Вакансии

Задачи: — Build RAG pipelines, semantic/vector search, conversational AI, and intelligent assistants. — Develop recommendation, ranking, classification, and predictive ML models. — Own end-to-end ML pipelines from data processing and training to deployment and monitoring. — Build MLOps / GenAIOps workflows, CI/CD, GPU inference, and cloud-native AI infrastructure. — Work with Product and Engineering to measure business impact through KPIs and experiments. — Improve AI system scalability, reliability, cost, governance, and performance. Требования: — 4–5+ years in AI, ML, or Data Science. — Strong Python, SQL, and production ML experience. — Hands-on experience with LLMs, Transformers, embeddings, RAG, and prompt engineering. — Experience with PyTorch/TensorFlow, Hugging Face, LangChain/LlamaIndex. — Strong knowledge of Docker, Kubernetes, MLflow/W&B, CI/CD, AWS, and Terraform. — Experience with Kafka, ETL pipelines, vector databases, and search systems. — Ability to independently take AI projects from idea to production. Плюсом будет: — Experience with A/B testing, responsible AI, recommendation systems, regulated environments, or team leadership. — Candidate should be able to work on US time schedule. — Good English conversation skill is much much preferred.

Location: Athens, Greece; Thessaloniki, Greece About the Role We are seeking an AI/ML Engineer - python developer to join our software development team. You will work on challenging international projects, using cutting-edge technology to deliver ML and AI solutions for major international public organizations. The role offers rapid career growth and real exposure to high-impact, large-scale work. What You Will Do Implement advanced ML solutions and model deployment strategies (MLOps); Build advanced Generative AI components and pipelines, including RAG architectures, orchestration layers and evaluation frameworks; Contribute to AI governance, observability, monitoring and responsible AI practices; Contribute to the design and documentation AI solutions; Contribute to python implementation tasks and implementation of micro-services using frameworks such as FastAPI, Flask, Pydantic. etc. Requirements A university degree in IT or a related field; Very good knowledge of python, pandas, numpy, scipy, matplotlib, seaborn; Good knowledge of  FastAPI, Flask, Pydantic, SQL Alchemy; Hands-on experience in use and integration with leading LLMs and SLMs; Familiarity with AI frameworks such as LangChain, LangGraph, Haystack; Familiarity with major ML frameworks such as PyTorch and TensorFlow, and with model formats such as ONNX; Excellent command of English, written and spoken. Nice to Have Experience designing and implementing AI/NLP pipelines (AI Ops / NLP Ops); Familiarity with AWS SageMaker and / or  Azure Machine Learning platform; Familiarity with cloud platforms such as AWS or Azure; Familiarity with Big Data systems such as Spark (PySpark) and/or Databricks. Benefits We believe in rewarding talent and dedication. Here's what you can expect as part of our team: Competitive full-time salary; Private Health Coverage on the Company’s group program; Flexible Working Hours; Top-of-the-Line Tools; Professional Development: Benefit from language courses, specialized training, and continuous learning opportunities; Career Growth: Work with some of the most innovative and exciting specialists in the industry; Dynamic Work Environment: Thrive in a setting that offers challenging goals, autonomy, and mentoring, fostering both personal and company growth. If you want an exciting challenge, work with some of the coolest technologies, and enjoy your time doing it, then join us! Submit your detailed CV in English, quoting reference: ( JA/ME-PD/11/26 ). You may also consider all our other open vacancies by visiting the career section of our website ( www.eurodyn.com ) and follow us on Twitter (@EURODYN_Careers) and LinkedIn. EUROPEAN DYNAMICS (ED) ( www.eurodyn.com ) is a leading European Software, Information, and Communication Technologies company, operating internationally (Athens, Brussels, Luxembourg, Copenhagen, Berlin, Stockholm, London, Nicosia, Valetta, Vienna, Den Haag, Hong Kong, etc.) The company employs over 1100 engineers, IT experts, and consultants (around 3% PhD, 41% MSc, and 54% BSc). We design and develop software applications using integrated, state-of-the-art technology. Our current IT projects have a value exceeding 300 million EURO. EUROPEAN DYNAMICS is a renowned supplier of IT services to European Union Institutions, international organizations, European Agencies, and national government Administrations in 40 countries and 4 continents. As part of our dedication to the diversity of our workforce, we are committed to Equal Employment Opportunity without regard for race, colour, national origin, ethnicity, gender, disability, sexual orientation, gender identity, or religion. EUROPEAN DYNAMICS (ED) adheres to the General Data Protection Regulation principles by applying its Privacy Policy as published at www.eurodyn.com/privacy . By submitting an application to this position and by sharing your personal data with ED, you acknowledge and accept its Policy and authorize ED to process your personal data for the purposes of the company's recruitment opportunities, in line with the Policy.

Location: Thuwal, Saudi Arabia This position is for building, deploying and evaluating Client's Arabic AI tutor for mathematics and computer science: preparing Arabic training data, fine-tuning, aligning and evaluating LLMs on Shaheen III and Ibex, and integrating the tutor into the learning platform for a pilot in at least 10 Saudi schools. The position is part of the RDIA-funded project 5870, “An AI-Powered Arabic Tutoring Platform for STEM Education in the Kingdom.” Two engineers will be hired into this role. Key Responsibilities Prepare Arabic training datasets from educational materials and consented platform interactions. Fine-tune, align, and evaluate commercial and open-source LLMs using Shaheen III and Ibex computing platforms. Develop Arabic STEM benchmarks, preference-training methods, and student-safety guardrails. Integrate AI tutoring capabilities into the learning platform, including Arabic right-to-left interfaces. Deploy and optimize models for reliable, low-latency real-time interactions. Support school pilot implementation, evaluation, and teacher feedback collection. Ensure student data privacy and compliance with Saudi data protection requirements. Contribute to research publications, technical reports, and project deliverables. Qualifications & Experience MSc in Computer Science, Artificial Intelligence, or a related field; alternatively, a BSc with at least one year of relevant experience. Hands-on experience in LLM fine-tuning, evaluation, and AI application deployment. Professional proficiency in Arabic and English. Experience with RLHF/DPO, educational technology, or mathematics/programming education is preferred. Technical Skills Advanced Python and PyTorch. Hugging Face Transformers, PEFT, and TRL. Distributed GPU training using DeepSpeed or FSDP. LLM evaluation, reward modeling, and preference optimization. Arabic NLP, data pipelines, and OCR. Model serving and inference optimization using tools such as vLLM. APIs, Docker, Git, CI/CD, and production deployment.

Location: Mapbox Poland Workplace: Remote Mapbox is the leading real-time location platform for a new generation of location-aware businesses. Mapbox is the only platform that equips organizations with the full set of tools to power the navigation of people, packages, and vehicles everywhere. More than 4 million registered developers have chosen Mapbox because of the platform’s flexibility, security and privacy compliance. Organizations use Mapbox applications, data, SDKs and APIs to create customized and immersive experiences that delight their customers.  WHAT WE DO The Navigation team https://www.mapbox.com/navigation at Mapbox develops and operates web services powering virtually all of Mapbox’s strategic navigation initiatives in consumer, automotive, and logistics verticals. We are a highly multidisciplinary organization, with work spanning distributed systems on AWS to geospatial localization, traffic telemetry, and mapping. Collectively we are revolutionizing in-car experiences with technology to build fast, industry-leading maps and navigation for any vehicle. We’ve launched products such as MapGPT https://www.mapbox.com/mapgpt, Mapbox for EV https://www.mapbox.com/ev, and we’re enhancing the visual experience of in-vehicle navigation with 3D styling with our new Mapbox Standard through web and mobile SDKs. Mapbox has worked with automotive partners https://www.mapbox.com/automotive— including BMW, Toyota, and GM — to bring the best aspects of mobile app development into in-vehicle navigation and infotainment. - Mapbox Blog: Introducing 3D Live Navigation https://www.mapbox.com/blog/mapbox-3d-navigation - Mapbox Blog: Route Notifications Improve Driver Safety https://www.mapbox.com/blog/route-notifications-improve-driver-safety - Mapbox Newsroom: AI Features in New Navigation SDK https://www.mapbox.com/press-releases/mapbox-unveils-ai-features-in-new-navigation-sdk As AI becomes embedded in modern engineering workflows, we value engineers who can thoughtfully integrate AI into design, development, and decision-making. Our interview process will include discussion of how you use AI in your engineering workflow. WHAT YOU’LL DO As a Machine Learning Engineer III on the Routing Cost team, you will lead the design, development, and delivery of highly trafficked recommendation systems for our routing cost engine. Your technical contributions will include: - Lead the engineering team(s) to execute end-to-end feature implementation within the larger navigation organization (~80 engineers). - Design and build new ML and data pipelines, as well as optimizing existing ML and data pipelines. - Build tools for automated quality assurance and data exploration. - Leverage geospatial algorithms to optimize pipelines, as well as creating automated tuning and outlier detection algorithms. - Partner with data scientists to make improvements to our models for determining patterns in movement and/or traffic. - Mentor colleagues and participate in code and design reviews. - Participate in an on-call rotation to ensure our systems remain available to customers 24/7. Team members alternate as the on-call primary responder, which may require immediate response outside normal working hours, including weekends. WHAT WE BELIEVE ARE IMPORTANT TRAITS FOR THIS ROLE - 8-10+ years of backend and ML experience: Extensive hands-on experience showcasing expertise and a strong track record in the engineering field. - Technical knowledge: Extensive experience with Python, distributed processing pipelines (Hadoop, Spark, Airflow, Dask), and very large datasets – statistical analysis, data quality control, optimizing storage. - Cloud proficiency: Extensive experience with cloud and infrastructure services, including, as well as deploying and managing infrastructure in cloud environments. - Operational excellence: You drive a culture of quality and continuous improvement in your organization. Strong passion for clean code and robust architecture, with a drive to elevate engineering practices and design standards across the team. - Ownership & execution: Own the technical vision, lead the strategy and guide the team to execute towards building the future of our routing cost engine. - Customer focus: You care deeply about your customers and their problems, and you work tirelessly to innovate on their behalf. - Growth mindset: Have the humility to accept mistakes, learn and grow from them. - Mentorship: Experience mentoring and leading engineering teams developing skill sets and career growth. - Strong written and vocal communication skills: You can talk and write about complex ideas concisely to a wide audience. You are able to lead and resolve org-wide technical discussions and are able to collaborate and include input from cross-functional teams (security, devops, data, compliance and product). - Data informed mindset: You believe in the value of data informed decision making and strive to measure things that are not straight forward but essential. You also have the right instincts to make speedy decisions in the absence of perfect & comprehensive data WHAT WE VALUE In addition to our core values https://www.mapbox.com/about/values/, which are not unique to this position and are necessary for Mapbox leaders: - We value high-performing creative individuals who dig into problems and opportunities. - We believe in individuals being their whole selves at work. We commit to this through supportive health care, parental leave, flexibility for the things that come up in life, and innovating on how we think about supporting our people. - We emphasize an environment of teaching and learning to equip employees with the tools needed to be successful in their function and the company. - We strongly believe in the value of growing a diverse team and encourage people of all backgrounds, genders, ethnicities, abilities, and sexual orientations to apply. Our annual compensation for this role ranges from $102.000 - $153.000 (salaries are in USD). Job level and actual compensation will be decided based on factors including, but not limited to, individual qualifications objectively assessed during the interview process (including skills and prior relevant experience, potential impact, and scope of the role). By applying for this position, you acknowledge that you have received the Mapbox Non-US Privacy Notice https://www.mapbox.com/legal/applicant-privacy-notice/ for applicants, which is linked here.  Completing this application requires you to provide personal data, such as your name and contact information, which is mandatory for Mapbox to process your application.  We are committed to a fair and equitable hiring process. We do not discriminate against any protected class.

Location: Finland; Mapbox Helsinki Workplace: Remote Mapbox is the leading real-time location platform for a new generation of location-aware businesses. Mapbox is the only platform that equips organizations with the full set of tools to power the navigation of people, packages, and vehicles everywhere. More than 4 million registered developers have chosen Mapbox because of the platform’s flexibility, security and privacy compliance. Organizations use Mapbox applications, data, SDKs and APIs to create customized and immersive experiences that delight their customers.  WHAT WE DO The Navigation team https://www.mapbox.com/navigation at Mapbox develops and operates web services powering virtually all of Mapbox’s strategic navigation initiatives in consumer, automotive, and logistics verticals. We are a highly multidisciplinary organization, with work spanning distributed systems on AWS to geospatial localization, traffic telemetry, and mapping. Collectively we are revolutionizing in-car experiences with technology to build fast, industry-leading maps and navigation for any vehicle. We’ve launched products such as MapGPT https://www.mapbox.com/mapgpt, Mapbox for EV https://www.mapbox.com/ev, and we’re enhancing the visual experience of in-vehicle navigation with 3D styling with our new Mapbox Standard through web and mobile SDKs. Mapbox has worked with automotive partners https://www.mapbox.com/automotive— including BMW, Toyota, and GM — to bring the best aspects of mobile app development into in-vehicle navigation and infotainment. - Mapbox Blog: Introducing 3D Live Navigation https://www.mapbox.com/blog/mapbox-3d-navigation - Mapbox Blog: Route Notifications Improve Driver Safety https://www.mapbox.com/blog/route-notifications-improve-driver-safety - Mapbox Newsroom: AI Features in New Navigation SDK https://www.mapbox.com/press-releases/mapbox-unveils-ai-features-in-new-navigation-sdk As AI becomes embedded in modern engineering workflows, we value engineers who can thoughtfully integrate AI into design, development, and decision-making. Our interview process will include discussion of how you use AI in your engineering workflow. WHAT YOU’LL DO As a Machine Learning Engineer III on the Routing Cost team, you will lead the design, development, and delivery of highly trafficked recommendation systems for our routing cost engine. Your technical contributions will include: - Lead the engineering team(s) to execute end-to-end feature implementation within the larger navigation organization (~80 engineers). - Design and build new ML and data pipelines, as well as optimizing existing ML and data pipelines. - Build tools for automated quality assurance and data exploration. - Leverage geospatial algorithms to optimize pipelines, as well as creating automated tuning and outlier detection algorithms. - Partner with data scientists to make improvements to our models for determining patterns in movement and/or traffic. - Mentor colleagues and participate in code and design reviews. - Participate in an on-call rotation to ensure our systems remain available to customers 24/7. Team members alternate as the on-call primary responder, which may require immediate response outside normal working hours, including weekends. WHAT WE BELIEVE ARE IMPORTANT TRAITS FOR THIS ROLE - 8-10+ years of backend and ML experience: Extensive hands-on experience showcasing expertise and a strong track record in the engineering field. - Technical knowledge: Extensive experience with Python, distributed processing pipelines (Hadoop, Spark, Airflow, Dask), and very large datasets – statistical analysis, data quality control, optimizing storage. - Cloud proficiency: Extensive experience with cloud and infrastructure services, including, as well as deploying and managing infrastructure in cloud environments. - Operational excellence: You drive a culture of quality and continuous improvement in your organization. Strong passion for clean code and robust architecture, with a drive to elevate engineering practices and design standards across the team. - Ownership & execution: Own the technical vision, lead the strategy and guide the team to execute towards building the future of our routing cost engine. - Customer focus: You care deeply about your customers and their problems, and you work tirelessly to innovate on their behalf. - Growth mindset: Have the humility to accept mistakes, learn and grow from them. - Mentorship: Experience mentoring and leading engineering teams developing skill sets and career growth. - Strong written and vocal communication skills: You can talk and write about complex ideas concisely to a wide audience. You are able to lead and resolve org-wide technical discussions and are able to collaborate and include input from cross-functional teams (security, devops, data, compliance and product). - Data informed mindset: You believe in the value of data informed decision making and strive to measure things that are not straight forward but essential. You also have the right instincts to make speedy decisions in the absence of perfect & comprehensive data WHAT WE VALUE In addition to our core values https://www.mapbox.com/about/values/, which are not unique to this position and are necessary for Mapbox leaders: - We value high-performing creative individuals who dig into problems and opportunities. - We believe in individuals being their whole selves at work. We commit to this through supportive health care, parental leave, flexibility for the things that come up in life, and innovating on how we think about supporting our people. - We emphasize an environment of teaching and learning to equip employees with the tools needed to be successful in their function and the company. - We strongly believe in the value of growing a diverse team and encourage people of all backgrounds, genders, ethnicities, abilities, and sexual orientations to apply. By applying for this position, you acknowledge that you have received the Mapbox Non-US Privacy Notice https://www.mapbox.com/legal/applicant-privacy-notice/ for applicants, which is linked here.  Completing this application requires you to provide personal data, such as your name and contact information, which is mandatory for Mapbox to process your application.  We are committed to a fair and equitable hiring process. We do not discriminate against any protected class.

Задачи: — Build RAG pipelines, semantic/vector search, conversational AI, and intelligent assistants. — Develop recommendation, ranking, classification, and predictive ML models. — Own end-to-end ML pipelines from data processing and training to deployment and monitoring. — Build MLOps / GenAIOps workflows, CI/CD, GPU inference, and cloud-native AI infrastructure. — Work with Product and Engineering to measure business impact through KPIs and experiments. — Improve AI system scalability, reliability, cost, governance, and performance. Требования: — 4–5+ years in AI, ML, or Data Science. — Strong Python, SQL, and production ML experience. — Hands-on experience with LLMs, Transformers, embeddings, RAG, and prompt engineering. — Experience with PyTorch/TensorFlow, Hugging Face, LangChain/LlamaIndex. — Strong knowledge of Docker, Kubernetes, MLflow/W&B, CI/CD, AWS, and Terraform. — Experience with Kafka, ETL pipelines, vector databases, and search systems. — Ability to independently take AI projects from idea to production. Плюсом будет: — Experience with A/B testing, responsible AI, recommendation systems, regulated environments, or team leadership. — Candidate should be able to work on US time schedule. — Good English conversation skill is much much preferred.

Задачи: — Build RAG pipelines, semantic/vector search, conversational AI, and intelligent assistants. — Develop recommendation, ranking, classification, and predictive ML models. — Own end-to-end ML pipelines from data processing and training to deployment and monitoring. — Build MLOps / GenAIOps workflows, CI/CD, GPU inference, and cloud-native AI infrastructure. — Work with Product and Engineering to measure business impact through KPIs and experiments. — Improve AI system scalability, reliability, cost, governance, and performance. Требования: — 4–5+ years in AI, ML, or Data Science. — Strong Python, SQL, and production ML experience. — Hands-on experience with LLMs, Transformers, embeddings, RAG, and prompt engineering. — Experience with PyTorch/TensorFlow, Hugging Face, LangChain/LlamaIndex. — Strong knowledge of Docker, Kubernetes, MLflow/W&B, CI/CD, AWS, and Terraform. — Experience with Kafka, ETL pipelines, vector databases, and search systems. — Ability to independently take AI projects from idea to production. Плюсом будет: — Experience with A/B testing, responsible AI, recommendation systems, regulated environments, or team leadership. — Candidate should be able to work on US time schedule. — Good English conversation skill is much much preferred.

Требования к специалисту: — Уверенный опыт работы с LLM/SLM (Few-shot learning, prompt engineering) и BERT (классификация сценариев и интентов). — Практический опыт finetuning LLM (PEFT, LoRA/QLoRA) под узкие прикладные задачи. — Уверенный дебаг LLM: умение быстро понять, почему модель ответила не так, и исправить это промптом, гиперпараметром или файнтюном. — Опыт дообучения и оптимизации TTS-моделей. — Опыт работы и интеграции современных ASR-систем (в частности, GigaAMv2). — Опыт работы как с графовыми/сценарийными диалоговыми движками, так и с LLM-агентами. — Высокая скорость диагностики проблем в инференсе, локализации багов и принятия инженерных решений. — Уверенное владение Python (PyTorch, huggingface, фреймворки инференса) и Go. — Опыт работы с оптимизацией инференса под low-latency (vLLM, TensorRT-LLM, Triton Inference Server, ONNX Runtime). — Практика работы со потоковым аудио в реальном времени (streaming ASR/TTS, gRPC-контракты). — GoLang: базовое понимание дизайна сервисов (SOLID, DRY, KISS); — типизация (строки, числа, слайсы и мапы); — примитивы синхронизации и конкуренция (горутины, каналы); — указатели и работа с ними; — observability. — Будет плюсом: • опыт работы с gRPC. • умение настроить инфраструктуру (k8s/inventory). • знания работы TCP. Задачи: — Поэтапный перевод системы с графа сценариев и скиллов на управляемый LLM-based диалог. — Дебаг голосового пайплайна (поиск причин high latency, галлюцинаций LLM, проблем с передачей аудио, графовых ошибок и т.п.). — Оперативное принятие архитектурных решений и их самостоятельная реализация в коде. — Работа с диалоговым стеком и NLP: поддержка и быстрая отладка few-shot классификации сценариев на базе Qwen и классификаторов на BERT. — Учёт краевых случаев, исправление ошибочных срабатываний интентов. — При необходимости — finetuning локальных LLM (извлечение сущностей, удерживание инструкций). — Работа с речевым стеком: интеграция и поддержка ASR на базе GigaAMv2 (отладка качества распознавания, работы VAD и передачи текста дальше по пайплайну). — Дообучение и настройка инференса собственных и опенсорсных TTS и систем клонирования голоса для достижения нужного качества звучания и скорости работы.

Требования к специалисту: — Уверенный опыт работы с LLM/SLM (Few-shot learning, prompt engineering) и BERT (классификация сценариев и интентов). — Практический опыт finetuning LLM (PEFT, LoRA/QLoRA) под узкие прикладные задачи. — Уверенный дебаг LLM: умение быстро понять, почему модель ответила не так, и исправить это промптом, гиперпараметром или файнтюном. — Опыт дообучения и оптимизации TTS-моделей. — Опыт работы и интеграции современных ASR-систем (в частности, GigaAMv2). — Опыт работы как с графовыми/сценарийными диалоговыми движками, так и с LLM-агентами. — Высокая скорость диагностики проблем в инференсе, локализации багов и принятия инженерных решений. — Уверенное владение Python (PyTorch, huggingface, фреймворки инференса) и Go. — Опыт работы с оптимизацией инференса под low-latency (vLLM, TensorRT-LLM, Triton Inference Server, ONNX Runtime). — Практика работы со потоковым аудио в реальном времени (streaming ASR/TTS, gRPC-контракты). — GoLang: базовое понимание дизайна сервисов (SOLID, DRY, KISS); типизация (строки, числа, слайсы и мапы); примитивы синхронизации и конкуренция (горутины, каналы); указатели и работа с ними; observability. — Будет плюсом: опыт работы с gRPC; умение настроить инфраструктуру (k8s/inventory); знания работы TCP. Задачи: — Поэтапный перевод системы с графа сценариев и скиллов на управляемый LLM-based диалог. — Дебаг голосового пайплайна (поиск причин high latency, галлюцинаций LLM, проблем с передачей аудио, графовых ошибок и т.п.). — Оперативное принятие архитектурных решений и их самостоятельная реализация в коде. — Работа с диалоговым стеком и NLP: поддержка и быстрая отладка few-shot классификации сценариев на базе Qwen и классификаторов на BERT. — Учёт краевых случаев, исправление ошибочных срабатываний интентов. — При необходимости — finetuning локальных LLM (извлечение сущностей, удерживание инструкций). — Работа с речевым стеком: интеграция и поддержка ASR на базе GigaAMv2 (отладка качества распознавания, работы VAD и передачи текста дальше по пайплайну). — Дообучение и настройка инференса собственных и опенсорсных TTS и систем клонирования голоса для достижения нужного качества звучания и скорости работы.

Задачи: — Build RAG pipelines, semantic/vector search, conversational AI, and intelligent assistants. — Develop recommendation, ranking, classification, and predictive ML models. — Own end-to-end ML pipelines from data processing and training to deployment and monitoring. — Build MLOps / GenAIOps workflows, CI/CD, GPU inference, and cloud-native AI infrastructure. — Work with Product and Engineering to measure business impact through KPIs and experiments. — Improve AI system scalability, reliability, cost, governance, and performance. Требования: — 4–5+ years in AI, ML, or Data Science. — Strong Python, SQL, and production ML experience. — Hands-on experience with LLMs, Transformers, embeddings, RAG, and prompt engineering. — Experience with PyTorch/TensorFlow, Hugging Face, LangChain/LlamaIndex. — Strong knowledge of Docker, Kubernetes, MLflow/W&B, CI/CD, AWS, and Terraform. — Experience with Kafka, ETL pipelines, vector databases, and search systems. — Ability to independently take AI projects from idea to production. Плюсом будет: — Experience with A/B testing, responsible AI, recommendation systems, regulated environments, or team leadership. — Candidate should be able to work on US time schedule. — Good English conversation skill is much much preferred.

Задачи: — Управление и валидация кредитных моделей — Управление рисками моделей Требования: — Опыт работы с нейронными сетями, машинным обучением, XGBoost — Знание принципов управления моделями и оценки рисков — Навыки валидации моделей и документации Условия: — Работа в гибридном формате в офисе в Сан-Франциско — Зарплата от $207,600 до $306,600 в год

Location: Los Angeles, USA Anti-Cheat is Riot’s first defense against cheaters that don’t want to play fair. As a cross-discipline team of former cheaters, kernel developers, machine learning engineers, data analysts, and software engineers, they make products like Vanguard to detect and ban the bad guys across all of Riot’s competitive titles and platforms, staying vigilant to whatever shape they’ll take in response. From beaming bots to scorching scripters, anti-cheat enables Riot’s game teams to take on competitive genres and design rewarding systems that would otherwise be too vulnerable to fraudsters and cheaters. Riot Machine Learning Engineers on Anti-Cheat are builders, trailblazers, and stewards of competitive integrity. We protect the legitimacy of Riot’s multiplayer games and player experience by building automated detection and decisioning systems to address a wide range of anticompetitive threats. As a Staff Applied AI/ML Scientist on Anti-Cheat, you will build machine learning driven detection systems to identify players engaging in cheating and other forms of abuse, and will develop agentic systems for reviewing and and actioning these players. You will partner with anti-cheat experts as well as game product, engineering, and insights teams to ensure that all Riot games afford safe, fair environments for players to compete and thrive. You will report to the Senior Manager of Anti-Cheat Data and Analytics. Responsibilities: Design, train, and deploy real-time machine learning models to detect gameplay anomalies, automated bots, and memory-tampering exploits. Build scalable inference pipelines capable of evaluating millions of high-throughput game actions per second with minimal latency impact. Partner with anti-cheat software engineers to identify emerging cheat techniques and engineer high-signal features from raw game client telemetry. Establish robust ML Ops practices, including automated model retrain loops and continuous monitoring for adversarial drift. Analyze complex, high-dimensional player data to minimize false positives and maintain a fair, competitive environment for players globally. Work directly with game design and gameplay engineering teams to balance strict anti-cheat enforcement with smooth, responsive, and uninterrupted player experiences. Collaborate with the data engineering team to troubleshoot and optimize ETL pipelines, ensuring efficient data flow and processing. Help set technical direction within Anti-Cheat ML in an ever-evolving anti-cheat landscape Required Qualifications: Bachelor’s degree or higher in Computer Science, Machine Learning, Statistics, or a related quantitative field, or equivalent practical experience. 6+ years of experience delivering ML systems in production, including 3+ years in applied modeling or ML research roles. Deep understanding of traditional machine learning models, and production deployment experience in at least one domain: computer vision, graph neural networks, transformer architectures, or agentic systems. Excellent written and verbal communication with both technical and nontechnical stakeholders. Demonstrated history of quickly delivering impactful machine learning solutions for critical business problems Product owner's mindset, to be able to identify opportunities for impact and then see them through to production. Track record mentoring engineers across roles and levels; evidence of raising the bar for people around you. Desired Qualifications: Experience working in adversarial detection (e.g. information/cyber security, fraud, money laundering, cheating, etc). Familiarity with relational databases (e.g., MySQL, Postgres), document stores (e.g., ElasticSearch), and distributed storage systems (e.g., HDFS, S3, Google Cloud Storage). Experience defining telemetry or instrumentation needs in close partnership with software or data engineers. Working understanding of Windows internal mechanics (e.g. processor memory, driver architecture, etc.) and how adversarial software can leverage them to bypass security controls. Understanding of high-performance competitive multiplayer games and the challenges associated with them. For this role, you'll find success through craft expertise, a collaborative spirit, and decision-making that prioritizes the delight of players. We will be looking at your past studies, experience, and your personal relationship with games. If you embody player empathy and care about players' experiences, this could be your role! Our Perks: Riot focuses on work/life balance, shown by our open paid time off policy and other perks such as flexible work schedules. We offer medical, dental, and life insurance, parental leave for you, your spouse/domestic partner, and children, and a 401k with company match. Check out our benefits pages for more information. At Riot Games, we put players first . That mission drives every decision in our quest to create games and experiences that make it better to be a player. Whether you’re working directly on a new player-facing experience or you’re supporting the company as a whole, everyone at Riot is part of our mission. And just like in our games, we’re better when we work together. Our goal is to create collaborative teams where you are empowered to bring your unique perspective everyday. If that sounds like the kind of place you want to work, we’re looking forward to your application. It’s our policy to provide equal employment opportunity for all applicants and members of Riot Games, Inc. Riot Games makes reasonable accommodations for handicapped and disabled Rioters and does not unlawfully discriminate on the basis of race, color, religion, sex, sexual orientation, gender identity or expression, national origin, age, handicap, veteran status, marital status, criminal history, or any other category protected by applicable federal and state law. We consider for employment all qualified applicants, including those with criminal histories, in a manner consistent with applicable federal, state and local law, including the California Fair Chance Act, the City of Los Angeles Fair Chance Initiative for Hiring Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, the San Francisco Fair Chance Ordinance, and the Washington Fair Chance Act. Per the Los Angeles County Fair Chance Ordinance, the following core duties may create a basis for disqualifying candidates with relevant criminal histories: Safeguarding confidential and sensitive Company data Communication with others, including Rioters and third parties such as vendors, and/or players, including minors Accessing Company assets, secure digital systems, and networks Ensuring a safe interactive environment for players and other Rioters These duties are directly related to essential operations, safety, trust, and compliance obligations within our organization. Please note that job duties may evolve based on business needs and additional responsibilities may be assigned as necessary to maintain operational efficiency and security.

Задачи: — Build RAG pipelines, semantic/vector search, conversational AI, and intelligent assistants. — Develop recommendation, ranking, classification, and predictive ML models. — Own end-to-end ML pipelines from data processing and training to deployment and monitoring. — Build MLOps / GenAIOps workflows, CI/CD, GPU inference, and cloud-native AI infrastructure. — Work with Product and Engineering to measure business impact through KPIs and experiments. — Improve AI system scalability, reliability, cost, governance, and performance. Требования: — 4–5+ years in AI, ML, or Data Science. — Strong Python, SQL, and production ML experience. — Hands-on experience with LLMs, Transformers, embeddings, RAG, and prompt engineering. — Experience with PyTorch/TensorFlow, Hugging Face, LangChain/LlamaIndex. — Strong knowledge of Docker, Kubernetes, MLflow/W&B, CI/CD, AWS, and Terraform. — Experience with Kafka, ETL pipelines, vector databases, and search systems. — Ability to independently take AI projects from idea to production. Плюсом будет: — Experience with A/B testing, responsible AI, recommendation systems, regulated environments, or team leadership. — Candidate should be able to work on US time schedule. — Good English conversation skill is much much preferred.

Задачи: — Build RAG pipelines, semantic/vector search, conversational AI, and intelligent assistants. — Develop recommendation, ranking, classification, and predictive ML models. — Own end-to-end ML pipelines from data processing and training to deployment and monitoring. — Build MLOps / GenAIOps workflows, CI/CD, GPU inference, and cloud-native AI infrastructure. — Work with Product and Engineering to measure business impact through KPIs and experiments. — Improve AI system scalability, reliability, cost, governance, and performance. Требования: — 4–5+ years in AI, ML, or Data Science. — Strong Python, SQL, and production ML experience. — Hands-on experience with LLMs, Transformers, embeddings, RAG, and prompt engineering. — Experience with PyTorch/TensorFlow, Hugging Face, LangChain/LlamaIndex. — Strong knowledge of Docker, Kubernetes, MLflow/W&B, CI/CD, AWS, and Terraform. — Experience with Kafka, ETL pipelines, vector databases, and search systems. — Ability to independently take AI projects from idea to production. Плюсы: — Experience with A/B testing, responsible AI, recommendation systems, regulated environments, or team leadership. — Candidate should be able to work on US time schedule. — Good English conversation skill is much much preferred.

Location: Berlin, Germany 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 Nebius Token Factory is looking for scientists who can turn frontier LLM and VLM inference bottlenecks into research questions, conduct rigorous experiments, and translate their findings into production capabilities. You will own focused research and optimization projects, write strong code, and produce prototypes, publications, and technical artifacts that engineers can build on. Your research will span quantization, distillation, speculative decoding, KV-cache optimization, and model/runtime co-optimization, alongside inference engines and distributed inference architectures. You will investigate how these approaches interact, evaluate their trade-offs across model quality, latency, throughput, memory footprint, and cost per token, and work with engineering teams to bring promising results into production. Your responsibilities Lead research projects in efficient LLM and VLM inference, from hypotheses and experiments through ablations, prototypes, and production handoff. Develop and evaluate methods spanning quantization, quantization-aware training (QAT), distillation, speculative decoding, KV-cache reuse, KV-cache compression, long-context inference, MoE routing, and model/runtime co-optimization. Build prototypes using PyTorch, Triton, CUDA-adjacent tooling, or inference-serving frameworks, and collaborate with MLEs and platform engineers to turn them into production components. Research LLM request routing and scheduling strategies, including cache-aware load balancing and prefill–decode disaggregation (PDD). Evaluate how queueing, KV-cache transfer, worker placement, and prefill/decode capacity allocation affect latency, throughput, and serving cost. Investigate multi-node inference for dense and mixture-of-experts models, including wide expert parallelism (WideEP). Study expert placement, load imbalance, and computation/communication trade-offs, and use the findings to guide model and system architecture choices. Develop rigorous evaluations covering quality, latency, throughput, numerical stability, memory footprint, tail latency, and cost per token. Compare serving architectures under consistent workload conditions and GPU budgets. Work with MLE, GPU kernel, backend infrastructure, product, and customer teams to select research priorities with measurable production impact. Share results through internal reports, technical blogs, papers, and open-source artifacts, and mentor engineers and scientists on experimental design, scientific rigor, and model/system trade-offs. Must-haves A PhD in computer science, machine learning, ML systems, computer systems, computer architecture, electrical engineering, applied mathematics, or a closely related discipline. A strong publication record or equivalent research artifacts in ML, ML systems, efficient inference, model compression, quantization, distillation, serving systems, or related areas. Strong Python and PyTorch implementation skills, with the ability to turn ideas into experiments and working prototypes. Deep knowledge of LLMs, VLMs, transformer inference, decoding algorithms, model compression, quantization, and production-serving trade-offs. Strong experimental design skills covering ablations, baselines, metrics, statistical reasoning, and failure analysis. Excellent written and verbal communication. Nice-to-haves First-author publications at venues such as NeurIPS, ICML, ICLR, MLSys, ACL, EMNLP, ASPLOS, OSDI, SOSP, ISCA, or HPCA. Experience deploying ML models or inference optimizations in production. Experience with vLLM, SGLang, TensorRT-LLM, NVIDIA Dynamo, FlashAttention, FlashInfer, Triton, CUDA, or PyTorch internals. Experience applying post-training, SFT, DPO, RLHF, RLAIF, preference optimization, or synthetic data generation to inference quality or efficiency. Open-source research artifacts, widely used benchmarks, technical blogs, or invited talks demonstrating contributions to efficient AI systems. Research or implementation experience in one or more areas of distributed inference system architecture, such as LLM request routing, PDD, multi-node serving, or MoE expert parallelism, including WideEP. 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: Zurich, Switzerland 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 Nebius Token Factory is looking for scientists who can turn frontier LLM and VLM inference bottlenecks into research questions, conduct rigorous experiments, and translate their findings into production capabilities. You will own focused research and optimization projects, write strong code, and produce prototypes, publications, and technical artifacts that engineers can build on. Your research will span quantization, distillation, speculative decoding, KV-cache optimization, and model/runtime co-optimization, alongside inference engines and distributed inference architectures. You will investigate how these approaches interact, evaluate their trade-offs across model quality, latency, throughput, memory footprint, and cost per token, and work with engineering teams to bring promising results into production. Your responsibilities Lead research projects in efficient LLM and VLM inference, from hypotheses and experiments through ablations, prototypes, and production handoff. Develop and evaluate methods spanning quantization, quantization-aware training (QAT), distillation, speculative decoding, KV-cache reuse, KV-cache compression, long-context inference, MoE routing, and model/runtime co-optimization. Build prototypes using PyTorch, Triton, CUDA-adjacent tooling, or inference-serving frameworks, and collaborate with MLEs and platform engineers to turn them into production components. Research LLM request routing and scheduling strategies, including cache-aware load balancing and prefill–decode disaggregation (PDD). Evaluate how queueing, KV-cache transfer, worker placement, and prefill/decode capacity allocation affect latency, throughput, and serving cost. Investigate multi-node inference for dense and mixture-of-experts models, including wide expert parallelism (WideEP). Study expert placement, load imbalance, and computation/communication trade-offs, and use the findings to guide model and system architecture choices. Develop rigorous evaluations covering quality, latency, throughput, numerical stability, memory footprint, tail latency, and cost per token. Compare serving architectures under consistent workload conditions and GPU budgets. Work with MLE, GPU kernel, backend infrastructure, product, and customer teams to select research priorities with measurable production impact. Share results through internal reports, technical blogs, papers, and open-source artifacts, and mentor engineers and scientists on experimental design, scientific rigor, and model/system trade-offs. Must-haves A PhD in computer science, machine learning, ML systems, computer systems, computer architecture, electrical engineering, applied mathematics, or a closely related discipline. A strong publication record or equivalent research artifacts in ML, ML systems, efficient inference, model compression, quantization, distillation, serving systems, or related areas. Strong Python and PyTorch implementation skills, with the ability to turn ideas into experiments and working prototypes. Deep knowledge of LLMs, VLMs, transformer inference, decoding algorithms, model compression, quantization, and production-serving trade-offs. Strong experimental design skills covering ablations, baselines, metrics, statistical reasoning, and failure analysis. Excellent written and verbal communication. Nice-to-haves First-author publications at venues such as NeurIPS, ICML, ICLR, MLSys, ACL, EMNLP, ASPLOS, OSDI, SOSP, ISCA, or HPCA. Experience deploying ML models or inference optimizations in production. Experience with vLLM, SGLang, TensorRT-LLM, NVIDIA Dynamo, FlashAttention, FlashInfer, Triton, CUDA, or PyTorch internals. Experience applying post-training, SFT, DPO, RLHF, RLAIF, preference optimization, or synthetic data generation to inference quality or efficiency. Open-source research artifacts, widely used benchmarks, technical blogs, or invited talks demonstrating contributions to efficient AI systems. Research or implementation experience in one or more areas of distributed inference system architecture, such as LLM request routing, PDD, multi-node serving, or MoE expert parallelism, including WideEP. 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: London, United Kingdom 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 Nebius Token Factory is looking for scientists who can turn frontier LLM and VLM inference bottlenecks into research questions, conduct rigorous experiments, and translate their findings into production capabilities. You will own focused research and optimization projects, write strong code, and produce prototypes, publications, and technical artifacts that engineers can build on. Your research will span quantization, distillation, speculative decoding, KV-cache optimization, and model/runtime co-optimization, alongside inference engines and distributed inference architectures. You will investigate how these approaches interact, evaluate their trade-offs across model quality, latency, throughput, memory footprint, and cost per token, and work with engineering teams to bring promising results into production. Your responsibilities Lead research projects in efficient LLM and VLM inference, from hypotheses and experiments through ablations, prototypes, and production handoff. Develop and evaluate methods spanning quantization, quantization-aware training (QAT), distillation, speculative decoding, KV-cache reuse, KV-cache compression, long-context inference, MoE routing, and model/runtime co-optimization. Build prototypes using PyTorch, Triton, CUDA-adjacent tooling, or inference-serving frameworks, and collaborate with MLEs and platform engineers to turn them into production components. Research LLM request routing and scheduling strategies, including cache-aware load balancing and prefill–decode disaggregation (PDD). Evaluate how queueing, KV-cache transfer, worker placement, and prefill/decode capacity allocation affect latency, throughput, and serving cost. Investigate multi-node inference for dense and mixture-of-experts models, including wide expert parallelism (WideEP). Study expert placement, load imbalance, and computation/communication trade-offs, and use the findings to guide model and system architecture choices. Develop rigorous evaluations covering quality, latency, throughput, numerical stability, memory footprint, tail latency, and cost per token. Compare serving architectures under consistent workload conditions and GPU budgets. Work with MLE, GPU kernel, backend infrastructure, product, and customer teams to select research priorities with measurable production impact. Share results through internal reports, technical blogs, papers, and open-source artifacts, and mentor engineers and scientists on experimental design, scientific rigor, and model/system trade-offs. Must-haves A PhD in computer science, machine learning, ML systems, computer systems, computer architecture, electrical engineering, applied mathematics, or a closely related discipline. A strong publication record or equivalent research artifacts in ML, ML systems, efficient inference, model compression, quantization, distillation, serving systems, or related areas. Strong Python and PyTorch implementation skills, with the ability to turn ideas into experiments and working prototypes. Deep knowledge of LLMs, VLMs, transformer inference, decoding algorithms, model compression, quantization, and production-serving trade-offs. Strong experimental design skills covering ablations, baselines, metrics, statistical reasoning, and failure analysis. Excellent written and verbal communication. Nice-to-haves First-author publications at venues such as NeurIPS, ICML, ICLR, MLSys, ACL, EMNLP, ASPLOS, OSDI, SOSP, ISCA, or HPCA. Experience deploying ML models or inference optimizations in production. Experience with vLLM, SGLang, TensorRT-LLM, NVIDIA Dynamo, FlashAttention, FlashInfer, Triton, CUDA, or PyTorch internals. Experience applying post-training, SFT, DPO, RLHF, RLAIF, preference optimization, or synthetic data generation to inference quality or efficiency. Open-source research artifacts, widely used benchmarks, technical blogs, or invited talks demonstrating contributions to efficient AI systems. Research or implementation experience in one or more areas of distributed inference system architecture, such as LLM request routing, PDD, multi-node serving, or MoE expert parallelism, including WideEP. 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: Cyprus Workplace: remote About Finom   Finom is a European tech startup headquartered in Amsterdam, and we’re on a journey towards revolutionizing the financial landscape for entrepreneurs worldwide. Our mission is to develop an all-in-one financial B2B solution that integrates banking functions, accounting, financial management, and invoicing into a seamless, mobile-first platform.   We recently closed a €115 million Series C equity round (around $133 million), bringing our total funding to approximately $346 million. This significant investment follows a $105 million growth funding round from General Catalyst, a long-term backer since 2021 known for supporting companies like Airbnb, HubSpot, KAYAK, and Stripe.   Finom's platform goes beyond traditional banking, offering invoicing and a growing suite of features, including AI-enabled accounting, aiming to simplify financial management for entrepreneurs. We're actively expanding our reach across key EU markets like Germany, France, the Netherlands, Italy, and Spain.   At Finom, we’re not just redefining the entrepreneurial experience — we’re empowering our employees to make a real difference. Your work matters, and your impact extends far beyond product metrics. We nurture innovation and an inspiring work environment where bold ideas thrive, prioritizing thorough research, swift implementation of solutions, and ensuring that every effort we make benefits our users, employees, partners, and our business as a whole.   Maintaining our start-up spirit, we prioritize thorough research, swift implementation of solutions, and ensuring that every effort we make benefits our users, employees, partners, and, of course, our business. At Finom, we're transforming how small and medium businesses across Europe manage their money - and financial crime prevention sits right at the heart of that promise. As a Senior Machine Learning Engineer, you'll take over DRS: a production ML product that scores our whole client base daily and drives our quarantine, alert suppression, and soon our credit decisions. This is genuine end-to-end ownership on a small team - you'll deploy, serve, monitor, and improve the system rather than inherit a finished one. If you're the kind of engineer who quietly keeps production models honest and wants their decisions to actually reach live traffic, this is your seat. What You Will Be Doing Full-stack ML model development (binary classification and regression) for the detection and prevention of financial crime during transaction processing and client onboarding: gathering requirements, prototyping, and deployment in a production environment. Own and evolve the Dynamic Risk Score (DRS) across all markets — daily scoring of the full client base that feeds decisioning across the client lifecycle. Handle a portfolio of transaction rules with ML-driven detection inside, optimizing their performance and the number of generated alerts. Set up continuous monitoring of model and rule performance in production, and develop the champion-challenger framework so the best-performing model is always the one live in production. Participate in infrastructure development for ML model operationalization and inter-service interactions, so models of varying complexity can be served and inferred. Who You Are At least 5 years as a Machine Learning Engineer or Full-stack Data Scientist, with production deployment experience (incl. API services like FastAPI). Strong in SQL and Python, and with cloud data platforms (e.g. Databricks). Strong architectural skills — able to design and improve infrastructure that serves models of varying complexity. Experience with model monitoring and champion-challenger / A-B evaluation in production. Exposure to graph modeling or foundation models/embeddings is a plus. Critical, analytical thinker — proactive, result-driven, comfortable working with little supervision in a start-up setting. A quantitative education (math, engineering, economics, or CS) and full working proficiency in English. What You Will Get In Return   Make a genuine impact on the product   Join our upward trajectory, and grow with us. We provide the resources and opportunities for continuous personal and professional development, empowering you to make a genuine impact on our evolving product.   Work in the EU   Embark on this exciting journey with us and enjoy the flexibility of traveling and working remotely or in a hybrid model across Europe.   Become a stock options holder   Unlock your inner entrepreneur and align your aspirations with ours through our Stock Options Program. This exciting opportunity is available to every team member, from junior team members to our founders.   Receive unwavering support and care   Finom stands by you at every step, embodying our commitment to your well-being and success reflected in our modern, friendly, and eco-conscious corporate culture. We offer constant support and care to ensure your Finom experience is successful and fulfilling.   Work & Swim program   Immerse yourself in our exclusive Work & Swim Program. Spend one month in a comfortable corporate apartment in enchanting Cyprus. It's the ideal opportunity to strike the perfect work-life balance while enjoying breathtaking Mediterranean views.   Equal Opportunity Statement    At Finom, we're an equal opportunity employer and value diversity at our company. We embrace diversity and invite applications from all walks of life. We do not discriminate based on race, religion, color, national origin, gender, sexual orientation, age, marital status, disability status, or other applicable legally protected characteristics.