Данные и ML вакансии - гибрид

Location: Helsinki; Paris Workplace: Hybrid ABOUT VOODOO Founded in 2013, Voodoo is a tech company that creates mobile games and apps with a mission to entertain the world. Gathering 800 employees, 7 billion downloads, and over 200 million active users, Voodoo is the #3 mobile publisher worldwide in terms of downloads after Google and Meta. Our portfolio includes chart-topping games like Mob Control and Block Jam, alongside popular apps such as BeReal and Wizz. TEAM The Engineering & Data team builds innovative tech products and platforms to support the impressive growth of their gaming and consumer apps which allow Voodoo to stay at the forefront of the mobile gaming industry. The Voodoo Ad-Network is an autonomous product group of around 60 highly driven professionals with an ambitious mission: building top-tier ad network services. Our primary goal is to leverage Voodoo's massive first-party data ecosystem to optimize and scale monetization. We are in a rapid growth phase, expanding into new ventures such as opening to external inventory, penetrating the external advertiser market, and driving social network monetization following our recent acquisition of BeReal. To support this incredible trajectory and promising early results, we are scaling our team. The Models team is a core element of the targeting performance. It leverages machine learning and a strong business understanding to directly impact the product's financial performance. It's composed of mostly senior Data Analysts, Analytics Engineers, Data Engineers and Data Scientists/ML Engineers that iterate together on finding and building the next performing iteration. This role can be either Paris or Helsinki based and done in a hybrid setup. ROLE We're looking for a Senior ML Engineer to join our Models team. You will be joining a dedicated squad of Data Engineers, Data Scientists, and ML Engineers focused on building and maintaining the ML training infrastructure that powers our ad-targeting models from data preprocessing through model deployment to production monitoring. In this role, you will be leading the following topics: - Architectural Ownership: Take end-to-end ownership of highly visible projects from ideation to production release. This includes feature scoping, timeline estimation, architecture design, and benchmarking new technologies. - Pipeline Engineering: Build and maintain quality data and ML pipelines to align with ever-evolving business and machine learning needs. Optimize training pipelines for performance, memory efficiency, and cost (e.g. spot instance strategies, efficient data loading, preprocessed artifact reuse). - Data Scientists Enablement: Enable Data Scientists to iterate faster by providing reusable, well-tested pipeline components (transformers, dataloaders, training utilities) and reviewing their contributions to shared code. Extend dataset capabilities: integrating new data sources, scaling feature windows, and increasing training data volumes without breaking pipeline constraints. - Deep Learning Development: Contribute to deep learning development: GPU workload orchestration, custom PyTorch training loops, and model architecture support. - ML Lifecycle & Reproducibility: Maintain reproducibility and consistency across the ML lifecycle: versioned configs, experiment tracking, and online-offline consistency tooling. - Scalability & Reliability: Collaborate with infrastructure teams on scalability — node pools, resource monitoring, CI/CD migrations. Participate in weekly rotation to triage and resolve alerts from Airflow, dbt, and related systems. - Agile Collaboration: Thrive in a fast-paced agile environment with rapid decision-making processes. You will collaborate daily with back-end developers, data scientists, infrastructure engineers, and product managers. - Mentorship & Team Culture: You will actively contribute to our engineering culture, share knowledge, and ensure every team member feels comfortable, supported, and empowered to grow in their role. PROFILE We are looking for a Senior ML Engineer who deeply understands both the ML training lifecycle and the specific challenges of putting machine learning models into production at scale. - 5+ years minimum of experience as an ML Engineer or a similar role - End-to-End ML Ownership: Problem framing, baselines, experimentation, deployment, and iteration - Python Proficiency: Extensive knowledge for ML pipeline code: preprocessing, training/evaluation workflows, experiment utilities, and reproducible configs - Training Mechanics: Comfortable implementing training mechanics where needed (custom steps/metrics, dataloading patterns, performance-conscious preprocessing), not only notebook-level prototyping - ML Frameworks: Practical experience training and evaluating models with scikit-learn, LightGBM, PyTorch for deep learning - Performance Optimization: Experience optimizing memory usage during data preprocessing and model training - Experiment Management: Hyperparameter tuning, experiment tracking, and reproducible training (configs, seeds, versioning) - Cloud & Infrastructure: Experience with Amazon Web Services. Familiarity with scalability, reliability, and security topics - ML Production Awareness: Understanding of the challenges involved in running ML models in production (familiar with topics such as feature store, training-serving skew, etc.) - Model Serving: Experience with model serving infrastructure (real-time or batch inference, latency/throughput optimization) is a plus - Excellent communication skills in English OUR STACK Python · Scikit-learn · LightGBM · PyTorch · DBT · Spark · MLflow · Prometheus · Terraform · Airflow · Kubernetes · Amazon Web Services BENEFITS - Excellent benefits that will depend on the country you're based in

Location: London Workplace: Hybrid WHO ARE WE? Cohere is the leading security-first enterprise AI company. We build cutting-edge foundation AI models and end-to-end products that are designed to solve real-world business problems. We’re training and deploying frontier models for enterprises who are building AI systems. We believe that our work is instrumental to the widespread adoption of AI and we are looking for folks that want to be part of that. We obsess over what we build. Each one of us is responsible for contributing to increasing the capabilities of our models and the value they drive for our customers. Cohere is a team of researchers, engineers, designers, and more, who are all passionate about their craft. We are a global technology company headquartered in Toronto with key offices in London, New York City, San Francisco, Montreal, Paris, Berlin and Seoul. Join us! Role Overview: Building AI agents that can assist with any kind of enterprise work is a challenging, open-ended problem. One key piece of solving it is replicating real work environments as realistically as possible - filling them with hard tasks to solve, creating plausible input data, and defining clear rewards for completing the work the right way. We build many of these reinforcement learning (RL) environments, then drop our agents into them to evaluate or train them. In this role, you are responsible for creating these RL environments, running AI agents inside them, and improving both the agents and the environments in the process. The results reach customers, whose feedback feeds back in - and the agent/environment improvement loop continues. Key Responsibilities: There are many open problems in this space. As a Member of Technical Staff, RL Environments, you will: - Build new RL environments targeting different agentic capabilities and industry areas - Train and evaluate agents in those environments - Make all the pieces work together: tasks, data, tool implementations, and verifiers - Work across modeling and product to identify gaps in agent performance, and improve both the agents and the environments - Work with external vendors to create high-quality, expert-built RL environments, and build tools to ensure high task, data, and verifier quality - Automate the discovery of model capability gaps, and systematically measure agent performance during evals and training Qualifications: You may be a good fit if: - You have engineered agents and optimized them for specific industry use cases - You have spent dozens of hours reviewing agent trajectories to pinpoint exact failure points and fix them with model training or harness engineering - You obsess over measuring agentic capabilities and turning that into a repeatable process - You have had many debates about what a good outcome from an AI agent should look like, you translated that into verifier implementations and tuned the reward designs - You have designed and run annotation workflows to surface insights into agent performance and verify data quality - You have built synthetic data pipelines to scale eval and training efforts - You use agents yourself in your daily work, and have stories about how you improved your setup to 10x your productivity - A plus: you have experience with training with RL: scaling, troubleshooting and tuning the environments If some of the above doesn’t line up perfectly with your experience, we still encourage you to apply. Working Location: This role can be based remotely or from one of our office locations listed on the job description - there is no minimum in-office qualification requirement. We care most about hiring exceptional people regardless of locations, though please check the location listed on the posting for guidance around the core time zone or working hours alignment expected for the role.   FULL-TIME EMPLOYEES AT COHERE ENJOY THESE PERKS: - A weekly lunch stipend of $75/£75 or equivalent in your local currency for lunch. - Full health and dental benefits, including a separate budget for mental health. - RRSP matching, 401K, Pension Scheme. - 100% Parental Leave top-up for up to 6 months, for either parent. - Annual enrichment benefits: Arts & culture, fitness/wellness, quality time, and a workspace improvement credit. Education & learning stipend for conferences, courses, and coaching. - 6 weeks of paid vacation (30 working days!) - Budget for traveling to other offices if you are remote, plus an annual company offsite. HOW AND WHERE WE WORK: - Cohere is remote-friendly, but we also have offices in Toronto, London, New York City, San Francisco, Montreal, Paris, Berlin and Seoul with more opening soon. - For those in the office: a daily lunch program, plenty of snacks, and regular community and social events. - For those not near an office: a co-working benefit so you can work alongside others in your city. - Everyone receives a $500 home office stipend to set up your workspace properly. If any of the above doesn’t line up exactly with your experience, we still encourage you to apply. We strive to create an inclusive work environment for all; we welcome applicants from all backgrounds and are committed to providing equal opportunities. Should you require any accommodations during the recruitment process, please submit an Accommodations Request Form https://docs.google.com/forms/d/12a6IrLdF3kI2nonKSr4tiFuz18rLQbaeYV-JM9L4o9Q/edit, and we will work together to meet your needs. We may use AI-enabled tools to screen and assess applicants against the criteria for this position. This helps our recruiters identify potentially qualified candidates, but it doesn't limit the applications our recruiters may review or consider. Beware of Scams: Cohere will never ask for payment or third-party services (e.g., CV writing) as part of our hiring process. All legitimate roles are listed on the Cohere careers page and LinkedIn only, with all communications from Cohere employees coming from an @cohere.com or @cw.cohere email alias. If jobs are viewed on other sites then please verify these through our official careers https://cohere.com/careers page.

Задачи: -Помощь в развитии рекомендательной системы. -Обеспечение производительности и отказоустойчивости runtime и offline-процессов. -Улучшение ML-инфраструктуры. Требования: -Навыки разработки на Java 21 или более поздних версиях. -Понимание микросервисной архитектуры. -Знание алгоритмов и структур данных. -Опыт работы с Git. -Английский язык на уровне чтения технической документации.

Location: Kazakhstan; Kyrgyzstan; Uzbekistan 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 You'll build and own the data pipelines and lakehouse architecture that turn raw data into reliable inputs powering products used by millions of people. What you'll do - Design and run batch and streaming pipelines on Databricks and Apache Spark over AWS, ingesting from databases, APIs, and event streams. - Shape our lakehouse architecture on S3 and Delta Lake, including medallion (Bronze / Silver / Gold) layering and storage optimization. - Work directly with analytics, engineering, and product teams to turn raw data into reliable, usable inputs for the business. What you'll own - Own projects end to end — from stakeholder alignment and design review through implementation, launch, and ongoing support. - Build self-service data solutions that let other teams deliver their own data needs, designed to scale and extend easily as new sources and use cases are added. - Build and maintain scalable ETL/ELT pipelines that are fault-tolerant and cost-optimized. - Optimize Spark jobs end to end — cluster sizing, caching, joins, shuffles, partitioning, and storage formats (Parquet, Delta). - Manage Databricks Workflows, jobs, cluster configurations, and the underlying infrastructure — security policies, resources, and access — responding to failures and performance issues. - Implement data governance and access control with Unity Catalog across accounts and environments, including handling of PII. What makes you a strong fit - Hands-on experience with Databricks and strong proficiency in Apache Spark (PySpark and/or Scala). - Strong Python for data engineering, plus SQL and Spark SQL for transformation and analytics. - Experience with Delta Lake and lakehouse architectures, including performance optimization. - Proven ownership — you've run projects where you knew the weak points, gathered feedback, and made your solutions easy to scale and maintain. - Experience writing clean, testable, maintainable code with Git-based version control. - Experience with infrastructure-as-code such as Terraform or Ansible is a plus — not required, and easy to pick up on the job. What we offer Build what's next in Southeast Asian fintech. Ownership and flexibility - Stock option plan — four-year vesting with a 25% cliff after the first year - Hybrid-first, with fully remote work available for eligible roles and core collaboration hours from 12:00 to 19:00 Manila time (UTC+8) - 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 - US$2,500 per year for flights home - Baby and family stipends for employees relocating to Manila: US$1,000 per month for each child under two and US$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 Considering a move to Manila? See what life with Salmon looks like https://www.youtube.com/watch?v=nYQLKhuZHIE&t=34s

Location: Paris Workplace: Hybrid ABOUT VOODOO Founded in 2013, Voodoo is a tech company that creates mobile games and apps with a mission to entertain the world. Gathering 800 employees, 7 billion downloads, and over 200 million active users, Voodoo is the #3 mobile publisher worldwide in terms of downloads after Google and Meta. Our portfolio includes chart-topping games like Mob Control and Block Jam, alongside popular apps such as BeReal and Wizz. TEAM The Engineering & Data team builds innovative tech products and platforms to support the impressive growth of Voodoo's gaming and consumer apps, keeping us at the forefront of the mobile industry. The team plays a key role in Voodoo's long-term strategy: it accelerates product diversification and provides a state-of-the-art growth engine to distribute and scale our games. We create solutions that drive growth with a pragmatic approach, ranging from simple search to complex machine learning systems. Within Engineering & Data, the Central Data & Analytics team makes data a reliable guide for the whole company. Data engineers, analytics engineers, data analysts and a product data PM work side by side to build and grow the foundations behind every critical business and product decision at Voodoo. This position is hybrid and requires 3 days/week in our Paris office. ROLE We’re looking for a Senior Data Engineer to join our Engineering & Data team. In this role, you’ll be responsible for a wide array of topics such as: - Build and operate robust data pipelines that keep pace with ever-evolving business needs, ensuring the architecture supports them cleanly. - Contribute to the codebase, ship and maintain your changes in production, and champion strong coding and data engineering practices. - Use AI coding tools (Claude Code, Codex, and whatever comes next) as a core part of your daily workflow — to move faster on pipelines, migrations, tests and documentation — while staying fully accountable for what you merge. - Look for opportunities to put AI inside our data platform, not just around it: LLM-assisted data quality checks, semantic layers, natural-language access to datasets, agent-driven internal tooling. - Take ownership of projects from initial discussions to release: feature estimation and scoping, architecture design, benchmarking of new technologies, product feedback. - Work in a very agile environment with a fast decision process. - Collaborate directly with back-end engineers, data scientists, mobile game and consumer app teams, product and marketing. PROFILE - Extensive experience as a Data Engineer or in a similar role. - A proven track record of building and optimizing data pipelines for massive volumes of data (at scale). - Strong experience with scalability, reliability, and security topics. - Hands-on experience coding with AI assistants and agentic tools — you know how to give them the right context, break work down for them, and review their output critically rather than trusting it blindly. - Genuine curiosity about modern tooling and practices: you follow how the data and AI ecosystem is evolving, try new tools on real problems, and form your own opinion instead of following hype. You can explain why you adopted something — or why you dropped it. - Strong analytical skills and comfort working with unstructured datasets. - Experience with Amazon Web Services. - Result-oriented and focused on the value your work creates. - Curious about business needs and keen to build innovative, pragmatic solutions that grow the business through data. - Excellent communication skills (you can speak and write English). - Experience with LLM-based systems (RAG, evals, prompt and context engineering) is a strong plus. - Experience in gaming, advertising, or another fast-growing company is a plus. - Familiarity with Voodoo's ecosystem: gaming, apps, advertising, analytics. OUR STACK - Amazon Web Services; Python; Spark; Scala; Terraform; SQL (Athena, StarRocks); DBT;; Airflow; Kubernetes; AI-assisted development tooling (Claude Code, MCP-based internal tools) BENEFITS - Competitive salary based on experience - Swile Lunch voucher - Gymlib (100% covered by Voodoo) - Premium healthcare coverage with SideCare, 100% covered for you and your family - Wellness activities in our Paris office - Remote Fridays

Задачи: - Развивать инструменты хостинга ML-моделей: внедрять и развивать inference-движки и сопутствующую инфраструктуру, следить за мировыми трендами, делать R&D мировых решений. - Развивать ML-инфраструктуру: трекер экспериментов Clear ML, внутренние инструменты для разметки данных, векторную БД Milvus, решение для запуска распределенных вычислений, а также те инструменты и сервисы, про которые мы уже думали, но еще не внедрили. - Развивать внутренние сервисы: поддерживать планировщик вычислительных ресурсов для запуска DS-решений, бота для глубокого анализа каналов поддержки Центра ИИ и прикладные решения для управления кластерами и биллингом. Требования: - Опыт работы с Docker и Kubernetes для развертывания ML-моделей. - Опыт работы с Ansible. - Знание и опыт внедрения DevOps-практик: CI/CD, IaC, SRE. - Навыки работы с системами мониторинга Prometheus, Grafana, ELK. - Опыт программирования на Python на уровне понимания экосистемы, базовых библиотек и навыков написания прикладных программ. - Опыт проектирования и внедрения инфраструктурных решений. - Способность и готовность работать в условиях высокой неопределенности. Будут плюсами: - Опыт работы в роли MLOps-инженера или аналогичной должности. - Понимание жизненного цикла ML-решений и специфики их развертывания на production. - Опыт работы с векторными БД. - Знание ML-инструментов и библиотек. - Опыт работы с GPU-серверами и диагностики проблем с GPU. Условия: - Уровень дохода, который зависит от ваших технических знаний и навыков. Регулярно пересматриваем зарплаты с учетом профессионального роста. Финансовые бонусы тоже есть. - Заботу о вашем комфорте. Можете работать удаленно, гибридно или в офисе, в любом случае предоставим технику и все необходимое. Если вам по душе работать из офиса — обеспечим удобным местом, зонами отдыха, кухней с полезными перекусами. - Сильное инженерное комьюнити. Регулярно проводим внутренние технические конференции, обмениваемся опытом между командами. Вы всегда найдете, с кем посоветоваться. - Возможность сменить продукт, команду или предметную область внутри компании. Поддерживаем развитие в любом направлении. - Общение на «ты» и максимум горизонтальных связей в коллективе, чтобы быстрее договариваться и решать рабочие задачи. - Возможность делиться экспертизой. Помогаем выступать на конференциях, участвовать в подкастах или видео, писать статьи. - Поддержку профессионального развития. Организуем внутреннее и внешнее обучение.

About our Machine Learning Business Banking team: Our mission in Business Banking is to simplify banking for small businesses; making business banking fairer, simpler and more transparent. Monzo Business Banking is fast-growing, is the UK’s most recommended business account for overall service quality. We recently hit 1 million customers and are used by 1 in 6 UK SMEs. We want to help businesses spend less time on financial admin and more time focused on running and growing their business. We’re building intelligent, data-driven and AI/ML-enabled product experiences that help small businesses feel more in control, make better decisions, and get more value from Monzo. ML is at the core of how we build and scale our products, enabling Monzo to make better decisions, faster, and helping us serve businesses more effectively. As a Lead Machine Learning Scientist in Business Banking, you’ll help build and ship models that power the next generation of Business Banking experiences. You’ll work closely with Product, Engineering, Design, Research, Data Science and Analytics Engineering to bring ML-enabled product capability into the heart of how we build for businesses. Whether it’s helping businesses understand their cash flow, reducing manual admin, surfacing the right insight at the right moment, or helping teams build intelligent product experiences responsibly, our work makes business banking smarter, simpler and more useful for SMEs. What you'll be working on As a Lead Machine Learning Scientist, you’ll be a technical leader and hands-on individual contributor, spearheading our ML and GenAI capabilities and shipping key models that powers magical Business Banking experiences. You’ll: — Be one of the first ML Scientists in Business Banking, bringing leadership through ambiguity by adding structure and direction to our ML capabilities while staying agile and building momentum. — Develop and deploy advanced ML models on our cloud-native data platform to serve hundreds of thousands of business customers. — Partner with Product, data science and other stakeholders to identify the highest-impact opportunities, size impact, and define success metrics. — Decide when to use ML, GenAI, or simpler approaches and be clear on trade-offs. — Design robust evaluation and monitoring so we can ship responsibly and measure impact in production. — Lead the design and implementation of batch and near-real-time models (e.g. LLM-powered experiences, predictive models, time-series forecasting). — Collaborate closely with MLOps and Backend Engineering to operationalise models end-to-end and raise the bar on ML lifecycle rigor. — Set technical standards, mentor others, and support experimentation and iteration. You should apply if: — What we’re doing here at Monzo excites you! — You have a multiple year track record of excellence leading the development and deployment of advanced Machine Learning models to tackle real business problems, preferably in a fast-moving tech company. — You have experience developing and shipping state-of-the-art ML models to production and delivering business impact. — You're impact-driven and excited to own the end-to-end journey that starts with a business problem and ends with your solution having a measurable impact in production. — You have a self-starter mindset; you proactively identify issues and opportunities and tackle them without being told to do so. — You speak Python fluently and have extensive, hands-on experience with scikit-learn. — You are comfortable using SQL, and keen to learn Go, which is used in many of our backend microservices. — You’re comfortable working in a team that deals with ambiguity and have experience helping your team and stakeholders resolve that ambiguity. — You want to be involved in building a product that small businesses use to run and manage their financial lives every day. — You have a product mindset: you care about customer outcomes and you want to make data-informed decisions. — You’re excited about fast-moving developments in Machine Learning and AI and can communicate those ideas to colleagues who are not familiar with the domain. — You’re adaptable, curious and enjoy learning new technologies and ideas. — You’re excited by the opportunity to help Business Banking evolve into AI and ML-enabled product capability. — You can work closely with Product, Engineering, Design, Research and Data colleagues to shape product strategy, clarify trade-offs and build things that customers actually use. — You’re thoughtful about responsible ML/AI; especially in financial products where trust, transparency and customer control matter. Nice to haves: — Experience working on personalisation, ranking, recommendation, forecasting, classification or decisioning problems for customer-facing applications. — Experience working on ML systems for fintech, banking, accounting, payments, lending, invoicing, cash flow, tax, risk or business software. — Experience with GenAI, LLMs, agentic workflows, retrieval, evaluation frameworks, or human-in-the-loop product experiences. — Commercial experience writing critical production code and working with microservices. — Experience designing experiments, A/B tests or other approaches to measure product and customer impact. — Experience working in regulated environments or with products where explainability, auditability and customer trust are especially important. The interview process: Our interview process involves 3 main stages. We promise not to ask you any brain teasers or trick questions! — 30 minute recruiter call — 45 minute call with hiring manager — Full Loop: ML Modelling Interview, Product & ML interview, Behavioural Interview, Leadership interview Our average process takes around 3-4 weeks but we will always work around your availability. You will have the chance to speak to our recruitment team at various points during your process but if you do have any specific questions ahead of this please contact us on tech-hiring@monzo.com. Please also use that email to let us know if there's anything we can do to make your application process easier for you, because of disability, neurodiversity or any other personal reason. What’s in it for you: — We can help you relocate to the UK — We can sponsor visas — This role can be based in our London office, but we're open to distributed working within the UK (with ad hoc meetings in London). — We offer flexible working hours and trust you to work enough hours to do your job well, at times that suit you and your team. — Learning budget of £1,000 a year for training courses and conferences — And much more, see our full list of benefits here If you prefer to work part-time, we'll make this happen whenever we can - whether this is to help you meet other commitments or strike a great work-life balance.

Location: New York, NY (HQ); San Francisco, CA Workplace: Hybrid ABOUT RAMP Ramp is building the smart infrastructure for finance teams, embedded in the transaction flow of every dollar a business spends. We automate how over $275B in annualized spend flows in and out of 85,000+ companies: authorizing payments, flagging risk, categorizing spend, and closing books. The problems are high-stakes, data-dense, and unforgiving. We hire people with high agency and high urgency. We look for slope over intercept. We care less about where you trained and more about what you’ve built. At Ramp, everyone is a builder who owns problems end to end and makes consequential decisions that shape the outcome. The median Ramp customer saves 7.5% and grows revenue 16% in their first year – far in excess of businesses operating without Ramp. We believe every ambitious company deserves the same. If you want to build systems that directly shape how companies move and manage billions, Ramp is the place to do it. WHAT YOU’LL DO - Full stack development, building models to consume, transform, and expose data to stakeholders and production systems - Drive a culture of experimental design, testing agenda, and best practices - Contribute to the culture of Ramp’s data team by influencing processes, tools, and systems that will allow us to make better decisions in a scalable way - Collaborate with Finance teams (e.g. GTM Finance, StratFin) to develop financial insights and influence business decisions - Work closely with data engineering teams to capture, move, store, and transform raw data into highly actionable insights, and partner with business teams to turn those insights into action WHAT YOU NEED - Minimum of 3 years of industry experience in Data Science / Software Engineering / Finance - Strong AI proficiency as a lever to quickly adopt new skills and subject matter - Track record of shipping high quality products and features at scale - Ability to thrive in a fast-paced, constantly improving, start-up environment that focuses on solving problems with iterative technical solutions - Familiarity with financial metrics and processes (e.g. contribution profit, financial statements, monthly close) and / or B2B enterprise sales cycle metrics and processes - Strong knowledge of SQL (preferably Redshift, Snowflake, BigQuery) and how to write efficient SQL queries NICE-TO-HAVES - Experience with the modern data stack (Fivetran / Snowflake / dbt / Looker / Hightouch or equivalents) - Familiarity with BI tools (preferably Looker, Omni, Sigma, Hex or equivalent) and experience distributing data insights via reports and dashboards - Strong intuition on business strategy and customer empathy (ROI, growth channels, sales cycles, customer incentives) - Strong perspective on analytics engineering development cycle (data modeling, version control, documentation + testing, best practices for codebase development) - Experience partnering with finance teams or within the payments and financial technology space BENEFITS AVAILABLE TO ALL FULL-TIME RAMP EMPLOYEES (GLOBAL) - Flexible PTO - Centralized home-office equipment ordering - Health and wellness stipend - Budget for intra-office travel - Weekly coffee stipend UNITED STATES - 100% medical, dental & vision insurance coverage for you, with partial coverage for dependents - One Medical annual membership - 401(k), including employer match on contributions made while employed by Ramp - Fertility HRA (up to $10,000 per year) - Parental leave: up to 16 weeks (birthing + bonding) or 8 weeks (bonding only) at 100% pay - Pet insurance - In-office perks: lunch, snacks, drinks, and more - Relocation expense coverage to NYC or SF (if needed) CANADA - Group medical, dental, and vision coverage through Sun Life - Life, AD&D, and disability coverage - Fertility drug coverage (up to $4,000 lifetime) - Group Retirement Plan with employer match (RRSP + DPSP) - Parental leave: up to 16 weeks (birthing + bonding) or 8 weeks (bonding only) at 100% pay, with additional time available at reduced pay - Employee Assistance Program and virtual care through Lumino Health UNITED KINGDOM - Private medical insurance through Freedom Elite - Virtual GP and at-home care via eMed x Livi - Workplace pension through Penfold, with salary sacrifice option - Parental leave: up to 16 weeks (birthing + bonding) or 8 weeks (bonding only) at 100% pay with additional time available at reduced pay REFERRAL INSTRUCTIONS If you are being referred for the role, please contact that person to apply on your behalf.   OTHER NOTICES Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.   Beware of recruiting scams: Ramp will only contact you through official @Ramp.com http://Ramp.com email addresses and will never ask for payment or sensitive personal information during the hiring process.   Ramp Applicant Privacy Notice https://ramp.com/legal/applicant-privacy-notice

Задачи: - Разрабатывать REST API с использованием FastAPI; - Проектировать и реализовать backend-сервисы с нуля; - Проводить интеграции с базами данных и системами обмена данными; - Обеспечивать масштабируемость и отказоустойчивость сервисов; - Писать и поддерживать автоматизированные тесты; - Настраивать и поддерживать CI/CD процессы для непрерывной интеграции; - Участвовать в планировании задач и работе по методологии Scrum; - Тесно взаимодействовать с командой для оперативного решения технических вопросов. Требования: - Сильные инженерные навыки, самостоятельность, понимание полного цикла доставки приложения; - Уверенный опыт настройки и использования CI/CD, работы с Docker и Kubernetes; - Понимание DevOps-культуры, умение написать и поддержать Helm-чарт/Dockerfile, разобраться с деплоем, настроить базовый мониторинг и алертинг; - Глубокие знания Python и опыт работы с FastAPI; - Опыт работы с PostgreSQL, ClickHouse, Kafka, Redis, Spark; - Умение писать чистый, поддерживаемый код с соблюдением best practices и знание архитектурных паттернов; - Опыт работы в высоконагруженных системах и решения реальных задач на production; - Аналитический склад ума, системное мышление и готовность самостоятельно доводить задачи до конца; - Продвинутое знание инструментов мониторинга (Prometheus, Grafana, Opensearch/ELK); - Глубокий опыт в DevOps, опыт работы с Triton Inference Server и другими инструментами деплоя ML-моделей будет плюсом.

Задачи: — Проектировать, строить и оптимизировать крупномасштабные ETL, стриминговые и пакетные конвейеры обработки данных. — Реализовывать и поддерживать высокопроизводительные/низколатентные хранилища данных (например, для рекомендаций в реальном времени). — Внедрять и улучшать механизмы управления данными для обеспечения качества и надежности данных. — Ускорять инновации, создавая и улучшая инфраструктуру машинного обучения, включая фреймворки для обучения, оценки и экспериментов. — Сотрудничать с менеджерами продуктов, дата-сайентистами, инженерами и аналитиками для создания положительного влияния на клиентов. — Помогать определять цели команды. — Постоянно улучшать самоорганизацию команды. Требования: — Опыт применения принципов инженерии программного обеспечения к системам управления данными. — Умение строить надежную, масштабируемую инфраструктуру данных с использованием качественного кода, а не только SQL. — Опыт не менее 3 лет в проектах по инженерии данных. — Хорошее понимание распределенных механизмов обработки данных. — Умение диагностировать и оптимизировать медленные конвейеры данных. — Опыт работы с крупномасштабной пакетной и/или потоковой обработкой (например, Spark, Hadoop, Flink, Storm, Apache Beam, Kafka). — Владение Python или Java. — Глубокое понимание распределенных баз данных, особенно высокопроизводительных/низколатентных хранилищ данных (например, DynamoDB, Cassandra, Redis). Условия: — Программа акций для сотрудников. — Скидки 40% на товары моды и красоты Zalando, 30% на Zalando Lounge, скидки у внешних партнеров. — 2 оплачиваемых дня волонтерства в год. — Гибридный режим работы с возможностью до 60% удаленной работы в неделю. — Возможность работать из-за рубежа до 30 рабочих дней в году. — 27 дней отпуска в год с начала работы. — Помощь с релокацией (по предварительной договоренности). — Семейные услуги, включая консультации и поддержку. — Опции для здоровья и благополучия (включая Gympass). — Поддержка психического здоровья и коучинг. — Возможности развития через обучающую платформу и двухразовые отзывы от коллег.

Задачи: — Проектировать, строить и оптимизировать крупномасштабные ETL, стриминговые и пакетные конвейеры обработки данных. — Реализовывать и поддерживать высокопроизводительные/низколатентные хранилища данных (например, для рекомендаций в реальном времени). — Внедрять и улучшать механизмы управления данными для обеспечения качества и надежности данных. — Ускорять инновации, создавая и улучшая инфраструктуру машинного обучения, включая фреймворки для обучения, оценки и экспериментов. — Сотрудничать с менеджерами продуктов, дата-сайентистами, инженерами и аналитиками для создания положительного влияния на клиентов. — Помогать определять цели команды. — Постоянно улучшать самоорганизацию команды. Требования: — Опыт применения принципов инженерии программного обеспечения к системам управления данными. — Умение строить надежную, масштабируемую инфраструктуру данных с использованием качественного кода, а не только SQL. — Опыт не менее 3 лет в проектах по инженерии данных. — Хорошее понимание распределенных механизмов обработки данных. — Умение диагностировать и оптимизировать медленные конвейеры данных. — Опыт работы с крупномасштабной пакетной и/или потоковой обработкой (например, Spark, Hadoop, Flink, Storm, Apache Beam, Kafka). — Владение Python или Java. — Глубокое понимание распределенных баз данных, особенно высокопроизводительных/низколатентных хранилищ данных (например, DynamoDB, Cassandra, Redis). Условия: — Программа акций для сотрудников. — Скидки 40% на товары моды и красоты Zalando, 30% на Zalando Lounge, скидки у внешних партнеров. — 2 оплачиваемых дня волонтерства в год. — Гибридный режим работы с возможностью до 60% удаленной работы в неделю. — Возможность работать из-за рубежа до 30 рабочих дней в году. — 27 дней отпуска в год с начала работы. — Помощь с релокацией (по предварительной договоренности). — Семейные услуги, включая консультации и поддержку. — Опции для здоровья и благополучия (включая Gympass). — Поддержка психического здоровья и коучинг. — Возможности развития через обучающую платформу и двухразовые отзывы от коллег.

Требования: — 5+ лет в production ML/NLP: системы, которые вы поддерживали после запуска — техническое лидерство: решения по стеку за команду, ревью, ответственность за сдачу шире своих задач — RAG целиком: гибридный поиск, чанкинг, реранкинг, методика оценки — self-hosted инференс открытых моделей, квантизация, расчет GPU — eval-наборы: чем вы докажете, что новая версия не хуже прошлой — развертывание в закрытом контуре, на инфраструктуре заказчика Половина роли техническая, половина переговорная: защищать архитектуру и метрики качества перед ИТ-директором заказчика. Условия: — Оплачиваемый испытательный срок — Бонусы за сдачу проектов — Офис в Алматы — Зарплата: 1 000 000 – 1 400 000 ₸

Задачи: - Реализация высоконагруженных конвейеров обработки данных для обеспечения надежной и бесперебойной репликации данных из ИТ-систем Банка. - Реализация комплексных задач по подготовке данных в целевых аналитических хранилищах (DataLake, SandBox, FeatureStore) для построения признаков, необходимых для разработки моделей машинного обучения. - Разработка и поддержание в актуальном состоянии документации по разработанному функционалу. - Своевременное отражение статуса выполнения задач в Jira. - Проверка качества кода (код-ревью), написанного инженерами и младшими инженерами данных. Требования: - Python - уверенное знание структур данных и алгоритмов, эффективное применение принципов ООП и ФП (Функциональное программирование), опыт написания модульных и интеграционных тестов, знание и опыт применения библиотек обработки и анализа данных - numpy, pandas. - Опыт разработки и внедрения в промышленную эксплуатацию сервисов загрузки и обработки неструктурированных и слабо структурированных данных (текст, xml, json) из внешних источников. - Способность разобраться с API поставщиков данных, используя доступную документацию. - SQL - умение создавать сложные запросы с использованием аналитических оконных функций и использовать инструменты профилирования для оптимизации их производительности, опыт работы с БД Oracle, Postgres, Greenplum. - Уверенное знание и опыт работы с инструментами разработки, планирования и мониторинга рабочих процессов (workflow engines) пакетной обработки данных. - Airflow. - Опыт разработки сложных, высоконагруженных приложений обработки данных на основе PySpark, уверенное знание настроек Spark и их влияния на производительность приложений Spark.

Задачи: - Развитие алгоритмов ранжирования и рекомендаций для контента вертикали Посутка - Построение и развитие моделей улучшения качества контента - Исследование возможностей внедрения ML и AI в команде - Вместе с продуктовым руководителем вертикали формировать планы и отвечать за результат работы ML моделей Требования к кандидату: - Опыт работы от 3х лет - Опыт доведения ML-моделей до продакшена и оценки влияния на бизнес от внедрения - Опыт построения ML-моделей для персонального ранжирования, рекомендаций и предсказания CTR - Опыт использования технологий для работы с большими данными и распределенными системами (Hadoop, Spark, S3) - Знание ML-алгоритмов и применение их на практике - Владение Python и основными ML-фреймворками - Понимание различных моделей монетизации и механизмов управления ликвидности - Прикладной опыт и подтвержденные успехи в решении задач ранжирования и построения рекомендаций

Location: Athens; Limassol; Nicosia; Thessaloniki Workplace: hybrid The Role:  We are looking for a Senior Data Scientist specializing in Graph AI to join our growing Data Science team. In this role, you will design and deploy production-grade graph machine learning solutions, working with Graph Neural Networks (GNNs), graph databases, and large-scale datasets to solve complex real-world problems.   You will play a key role in shaping the company's Graph AI capabilities, collaborating with cross-functional teams and mentoring other data scientists while delivering innovative, business-impacting solutions. The main responsibilities of the position include: Design, develop, and deploy Graph Neural Networks (GNNs) and graph-based machine learning solutions Transform large, complex datasets into graph representations and extract actionable insights using advanced analytics, graph algorithms, and statistical techniques. Drive the adoption of Graph AI across the organisation by identifying new use cases and contributing to the evolution of the company's data science strategy. Communicate complex technical concepts and model outcomes clearly to both technical and business audiences. Ensure high standards in data preparation, feature engineering, model evaluation, documentation, and code quality . Leverage technologies such as Python, Graph Neural Networks, graph databases, modern ML frameworks, and cloud-based data platforms to build innovative data solutions Ensure adherence to best practices in data quality, model monitoring, version control, and reproducible analytics Lead and supervise a team of data scientists working on graph-related tasks Partner closely with Data Engineers, Software Engineers, Product Managers, and business stakeholders to translate business challenges into scalable Graph AI solutions. Main requirements: University degree in Mathematics, Physics, Computer Science, Engineering, Data Science, or a related quantitative field At least 6 years of experience in data science, machine learning and AI , including hands-on experience in designing, training and optimising Graph Neural Networks (GNNs) for production applications. Experience working with relational and non-relational databases Solid experience working with graph databases (e.g., Neo4j, Neptune) and proficiency in graph query languages (e.g., Cypher or Gremlin). Experience with data visualization tools such as Matplotlib, Seaborn, or Plotly Strong programming skills in Python and experience with Pandas, NumPy , and similar data analysis libraries Strong analytical thinking, problem-solving ability, and attention to detail . Excellent communication skills and the ability to work collaboratively in a team environment Fluent in English Benefit from: Attractive remuneration package plus performance related reward Private health insurance Corporate pension fund Intellectually stimulating work environment Continuous personal development and international training opportunities The Hiring Experience: What Awaits You Let’s Connect – Intro Chat with Talent Acquisition Deep Dive – First Interview with Your Future Team Bring It to Life – Role-Specific Take-Home Task Final Connection – Final Interview All applications will be treated with strict confidentiality!   By submitting this application, I confirm that I have read and agree to the terms of the Privacy Policy and the Confidentiality Undertaking.

Задачи: - Разработка co-pilot решения на базе данных RAG и применении LLM - Разработка и интеграция AI-агентов, исследование новых поверхностей взаимодействия с AI - Разработка микросервисов на Python в качестве MCP инструментов - Анализ требований, формализация функционала, проектирование архитектуры взаимодействия агентов и сервисов - Участие в планировании, декомпозиции задач и оценке сложностей, планировании спринтов на основе SCRUM церемоний - Проведение код-ревью, поддержание высокого стандарта качества и чистоты кода - Тесное взаимодействие с командой на всех этапах разработки - Участие в подготовке и выпуске релизов, взаимодействие со смежными подразделениями - Документирование своей работы и менторство джуниор-разработчиков Требования: - Опыт коммерческой разработки на Python от 4 лет - Уверенное знание Python 3.7+ (в проекте используется 3.12+) - Понимание и успешный опыт применения паттернов проектирования (GoF, SOLID, GRASP) - Опыт проведения код-ревью и работы по GitFlow - Опыт разработки агентных систем с помощью langchain, langgraph и успешное внедрение в промышленную среду - Опыт разработки приложений, основанных на микросервисной архитектуре - Уверенное знание SQL и понимание принципов работы реляционных СУБД (PostgreSQL) - Опыт написания unit-тестов - Опыт разработки асинхронных приложений на Python - Понимание жизненного цикла разработки приложений в контексте Agile - Опыт и желание работать в команде, умение аргументировать свои решения и договариваться Условия: - Возможность участия в интересном проекте - Возможность профессионального и карьерного роста в компании - Опыт работы в команде профессионалов - Уровень заработной платы обсуждается индивидуально - Удаленный формат работы и офисный формат в Москве

Location: Limassol; Athens; Nicosia Workplace: hybrid The Role: Are you passionate about enterprise database systems, adept with both on-premises and cloud environments, and driven by operational reliability?   If you thrive in a dynamic environment where performance tuning, data security, and high availability fuel your growth, we want to connect with you! Join our team as a Database Administrator and be part of a community that values your expertise and encourages your continuous advancement. The main responsibilities of the position include: Manage and support various databases (Oracle, MySQL, PostgreSQL, MongoDB) on-premises as well as in the cloud, handling daily support, performance optimisations, infrastructure improvements, backups, and replication streams Be the primary DBA for some databases, collaborate with other DBA team members to keep the tasks evenly distributed between everyone Handle migrations of various data sets and schemas between distinct database environments Monitor database performance metrics continuously and proactively troubleshoots and resolves structural or transactional issues Provide technical direction and guidance towards DB configuration Work in close cooperation with the Information Security Department to maintain security, access rights and compliance of all databases Recommend and implements emerging database technologies Collaborate effectively with other departments to achieve operational and project related goals Main requirements: BSc/MSc in Information Technology or any other relevant field Minimum 3 years’ experience in a similar position, preferably in the financial services industry Hands-on experience with major DB engines (MSSQL, MySQL, Oracle, PostgreSQL, MongoDB, or ClickHouse) alongside strong SQL writing and optimization skills Solid knowledge of AWS services (RDS, DMS, S3, IAM), with additional cloud experience (Azure, Oracle Cloud, MongoDB Atlas) considered a plus Strong knowledge of Linux/Windows environments, Shell scripting, and database/infrastructure monitoring tools (CloudWatch, OEM, Zabbix, Percona Toolkit, NewRelic) Excellent verbal and written communication skills in English Proven ability to collaborate effectively across cross-functional teams High accountability, strong attention to detail, and an adaptable mindset focused on continuous upskilling and operational efficiency Benefit from: Attractive remuneration package plus performance related reward Private health insurance Corporate pension fund Intellectually stimulating work environment Continuous personal development and international training opportunities The Hiring Experience: What Awaits You Let’s Connect – Intro Chat with Talent Acquisition Deep Dive – First Interview with Your Future Team Test your edge – Aptitude / Personality Assessment Final Connection – Final Interview All applications will be treated with strict confidentiality!   By submitting this application, I confirm that I have read and agree to the terms of the Privacy Policy and the Confidentiality Undertaking.