMaker, Google Vertex AI, or Azure ML Familiarity with ML frameworks and tools: PyTorch, TensorFlow, scikit-learn Experience with data infrastructure: Redis, Elasticsearch, Apache Kafka. ML experiment tracking and model management: Weights & Biases, MLflow, KubeFlow. API development with FastAPI or similar frameworks. Java programming experience is a plus. What’s in it for You Reveal great tech solutions Join the team of experts who create custom, cutting-edge tech solutions for world-renowned businesses, fueling clients growth. Unleash your potential, tackle new challenges, and be part of a team that values your skills and contributions. Focus on long-term impact and building tailored, long-lasting partnerships with our clients. Experience an agile and flexible working environment Work fully remote around the globe, or in a hybrid work model from our office hubs which are equipped with everything needed for comfortable work. Enjoy a flexible working schedule and create work and rest balance for yourself. Empower yourself with a stable workload and a stable income, focusing on lasting cooperation and high-performance culture. Embrace the opportunity for personal and professional growth Take advantage of performance review and career path. Elevate your skills with mentorship program, personal development plan, and individual learnings through the corporate library, public speaking support, and more. Feel cared about Be among like-minded people and feel supported with collaboration, open communication and mutual respect. Join company-wide tech and cultural events, and contribute to meaningful CSR initiatives that resonate with your values. Interview steps Interview with the recruiter Live Coding (up to 30 min) Tech Interview: ML Fundamentals, Software Engineering (up to 60 min) ML System Design Interview (up to 45 min) Interview with Engineering Manager (60 min)

Задачи: — Участие в разработке и деплое ML/LLM решений. — Применение Prompt Engineering и методов Fine-tuning/PEFT. — Построение RAG пайплайнов и работа с векторными базами данных. — Оптимизация производительности LLM и оценка их качества. — Разработка с использованием мультимодальных моделей. Требования: — Уверенное владение Python. — Опыт деплоя ML/LLM решений с использованием Docker, Kubernetes, облачных платформ AWS, GCP, Azure. — Опыт работы с Prompt Engineering (few-shot, chain-of-thought, ReAct). — Знание Fine-tuning / PEFT (LoRA, QLoRA, adapters). — Опыт построения RAG пайплайнов (chunking → embedding → retrieval → генерация). — Умение работать с векторными базами данных (Qdrant, Pinecone, Milvus, FAISS). — Опыт RLHF или работы с синтетическими данными (предпочтения, генерация и фильтрация). — Знание фреймворков LangChain, LlamaIndex, Hugging Face. — Опыт оценки LLM (BLEU, ROUGE, MMLU, кастомные эвалы). — Опыт оптимизации инференса (vLLM, TensorRT, ONNX). — Опыт работы с мультимодальными моделями (LLaVA, GPT-4V, Qwen-VL). — Грейд Middle/Middle+. Условия: — Полная занятость. — Удалённый формат работы. — Интересные проекты в области ИИ.

О ПРОЕКТЕ DigiNavi — это иммерсивная AR-навигация для крупных общественных пространств (ТЦ, аэропорты). Мы превращаем локации в SMART-зоны с помощью Computer Vision и дополненной реальности. Наша команда внедряет подход VibeCoding: вы выступаете архитектором решений, используя AI-агентов (Claude Code, Cursor) как основной инструмент исполнения. ЧЕМ ПРЕДСТОИТ ЗАНИМАТЬСЯ — AI-Driven Development: Разработка и поддержка кода через AI-инструментарий. — R&D & Prototyping: Быстрая проверка гипотез и внедрение алгоритмов (SuperPoint, NetVLAD, Gaussian Splatting). — Architecture: Рефакторинг CV-компонентов в гетерогенной среде (Go / C++ / Python). — Integration: Внедрение ML-моделей в микросервисную архитектуру через gRPC и Kafka. — Optimization: Работа с high-load системами и оптимизация инференса моделей. ЧТО МЫ ЖДЕМ ОТ ВАС — CV & ML Expert: Глубокие знания PyTorch и OpenCV. Опыт создания Computer Vision решений. — VibeCoding Skills: Практический опыт работы с Claude Code, Cursor или аналогами. Понимание SDD и TDD подходов. — Backend: Опыт production-развертывания (FastAPI, Docker) и понимание микросервисов. — Polyglot Mindset: Готовность работать в окружении, где используются Go и C++ (чтение и интеграция). — Документация: Умение создавать техническую документацию и структурировать процессы там, где их нет. БУДЕТ ПЛЮСОМ — Опыт оптимизации инференса для мобильных платформ. — Работа с облачной инфраструктурой GCP. — Понимание специфики разработки в сфере AR/VR.