Вакансии

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

Location: San Juan, Puerto Rico; New York, NY; District of Columbia; Europe | Spain Workplace: Hybrid About the Company Rain is the global stablecoin payments platform for enterprises, neobanks, platforms, developers, and AI agents. Our technology allows partners to move, store, and use stablecoins instantly and compliantly through global payment cards, rewards, on/offramps, wallets, and cross-border rails. As both a Visa and Mastercard Principal Member, Rain issues cards that work at more than 175 million merchant locations in over 220 countries and territories. Built natively for stablecoins and trusted by more than 100 organizations worldwide, Rain delivers secure, scalable infrastructure that makes money move freely and instantly around the world. You will have the opportunity to deliver massive impact at a hypergrowth company backed by some of the top investors in fintech, crypto, and SaaS. In January 2026, we closed a $250M Series C led by ICONIQ, valuing Rain at $1.95B, with Sapphire Ventures, Norwest, Galaxy Ventures, Dragonfly, Bessemer Venture Partners, FirstMark, Lightspeed, and Endeavor Catalyst also participating. If you're curious, bold, and excited to help shape a borderless financial future, we'd love to talk. Our Ethos We believe in an open and flat structure. You will be able to grow into the role that most aligns with your goals. Our team members at all levels have the freedom to explore ideas and impact the roadmap and vision of our company. About the role Rain builds payments and stablecoin infrastructure. Finance, Risk, Compliance, and our partners rely on data from card transactions, settlements, ledger entries, and onchain transfers. We’re looking for a Data Engineer with batch and streaming experience to bring this data into our warehouse and keep it correct. You’ll build scalable, idempotent pipelines that handle late and duplicate data and alert us when something breaks. At Rain, everyone models data, writes production-quality code, and automates their own workflows. We use AI to speed up repetitive work, but we own and check the results. In this role, you’ll automate ingestion and quality checks so new sources come online quickly and stay trustworthy. What you’ll do - Build scalable batch and streaming pipelines, choosing the right approach for latency, cost, and correctness. - Process events near real time, handling deduplication, windowing, and late or out-of-order events. - Own batch ingestion from databases (CDC), files (SFTP and GCS), APIs, and onchain sources. - Build incremental dbt models that handle late data, duplicates, CDC deletes, and backfills; reconcile streaming and batch outputs. - Orchestrate pipelines in Dagster with sensors, schedules, and alerts. - Add freshness, volume, and reconciliation checks to every source, and fix root causes. - Work with backend pods on event and table contracts to prevent downstream breakage. - Partner with Analytics Engineers, Data Scientists, and Risk on their data needs, including ML features. What you’ll need - At least 3 years building production data solutions, including owning both batch and streaming pipelines end to end, from source to warehouse. - Experience designing data solutions at scale. - Strong SQL and Python / Rust, plus experience with a cloud data warehouse such as BigQuery, ClickHouse, or Snowflake and an understanding of their trade-offs. - Streaming experience with tools such as Pub/Sub, Kafka, Dataflow/Beam, or Flink. - Batch experience with dbt and an orchestrator such as Dagster or Airflow. - Experience with production data quality testing, monitoring, and reconciliation. - Ability to debug across the source database, pipeline, warehouse, and report. - Ability to use AI tools to learn and work faster while checking their output. Nice to have - Building offline and real-time ML feature pipelines, such as fraud or risk features. - Working with payments, banking, or blockchain data, including EVM or Solana events and indexers. - Using CDC and ingestion tools such as Datastream, Debezium, Meltano, Fivetran, or Airbyte. - Experience with GCP. Things that enable a fulfilling, healthy and happy experience at Rain: Flexible time off 🌴 Unlimited vacation can be daunting, so we require Rainmakers to take 10 days minimum for themselves. Flexible working ☕ We support a flexible workplace – work from home, come into an office, or both. We want everyone to work in an environment where they're their most confident and productive selves. New Rainmakers receive a stipend to set up a comfortable home workspace. Easy to access benefits 🧠 For US Rainmakers, we cover 95% of your health, dental, and vision plan costs and 90% for your dependents, plus a 100% company-subsidized life insurance plan. Retirement goals💡 Plan for the future with confidence. We offer a 401(k) with a 4% company match. Equity plan 📦 Every Rainmaker gets an equity option plan so we all benefit from our success. Health and Wellness 📚 High performance begins from within. Rainmakers receive a monthly stipend to be used for eligible health and wellness spending like gym memberships/fitness classes, massages, acupuncture - whatever recharges you! In-office meals 🍜 Rainmakers working from the office enjoy lunch and dinner on us, covered with a DoorDash credit. Team summits ✨ Summits play an important role at Rain. Time together helps us build relationships and a common destiny. Expect team and company offsites, both domestic and international.

Location: San Juan, Puerto Rico; New York, NY; District of Columbia; Europe | Spain Workplace: OnSite About the Company Rain is the global stablecoin payments platform for enterprises, neobanks, platforms, developers, and AI agents. Our technology allows partners to move, store, and use stablecoins instantly and compliantly through global payment cards, rewards, on/offramps, wallets, and cross-border rails. As both a Visa and Mastercard Principal Member, Rain issues cards that work at more than 175 million merchant locations in over 220 countries and territories. Built natively for stablecoins and trusted by more than 100 organizations worldwide, Rain delivers secure, scalable infrastructure that makes money move freely and instantly around the world. You will have the opportunity to deliver massive impact at a hypergrowth company backed by some of the top investors in fintech, crypto, and SaaS. In January 2026, we closed a $250M Series C led by ICONIQ, valuing Rain at $1.95B, with Sapphire Ventures, Norwest, Galaxy Ventures, Dragonfly, Bessemer Venture Partners, FirstMark, Lightspeed, and Endeavor Catalyst also participating. If you're curious, bold, and excited to help shape a borderless financial future, we'd love to talk. Our Ethos We believe in an open and flat structure. You will be able to grow into the role that most aligns with your goals. Our team members at all levels have the freedom to explore ideas and impact the roadmap and vision of our company. About the role Rain is building payments and stablecoin infrastructure, and our data platform holds some of the most sensitive data a company can have: cardholder, ledger, and partner data across many countries. We’re looking for a Data Platform Engineer who has built and run data platforms for both batch and streaming to own the platform our data team and engineering pods depend on: the warehouse, streaming infrastructure, orchestration, BI, ML platform, access controls, and the tooling that makes all of it safe, reliable, and cost-efficient. Your product is the platform, and your customers are the engineers, analysts, and scientists who build on it. You’ll own the platform layer: the data and ML platform, the infrastructure for ingestion and processing in batch and streaming, data governance and security compliance, the warehouse, and the analytics and BI tooling. Data Engineers, Analytics Engineers, Data Scientists, and engineering pods build on it to run experiments, operate reliably, and turn data into decisions. At Rain, we’re builders who use AI to automate repetitive work. That changes what data roles look like: where a data scientist used to only analyze and an analyst used to only build dashboards, here everyone models data, writes production-quality code, and automates their own workflows. You own the result, including checking what the AI produces. For this role, that means automating access, deploys, and governance so the platform runs itself and stays compliant. What you’ll do - Design and build data platforms at scale for both batch and streaming, choosing storage, compute, and processing based on latency, cost, and reliability needs. - Own the infrastructure for BigQuery, streaming (such as Pub/Sub or Kafka, and Dataflow or Flink), Dagster+, and Lightdash as code, including environments, CI/CD, and deploys. - Own access and governance: IAM, policy tags, PII contracts, masked and no-PII access paths, and audit logs, across tables and streams. - Provide ML platform capabilities: feature pipelines, training data, and serving real-time features to fraud and risk models. - Build platform tooling: templates, CI checks, schema registry and data contracts, catalog, and lineage. - Monitor platform reliability, latency, and cost, and fix what is slow or expensive. - Design how backend events and CDC land in the warehouse, and advise backend pods on event design. - Respond to platform incidents and access requests, and automate the repetitive ones away. What you’ll need - At least 4 years building and running data platforms in production on a cloud provider such as GCP, AWS, or Azure, in both batch and streaming: you have operated infrastructure for each, not only one. - Experience designing data platforms at scale: storage and compute choices, multi-team access, reliability, and cost. - Experience operating cloud data warehouses and real-time stores such as BigQuery and ClickHouse. - Experience operating streaming infrastructure such as Pub/Sub, Kafka, Dataflow, or Flink, including retention, schema evolution, replay, and lag monitoring. - Experience operating batch infrastructure: orchestration (Dagster or Airflow), CI/CD, and environments. - Infrastructure as code with Terraform. - Experience with access control and data governance, such as IAM, column- or row-level security, and PII handling. - Strong Python and SQL. - Proficiency using AI tools to learn new skills and accelerate your work, while taking responsibility for checking the output. Nice to have - ML infrastructure experience, such as feature stores, Vertex AI, or model serving. - Experience in regulated environments such as payments, banking, PCI, or SOC 2. - Experience with data catalogs and lineage tools. - Experience with cost optimization in BigQuery, ClickHouse, or Snowflake. - Experience with CDC tools such as Datastream or Debezium. Things that enable a fulfilling, healthy and happy experience at Rain: Flexible time off 🌴 Unlimited vacation can be daunting, so we require Rainmakers to take 10 days minimum for themselves. Flexible working ☕ We support a flexible workplace – work from home, come into an office, or both. We want everyone to work in an environment where they're their most confident and productive selves. New Rainmakers receive a stipend to set up a comfortable home workspace. Easy to access benefits 🧠 For US Rainmakers, we cover 95% of your health, dental, and vision plan costs and 90% for your dependents, plus a 100% company-subsidized life insurance plan. Retirement goals💡 Plan for the future with confidence. We offer a 401(k) with a 4% company match. Equity plan 📦 Every Rainmaker gets an equity option plan so we all benefit from our success. Health and Wellness 📚 High performance begins from within. Rainmakers receive a monthly stipend to be used for eligible health and wellness spending like gym memberships/fitness classes, massages, acupuncture - whatever recharges you! In-office meals 🍜 Rainmakers working from the office enjoy lunch and dinner on us, covered with a DoorDash credit. Team summits ✨ Summits play an important role at Rain. Time together helps us build relationships and a common destiny. Expect team and company offsites, both domestic and international.

Workplace: remote Requirements Min. 3 years of practical experience in carrying out complex data analyses with Python, including data preparation, data validation, and target-audience-appropriate preparation of the results Practical experience with SQL for querying, preparing, and integrating structured data (4 reference projects) Practical experience in processing infrastructure and geodata (1 reference project) Certification in a cloud-based data management solution (Databricks, Snowflake, Fabric) Knowledge of German at C1 level or above Knowledge of English at B2 level or above Nice to have In-depth knowledge of coordinate systems and coordinate transformations, demonstrated by a completed degree in mathematics or physics Experience with OCR methods for the automated extraction of structured data (1 reference project) Responsibilities Analysis of the provided geodata inventories, mast plates, and existing BIM data structures Analysis of the existing Python script as well as the current IFC model structure Assessment of additional infrastructure objects regarding their suitability for data enrichment and BIM modeling Technical coordination of the attributes and modeling parameters to be used Development and further development of data pipelines for automated data ingestion and data processing Integration of information from various data sources Processing of PDF documents including OCR-supported text recognition and table evaluation Transformation, validation, and cleansing of structured and unstructured data Design and optimization of suitable data models for storing and further processing the data Extraction of relevant information from mast plates Prototypical processing of the mast plates, including PDF/image preprocessing, OCR, table recognition, and plausibility checking Extraction of the agreed information and assignment to the matching objects in the geodata Flagging of unrecognized, ambiguous, or implausible information and provision of the enriched data in machine-readable form Ensuring traceable data quality and data integrity Development of methods for automated plausibility checking of the results Extension and adaptation of existing script components for automated IFC model generation Support in the parametric modeling of overhead line masts and other coordinated infrastructure components Integration of enriched data into existing BIM structures Creation and review of IFC output files with regard to technical validity and technical plausibility Conducting quality checks at the data, process, and modeling level Documentation of data structures, transformation logic, and validation rules Documentation of unresolvable data conflicts and special cases Creation of traceable technical documentation of the developed solution We offer Projects for such clients as PayPal, Wargaming, Xerox, Philips, Adidas and Toyota Competitive compensation that depends on your qualification and skills Career development system with clear skill qualifications Flexible working hours aligned to your schedule Options to work remotely Corporate medical insurance covering services of private and public medical centers English courses online Corporate parties and events for employees and their children Internal conferences, workshops and meetups for learning and experience sharing Gym membership compensation 5 days of paid sick leave per year with no obligation to submit a sick-leave certificate Any questions? Anna hh@itransition.com

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

Location: Paris, France Workplace: Hybrid We’re a team of experts pushing the limits of what’s possible, united by our common goal to unlock true freedom through digital ownership, making technology accessible for all. We believe in a world where users, creators and enterprises manage their value with ownership and freedom. Our curiosity drives us to innovate, empowering individuals on a global scale. We believe change is constant and our team moves forward as one, with a culture of problem-solving where every employee is empowered and supported to challenge tradition and create solutions. Our mission is simple: to make self-custody accessible and give people the keys to their own financial futures. If you want to make a true impact, we want you to join us at Ledger. At Ledger, we’re proud to be the global platform for digital assets and Web3, with over 20% of the world’s crypto assets secured through our Ledger devices. With our headquarters in Paris, and offices in Vierzon, Grenoble, Montpellier, London, Portland, Geneva, Zurich and Central Singapore, we have a team of around 600 professionals developing a variety of products and services to enable individuals and companies to securely buy, store, swap, grow and manage crypto assets – including the Ledger hardware wallets line with more than 7.5 millions units already sold in 200 countries.   The team: To support our continued growth, we’re hiring an Engineering Manager to join the Wallet Experience business unit. You’ll work directly with the Engineering Director and lead high-performing teams that deliver core features across our top-rated native applications. This is a hands-on leadership role: you’ll shape the product experience millions of users rely on every day and help define how a modern engineering team builds with AI. What you’ll be doing: Management - Lead your team and meet regularly with your direct reports to give frequent, actionable feedback. - Drive recruitment, performance assessments, training and career development for your team. - Help each person set clear goals and improve their practices, supporting their growth and our retention goals. - Build AI fluency in your team: coach engineers to use AI tools well, share what works, and make AI-assisted development part of how they grow. Tech - Drive projects end to end, applying best-in-class engineering practices: clean architecture, strong test coverage, code review and observability. - Lead the adoption of AI across the development lifecycle, from coding agents and AI-assisted reviews to test generation and automated workflows. Set clear guardrails for quality, security and privacy. - Participate in sprint delivery, monitor engineering metrics, and be accountable for the quality of what ships. - Bring forward innovative solutions and encourage innovative thinking from others, including new ways AI can remove toil and speed up delivery. Delivery & Impact - Work closely with Delivery Managers, Product Owners and Designers to assess feasibility, give technical recommendations, and help with estimation and planning. - Focus the team on outcomes, not output: tie work to user and business impact, and measure it. - Delegate effectively so work is completed as specified, without micromanaging. - Set clear expectations and make sure practices are followed to meet Ledger’s quality bar. Leadership & Communication - Bring a positive “can do” attitude and be a role model for others. - Communicate proactively and be a well-identified point of contact for other teams. - Track progress, risks and issues, and communicate them to the right stakeholders. - Champion a culture of continuous improvement, where the team regularly reviews and raises its own standards. What we’re looking for: - 3+ years of experience managing engineering teams. - Strong experience in React.js development, tools and libraries. - Good knowledge of browsers and web APIs. - Knowledge of React Native is appreciated. - Hands-on experience using AI tools in software development (e.g. coding assistants or agents), and a clear view of how to adopt them responsibly at team level. - A track record of raising engineering standards and delivering measurable impact. - Experience with CI/CD and building deployment pipelines is a plus. - Knowledge of UI/UX practices in software development. - A team-player mindset, excellent interpersonal skills and the ability to work cross-functionally. - Enthusiasm for hiring and developing people. - Willingness to build skills in the Web3 industry. At Ledger, we are dedicated to continually investing in our employees which is why we offer more than just salaries; we provide comprehensive compensation packages that include a wide range of benefits. Here are some of the benefits you can look forward to: - Flexible work options - Our hybrid policy allows employees to work from home up to 3 times per week - Health & Wellness support - Health and Life Insurance. - Financial growth opportunities - Employees can become shareholders in Ledger as well as other financial benefits depending on your country of work. - Commuter allowance - Ledger offers a commuter allowance to contribute to your preferred means of transportation. - Learning & Development - A comprehensive suite of training solutions providing a personalised learning experience for every employee. For regionally specific benefits, your Talent Acquisition contact will be able to provide you with more information.   We’re committed to building an inclusive hiring process. If you need any adjustments or accommodations, just let us know, we’ll do our best to support you.

Location: San Juan, Puerto Rico; New York, NY; District of Columbia; Europe | Spain Workplace: OnSite About the Company Rain is the global stablecoin payments platform for enterprises, neobanks, platforms, developers, and AI agents. Our technology allows partners to move, store, and use stablecoins instantly and compliantly through global payment cards, rewards, on/offramps, wallets, and cross-border rails. As both a Visa and Mastercard Principal Member, Rain issues cards that work at more than 175 million merchant locations in over 220 countries and territories. Built natively for stablecoins and trusted by more than 100 organizations worldwide, Rain delivers secure, scalable infrastructure that makes money move freely and instantly around the world. You will have the opportunity to deliver massive impact at a hypergrowth company backed by some of the top investors in fintech, crypto, and SaaS. In January 2026, we closed a $250M Series C led by ICONIQ, valuing Rain at $1.95B, with Sapphire Ventures, Norwest, Galaxy Ventures, Dragonfly, Bessemer Venture Partners, FirstMark, Lightspeed, and Endeavor Catalyst also participating. If you're curious, bold, and excited to help shape a borderless financial future, we'd love to talk. Our Ethos We believe in an open and flat structure. You will be able to grow into the role that most aligns with your goals. Our team members at all levels have the freedom to explore ideas and impact the roadmap and vision of our company. About the role Rain is building payments and stablecoin infrastructure. Every transaction generates data across our system. We’re looking for an Analytics Engineer, Finance to own the data layer Finance runs on: model it, reconcile it against source systems, test it, automate it, and make it self-serve. You’ll partner with Finance and Accounting and the data team to make sure every number we report ties back to its source. We’re looking for someone with high agency and urgency who owns problems end to end, digs into discrepancies until the numbers tie out, and communicates clearly when something is uncertain. We care less about where you trained and more about what you’ve built. At Rain, we’re builders who use AI to automate repetitive work. That changes what data roles look like: where a data scientist used to only analyze and an analyst used to only build dashboards, here everyone models data, writes production-quality code, and automates their own workflows. You own the result, including checking what the AI produces. For this role, that means building trusted models and automating the reconciliations and reporting Finance depends on. What you’ll do - Build and own models for Finance: ledger, settlements, card transactions, invoicing, fees, and collateral, with clear grain and consistent definitions. - Build and run reconciliations between internal systems and external sources (card networks, processors, banks, partners); investigate breaks and fix the root cause. - Support month-end close and audit readiness with validated, documented outputs Finance can trust. - Automate recurring Finance reporting and deliveries (scheduled reports, exports, and alerts) so close and partner reporting run without manual work. - Add data quality tests and alerts so issues are caught before Finance sees them. - Partner with Finance to define metrics such as revenue, costs, volume, and contribution profit, and make them reusable across teams. - Build self-serve reporting so Finance can answer recurring questions independently. - Work with data and platform engineers on ingestion and source changes, following Rain’s data standards and access policies. What you’ll need - At least 3 years of experience in analytics engineering, software engineering, or analytics with a strong data modeling focus. - Proficiency using AI tools to learn new skills, navigate unfamiliar domains, and accelerate your work, while taking responsibility for checking the output. - Strong SQL in a modern data warehouse such as BigQuery, Snowflake, or Redshift, including complex joins, window functions, and incremental logic. - Hands-on dbt experience: data modeling, testing, documentation, version control, and code review. - Experience reconciling data across systems and explaining differences down to the row. - Familiarity with financial metrics and processes, such as revenue and costs, financial statements, reconciliation, or monthly close. - Strong attention to detail: you validate results against source systems and explain assumptions, tradeoffs, and uncertainty. - Ability to work independently and collaboratively in a fast-moving startup, communicating clearly with technical and nontechnical partners. Nice to have - Experience in payments, card issuing, fintech, or stablecoins, such as settlement files, ledgers, or interchange. - Experience with BI as code, such as Lightdash, Looker, or Omni. - Python or another scripting language for automation and data checks. - Experience working directly with Finance or Accounting teams. - Experience with orchestration and ingestion tools such as Dagster, Airflow, Meltano, Fivetran, or Datastream. Things that enable a fulfilling, healthy and happy experience at Rain: Flexible time off 🌴 Unlimited vacation can be daunting, so we require Rainmakers to take 10 days minimum for themselves. Flexible working ☕ We support a flexible workplace – work from home, come into an office, or both. We want everyone to work in an environment where they're their most confident and productive selves. New Rainmakers receive a stipend to set up a comfortable home workspace. Easy to access benefits 🧠 For US Rainmakers, we cover 95% of your health, dental, and vision plan costs and 90% for your dependents, plus a 100% company-subsidized life insurance plan. Retirement goals💡 Plan for the future with confidence. We offer a 401(k) with a 4% company match. Equity plan 📦 Every Rainmaker gets an equity option plan so we all benefit from our success. Health and Wellness 📚 High performance begins from within. Rainmakers receive a monthly stipend to be used for eligible health and wellness spending like gym memberships/fitness classes, massages, acupuncture - whatever recharges you! In-office meals 🍜 Rainmakers working from the office enjoy lunch and dinner on us, covered with a DoorDash credit. Team summits ✨ Summits play an important role at Rain. Time together helps us build relationships and a common destiny. Expect team and company offsites, both domestic and international.

About the Role As Engineering Manager for the Subledger team, you will own the system of record for every financial transaction at Hopper, driving accurate reporting and reconciliation across all money movements in our ecosystem. • От 8 лет в разработке ПО, включая от 2 лет управления инженерами;

Location: Remote Ireland; Remote, Austria; Remote, France; Remote, Germany; Remote, Netherlands; Remote, Spain; Remote, United Kingdom GitLab is the intelligent orchestration platform for DevSecOps. GitLab enables organizations to increase developer productivity, improve operational efficiency, reduce security and compliance risk, and accelerate digital transformation. More than 50 million registered users and more than 50% of the Fortune 100* trust GitLab to ship better, more secure software faster. The same principles built into our products are reflected in how our team works: we embrace AI as a core productivity multiplier, with all team members expected to incorporate AI into their daily workflows to drive efficiency, innovation, and impact. GitLab is where careers accelerate, innovation flourishes, and every voice is valued. Our high-performance culture is driven by our values and continuous knowledge exchange, enabling our team members to reach their full potential while collaborating with industry leaders to solve complex problems. Co-create the future with us as we build technology that transforms how the world develops software. * Fortune 500® is a registered trademark of Fortune Media IP Limited, used under license. Claim based on GitLab data. Fortune 100 refers to the top 20% ranked companies in the 2025 Fortune 500 list, published in June 2025. Fortune and Fortune Media IP Limited are not affiliated with, and do not endorse products or services of GitLab. An overview of this role GitLab’s Forward Deployed Engineering team is a field-situated product engineering function focused on strategic customer outcomes, artificial intelligence (AI) adoption, and direct contributions to GitLab’s product. This isn't a traditional field engineering, solutions architecture, or professional services role. As a Staff Forward Deployed Engineer, you'll work closely with customers while maintaining the technical standards and contribution discipline of a Staff-level product engineer. You'll work where strategic customer urgency, complex technical ambiguity, and GitLab product execution intersect. You'll partner directly with strategic customers and teams across Product, Engineering, Support, Site Reliability Engineering (SRE), Solutions Architecture (SA), Customer Success Architecture (CSA), Customer Success Management (CSM), and Professional Services. You'll diagnose and resolve high-impact adoption blockers, contribute production-quality fixes and features, and turn what you learn into repeatable patterns for GitLab and its customers. Why this role exists Strategic customers sometimes encounter challenges that are too broad, deep, urgent, or cross-functional for any single team to own from start to finish. These challenges may involve product gaps, performance degradation, scale bottlenecks, AI governance, adoption blockers, architectural constraints, Git usage patterns, continuous integration and continuous delivery (CI/CD) complexity, application programming interface (API) automation, security requirements, or operational instability. Forward Deployed Engineering provides technical stewardship in these moments. The team doesn't simply observe, report, or escalate. You'll engage directly, diagnose the underlying technical problem, collaborate with the right teams, contribute code or technical solutions where appropriate, and transition the outcome into normal ownership once the customer is stable and the learning can be reused. What you’ll do Product contribution Contribute production-quality code to GitLab’s product and adjacent systems. Develop bug fixes, features, integrations, tooling, and performance improvements when strategic customer outcomes require direct engineering work. Work within GitLab’s product and engineering processes, including issue discovery, merge request creation, testing, review, documentation, and maintainability. Partner with Product and Engineering so customer-driven solutions support long-term product direction rather than becoming one-off customizations. Turn blockers discovered in the field into durable product improvements, reference architectures, documentation, and repeatable implementation patterns. Strategic technical stewardship Engage with strategically important customers when adoption, retention, executive confidence, or public trust is materially at risk. Diagnose complex problems across GitLab product behavior, customer architecture, Git internals, CI/CD workflows, API usage, observability, infrastructure, networking, and cloud environments. Coordinate across Product, Engineering, SRE, Support, SA, CSA, CSM, Professional Services, and customer technical teams to drive resolution from discovery through handoff. Provide recommendations grounded in data, traces, and systems analysis to improve GitLab usage, workflows, automation, and architecture. Create durable outcomes without establishing permanent dependency on Forward Deployed Engineering. AI adoption and agentic software development lifecycle Drive governed, measurable, and production-oriented adoption of GitLab Duo Agent Platform with strategic customers. Identify and resolve AI adoption blockers involving governance, usage controls, auditability, security, compliance, operating models, workflow design, and integrations. Develop customer-safe adoption patterns for the agentic software development lifecycle (SDLC), AI-assisted development, intent-based review, AI governance, and human-in-the-loop software delivery. Partner with Product, Engineering, Field, and Customer Success teams to turn AI adoption learning into reusable guidance and product improvements. Thought leadership Create technical narratives grounded in customer experience and product reality that can help shape the industry’s approach. Contribute to Well-Architected Frameworks, whitepapers, webinars, reference architectures, technical strategy memos, and adoption frameworks. Guide ideas through discovery, formulation, validation, amplification, and adoption across GitLab. Partner with SA, CSA, Product, Engineering, Marketing, Product Marketing Management, CSM, Support, and executive stakeholders to make technical thought leadership a shared company capability. What you’ll bring Staff-level experience building, operating, or improving complex production software systems. Experience contributing to large, mature product codebases with high standards for testing, maintainability, performance, and security. Strong Ruby on Rails or Go experience and the ability to contribute directly to GitLab’s product and adjacent systems. Experience with AI-assisted software development, agentic workflows, AI governance, model evaluation, context management, prompt and workflow design, or AI adoption in enterprise environments. Experience diagnosing complex system behavior across application code, data stores, infrastructure, observability, networking, APIs, and customer usage patterns. Deep knowledge of modern DevSecOps workflows, including Git, merge requests, CI/CD, security controls, compliance needs, and enterprise software delivery. Experience working with strategic customers or senior technical stakeholders in ambiguous, high-impact situations. The ability to coordinate across product, engineering, field, and customer teams without formal authority. Strong written communication and sound judgment, including the ability to create technical narratives and distinguish reusable product work from customer-specific customization. Strongly preferred qualifications Direct experience with GitLab as a platform, including GitLab.com, GitLab Dedicated, or GitLab Self-Managed. Production experience with Ruby on Rails, Go, PostgreSQL, Redis, Sidekiq, Vue, TypeScript, Kubernetes, Docker, Linux, or observability tools. Deep familiarity with Git internals, repository performance, large-scale Git usage patterns, or GitLab Runner behavior. Experience creating technical thought-leadership materials, such as whitepapers, technical talks, architecture frameworks, requests for comments, public technical writing, or internal strategy documents. A background in performance engineering, site reliability engineering, platform engineering, infrastructure engineering, or distributed systems. Experience working in open source or highly asynchronous engineering environments. Technologies and domains Directly relevant technologies and domains include: Ruby on Rails Go AI-assisted development and the agentic SDLC PostgreSQL Redis and Sidekiq Git internals GitLab CI/CD and Runner architecture Kubernetes and containers Terraform Linux, networking, observability, and performance analysis Vue and TypeScript APIs and automation DevSecOps governance, security, and compliance What this role is not This role isn't: A generic field engineering role. A proof-of-concept factory. A replacement for Solutions Architecture, Customer Success Architecture, Customer Success Management, Professional Services, Support, SRE, Product, or Engineering. A customer-specific customization function. Unbilled Professional Services. A requirements-gathering role that only escalates to Product. An AI evangelism role disconnected from product execution. Success measures Success in this role may include: Stabilizing strategic customer adoption or retention risks. Fixing product bugs or contributing features. Removing AI adoption blockers for strategic accounts. Creating reusable implementation patterns and transitioning them to long-term owners. Producing or enabling Well-Architected Frameworks, whitepapers, webinars, or technical narratives. Resolving cross-functional customer challenges without creating permanent dependency on Forward Deployed Engineering. Equipping Product, Engineering, Support, CSA, CSM, Professional Services, and SA teams with better guidance based on learning from the field. Increasing adoption and effective use of GitLab capabilities, especially GitLab Duo and GitLab Duo Agent Platform, in targeted accounts. About the team The Forward Deployed Engineering team connects strategic customer needs with GitLab product execution. The team works directly with customers and across GitLab to resolve complex adoption challenges, contribute durable product improvements, and create reusable technical guidance. Its goal is to stabilize urgent situations, improve the product, and transition ongoing ownership to the appropriate team rather than create a permanent dependency. Ideal candidate profile You're a Staff-level product or platform engineer who can operate in the field without becoming field-only. You're comfortable reading traces, writing production code, debugging architecture, discussing AI governance, leading a customer technical session, opening a merge request, outlining a whitepaper, and coordinating across teams to achieve a strategic customer outcome. You're not simply a strong communicator with technical awareness. You're a credible engineer with the judgment, presence, and narrative ability to turn customer reality into product contributions, adoption guidance, and company-level thought leadership. We welcome candidates with different backgrounds and levels of experience. Many successful candidates don't meet every qualification. If you're excited about this role, we encourage you to apply and let our recruiting team assess your application. How GitLab Supports Full-Time Employees Benefits to support your health, finances, and well-being Flexible Paid Time Off Team Member Resource Groups Equity Compensation & Employee Stock Purchase Plan Growth and Development Fund Parental Leave Please note that we welcome interest from candidates with varying levels of experience; many successful candidates do not meet every single requirement. Additionally, studies have shown that people from underrepresented groups are less likely to apply to a job unless they meet every single qualification. If you're excited about this role, please apply and allow our recruiters to assess your application. Country Hiring Guidelines: GitLab hires new team members in countries around the world. All of our roles are remote, however some roles may carry specific location-based eligibility requirements. Our Talent Acquisition team can help answer any questions about location after starting the recruiting process. Privacy Policy: Please review our Recruitment Privacy Policy. Your privacy is important to us. GitLab is proud to be an equal opportunity workplace and is an affirmative action employer. GitLab’s policies and practices relating to recruitment, employment, career development and advancement, promotion, and retirement are based solely on merit, regardless of race, color, religion, ancestry, sex (including pregnancy, lactation, sexual orientation, gender identity, or gender expression), national origin, age, citizenship, marital status, mental or physical disability, genetic information (including family medical history), discharge status from the military, protected veteran status (which includes disabled veterans, recently separated veterans, active duty wartime or campaign badge veterans, and Armed Forces service medal veterans), or any other basis protected by law. GitLab will not tolerate discrimination or harassment based on any of these characteristics. See also GitLab’s EEO Policy and EEO is the Law . If you have a disability or special need that requires accommodation , please let us know during the recruiting process .

• Разработка ML-моделей для автоматической модерации видеоконтента: классификация контента, выявление запрещённых материалов, определение нарушений правил платформы. • Разработка моделей на основе Computer Vision и Deep Learning для анализа видео и изображений.

Location: New York City, NY Reddit is a community of communities. It’s built on shared interests, passion, and trust, and is home to the most open and authentic conversations on the internet. Every day, Reddit users submit, vote, and comment on the topics they care most about. With 100,000+ active communities and approximately 130 million daily active unique visitors, Reddit is one of the internet’s largest sources of information. For more information, visit www.redditinc.com . Location: Reddit has a flexible-first workforce. Don't live near our New York office? No worries: you can work remotely from anywhere on the US East Coast (Eastern Time). The Ads Foundational Representations (AFR) team develops signals and representations of Reddit’s core entities (ads, posts, users, and so on), capturing the semantic, contextual, and behavioral information that Reddit Ads needs. We work on building embeddings to understand content and users' interests based on the content they engage with. Our team has the potential to highlight one of Reddit's biggest differentiators: genuinely curated, high-quality, extremely relevant, and daily updated organic content. We are a Machine Learning/Data heavy team with a focus on the following areas: Multimodal & Content Embeddings - Make sense of organic (posts, comments, subreddits) and promoted (ads, shopping products, their landing pages) text and media content by embedding them into a shared space. Contextual and Behavioral Relevance - Working with Product & Data Science, establishing definitions of what ads are relevant to users and the content we show them next to, building metrics and fine-tuning embeddings to better reflect relevance. Knowledge Graph Embeddings - Building representations for the Knowledge graph entities, e.g., intellectual properties/brands, to be used for high-precision targeting & business insights. User Intent Modeling - Leveraging various techniques to introduce user representations based on the content they interact with: batch & real-time sequence modeling, LLM summarization, etc. LLM-based Representations - Leveraging LLMs, VLMs, and foundational models to build complex representations of Reddit entities that improve ranking outcomes The signals and features we create become a key piece in the Ads Delivery funnel, from targeting to the auction, as well as the Business Insights product and other advertiser-facing products such as Creative generation and optimization. As a Staff ML Engineer , you’ll be in charge of setting the technical direction of multiple pillars the team owns. You will lead cross-functional ML projects end to end - from high-level business gap analysis to engineering execution. Roughly 50% of your time will be spent on technical leadership and mentorship (driving strategy & designs, cross-functional collaboration, raising the quality bar), another 50% being individual hands-on work (data analysis & engineering, modeling & automation). Responsibilities Providing technical leadership and mentorship to MLEs in the team: driving designs & their review, establishing best practices in analysis, modeling and engineering, keeping the bar high. Working closely with team/org leadership developing technical strategy for content-based embeddings & relevance for Ads. Developing new or iterating on existing embedding models for advertising use cases, ranging from aggregation pipelines to two-tower architectures and sequence models. Working with local and 3rd-party LLMs/VLMs: extract representations, develop evaluation methodologies, prompt tune and fine-tune large models to build state-of-the-art embeddings. Building data processing and inference pipelines for the models we develop. Qualitative and quantitative evaluation of the various features we develop, end-to-end experimentation from internal benchmarks to downstream recommender system offline metrics to online experiments. Ensuring the reliability, scalability, and performance of the ML systems by writing automated tests, monitoring performance, and implementing best practices for model management. Participating in modeling and coding reviews: You will review work by other team members and provide feedback to ensure that it meets the team's standards for quality and performance. Collaborating with cross-functional teams to understand business requirements and translate them into technical solutions. Required Qualifications: 7+ years of hands-on experience with the full lifecycle of designing, training, evaluating, testing, and deploying industry-level models. Demonstrated Staff-level technical leadership: mentoring engineers, driving standards and bar raising, leading complex cross-functional projects: from requirements, design to cross-team/functional alignment and execution without direct people-management authority. Excellent communication skills, with the ability to translate complex technical concepts to different audiences, both verbally and in writing. Strong track record of working on content rich NLP/CV problems at scale, and using embeddings as a tool to solve them. Established data-driven approach for ML system development. Excitement about working with data and readiness to look behind the metric numbers. Familiarity with the Ads domain and/or Search/Recommender systems. Experience with mainstream DL frameworks: PyTorch or TensorFlow. Preferred Qualifications: Experience with our stack (Python, Pytorch, Airflow, BigQuery, Ray, k8s, kafka, GCP) Tech leadership experience: mentoring junior engineers and leading complex projects. Hands-on experience with using/fine-tuning/building LLMs. Benefits: Comprehensive Healthcare Benefits and Income Replacement Programs 401k with Employer Match Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support Family Planning Support Gender-Affirming Care Mental Health & Coaching Benefits Flexible Vacation & Paid Volunteer Time Off Generous Paid Parental Leave Pay Transparency: This job posting may span more than one career level. In addition to base salary, this job is eligible to receive equity in the form of restricted stock units, and depending on the position offered, it may also be eligible to receive a commission. Additionally, Reddit offers a wide range of benefits to U.S.-based employees, including medical, dental, and vision insurance, 401(k) program with employer match, generous time off for vacation, and parental leave. To learn more, please visit https://www.redditinc.com/careers/ . To provide greater transparency to candidates, we share base salary ranges for all US-based job postings regardless of state. We set standard base pay ranges for all roles based on function, level, and country location, benchmarked against similar stage growth companies. Final offer amounts are determined by multiple factors including, skills, depth of work experience and relevant licenses/credentials, and may vary from the amounts listed below. The base salary range for this position is: $230,000 — $322,000 USD In select roles and locations, the interviews will be recorded, transcribed and summarized by artificial intelligence (AI). You will have the opportunity to opt out of recording, transcription and summarization prior to any scheduled interviews. During the interview, we will collect the following categories of personal information: Identifiers, Professional and Employment-Related Information, Sensory Information (audio/video recording), and any other categories of personal information you choose to share with us. We will use this information to evaluate your application for employment or an independent contractor role, as applicable.  We will not sell your personal information or disclose it to any third party for their marketing purposes.  We will delete any recording of your interview promptly after making a hiring decision.  For more information about how we will handle your personal information, including our retention of it, please refer to our Candidate Privacy Policy for Potential Employees and Contractors . Reddit is proud to be an equal opportunity employer, and is committed to building a workforce representative of the diverse communities we serve.  Reddit is committed to providing reasonable accommodations for qualified individuals with disabilities and disabled veterans in our job application procedures. If, due to a disability, you need an accommodation during the interview process, please let your recruiter know.

Ты ведёшь облачный голосовой конвейер: определение конца фразы, STT, LLM, TTS со сквозной задержкой 1-2 секунды (p50 и p90). ● Вести потоковый конвейер: barge-in, стриминг токенов в TTS, таймауты, fallback между провайдерами.

Location: London Qube Research & Technologies (QRT) is a global quantitative and systematic investment manager, operating in all liquid asset classes across the world. We are a technology and data driven group implementing a scientific approach to investing. Combining data, research, technology and trading expertise has shaped QRT’s collaborative mindset which enables us to solve the most complex challenges. QRT’s culture of innovation continuously drives our ambition to deliver high quality returns for our investors. We are looking for an experienced Trading Systems Software Engineer to help build the applications, platforms and engineering capabilities that support QRT’s global trading environment. This is a broad software engineering role spanning application development, distributed systems, cloud technology, deployment automation and production engineering. You will design and build software used by traders, quantitative researchers, operations teams and other engineers across the firm. Your Future Role within QRT Design, develop and maintain production-grade applications, services and APIs supporting QRT’s trading platform. Build shared platforms and reusable capabilities that can be adopted across multiple trading desks and engineering teams. Develop modern web applications and operational tools that improve visibility, control and efficiency across trading workflows. Design scalable distributed and event-driven systems that integrate with trading platforms, market data services and internal infrastructure. Build deployment tooling, release automation and self-service capabilities that make software easier and safer to deploy and operate. Develop automated workflows for data processing, operational tasks and engineering processes using technologies such as Apache Airflow. Partner with traders, quantitative researchers and operations teams to understand their needs and solve practical business problems. Design software for latency-sensitive environments, balancing performance with reliability, scalability and operational simplicity. Build observable and resilient systems with appropriate monitoring, alerting, recovery and failure-handling capabilities. Work alongside infrastructure, cloud and DevOps engineers to deploy and support software across global production environments. Contribute to technical design and architectural decisions, evaluating new technologies where they can deliver meaningful value. Take ownership of software throughout its lifecycle, from initial design and implementation through to production support and continuous improvement. Your present skillset At least five years of experience developing and supporting production-grade software. Strong programming and software engineering skills in one or more languages such as Python, Java, Go, Node.js or C++. Experience designing maintainable, testable and scalable applications and services. A strong understanding of software design principles, data structures, algorithms and engineering fundamentals. Experience building RESTful APIs, distributed systems or event-driven architectures. Exposure to containerisation, orchestration and delivery technologies such as Docker, Kubernetes and CI/CD pipelines. Experience with public cloud technologies, preferably AWS. An understanding of messaging technologies such as Kafka or RabbitMQ. Experience considering reliability, observability, performance and failure recovery when designing production systems. Strong analytical and problem-solving skills, with the ability to make sound engineering decisions and explain the associated trade-offs. The ability to work effectively with both technical and non-technical stakeholders. QRT is an equal opportunity employer. We welcome diversity as essential to our success. QRT empowers employees to work openly and respectfully to achieve collective success. In addition to professional achievement, we are offering initiatives and programs to enable employees to achieve a healthy work-life balance.

Задачи: — Работа на первой линии технической поддержки — Мониторинг, анализ и координация поступающих обращений от пользователей — Приём входящих заявок и первичное взаимодействие с разработчиками / DevOps — Взаимодействие со смежными подразделениями — Работа с системой контроля заявок JIRA — Развитие инструментов мониторинга и сопровождения — Отработка триггеров Zabbix — Техническая поддержка внутренних пользователей по всему спектру IT-вопросов — Проведение процедур первичной диагностики проблем — Участие в разборе и устранении инцидентов и аварий — Выполнение интересных технических задач для профессионального развития Требования: — Опыт работы в сфере IT от 2 лет — Опыт работы с Graylog или аналогами, Grafana / Kibana — Знание Linux и уверенная работа в командной строке — Уверенное знание и практический опыт работы с Zabbix — Знание принципов работы протоколов TCP / UDP — Знание систем виртуализации — Опыт работы с IPMI / iDRAC Будет преимуществом: — Высшее, неполное высшее или среднее образование в сфере информационных технологий — Знание MySQL, DNS, Nginx, Proxmox, iptables, Ceph, Exim, VoIP, Grafana — Администрирование Linux-систем на уровне продвинутого пользователя — Умение читать и анализировать системные логи — Знание Ansible на начальном уровне — Знание Winback и умение работать с IIS — Сдержанность, грамотная речь и стрессоустойчивость Компания помогает с релокацией в Сербию. Формат работы — офисный, график 4/4 по 12 часов (дневные и ночные смены).

Location: Paris; Linz; Munich; London; Berlin Workplace: Remote ABOUT MISTRAL Mistral provides full-stack AI solutions: from frontier models to developer tools, applications, and compute. We partner with enterprises tackling the hardest problems across high-stakes industries like finance, manufacturing, defense, healthcare, and the public sector, co-creating customized AI systems that they can run on their terms. We are a dynamic, collaborative team passionate about AI and its potential to transform society. Our diverse workforce thrives in competitive environments and is committed to driving innovation. Our teams are distributed between Europe, North America, Asia and the Middle East. We are creative, low-ego and team-spirited. The Role Mistral AI is looking for AI Scientists with deep machine learning expertise and hands-on engineering experience to expand what Mistral's agentic tools can do across the engineering lifecycle across CAE (CFD, FEA etc) and EDA/Semi.. Within AI4Engineering Science, your core work is building the pre and post-training data for Mistral's LLMs to reason about and execute real engineering tasks. Alongside this, you'll help shape the agent architectures and harness that let these models operate reliably inside multi-step engineering workflows, not just answer isolated questions. You'll work closely with domain experts across CAE and EDA to ground this work in how engineers actually work, and with the broader research team to translate that domain grounding into training signal and evaluation benchmarks that measure genuine task competence. What You Will Do - Design pretraining, SFT, and RL data for engineering tasks across CAD, CAE, and semiconductor/EDA - Define verifiers and evaluation criteria that capture what "correct" and "high-quality" actually mean for each engineering task, beyond surface-level plausibility - Design and improve agent architectures and harnesses: how models plan, call tools, recover from errors, and chain steps together across long-horizon engineering workflows - Build evaluation benchmarks and diagnostic tooling to identify where models fail on engineering tasks, and trace those failures back to gaps in data, reward design, or agent scaffolding - Collaborate with domain experts across CAEand EDA (and the science and solutions teams more broadly) to identify which engineering workflows are highest-value to target next - Contribute to Mistral's broader pre and post-training research, sharing findings and methodology across the science organization What We're Looking For - Fluent English with excellent communication skills, able to explain technical ML and engineering concepts to both engineering and non-technical audiences - Deep, hands-on machine learning expertise, particularly LLM development - Demonstrated experience running, debugging, and validating real engineering workflows in at least one of CAD, CAE, semiconductor simulation, or EDA. - You write clean, readable Python code and are comfortable in Linux/HPC environments - Self-directed, you don't need detailed roadmaps to make progress - Low-ego, collaborative, and eager to learn at the intersection of engineering and ML It would be great if you - Have experience building or fine-tuning agentic systems (tool use, multi-step planning, agent orchestration frameworks) - Have experience with reward modeling, RLHF/RLAIF/RLVR, or preference-based training - Have industrial or academic experience with CAE, or EDA tools (e.g. SolidWorks, CATIA, Fluent, Abaqus, LS-DYNA, STAR-CCM+, Cadence/Synopsys/Siemens EDA tools) - Have contributed to a large open-source or industry codebase - Have publications in engineering and ML venues (NeurIPS, ICLR, JFM, AIAA, etc.)   WHAT WE OFFER We offer a comprehensive benefits package designed to support your well-being, growth, and work-life balance. Benefits vary by country and may include healthcare coverage, parental leave, retirement plans, relocation support, wellness programs, meal and transportation allowances, and other location-specific perks. For the most up-to-date details on benefits available in your location, please refer to our Benefits page https://app.notion.com/p/mistralai/Benefits-at-Mistral-36e6ba59a7fe836b93dd01737fcc27ef?source=copy_link. PRIVACY POLICY Your privacy matters to us. You can learn more about how we handle your personal data in our Applicant Privacy Policy https://legal.mistral.ai/terms/applicant-privacy-policy.

Location: Москва Наша команда строит Единую агентскую платформу. Enterprise harness – это окружение: среда и набор инструментов, который обеспечивает безопасную и контролируемую работу агента, использование инструментов и навыков, контролирует права, память, контекст, лимиты, аудит и безопасность. В рамках агентской платформы вам предстоит разрабатывать и внедрять инструменты для окружения агентов. Мы адаптируем лучшие мировые практики, а также сотрудничаем с RnD-командами для пилотирования новых разработок на агентах Банка. Примеры направлений, которыми мы занимаемся: * SkillOpt: автоматическая эволюция навыков агентов на основе трейсов и оценки LLM-as-a-Judge; * Memory-as-a-Service: централизованный сервис памяти для агентов; * AutoJudge: инструмент для подбора судей и метрик для оценки качества работы агентов. Ожидаем от кандидата опыт в разработке AI-агентов и LLM-приложений (RAG, чат-боты), а также владение современными инструментами разработчика (agent-driven development). Обязанности * адаптировать код из open-source и от RnD-команд под требования и специфику платформы * проектировать сервисы для агентского окружения с LLM под капотом * подбирать критерии успеха, метрики для оценки качества работы агентов, формировать тестовые корзины для агентов * изучать SotA-решения от лидеров рынка, составлять обзоры. Требования * высшее образование: математика, физика, информатика или профильный data science * опыт от 2 лет в ML, NLP, LLM, AI-агентах * уверенное знание Python, пишете код, который уходит в прод: есты, ревью, код-стайл * английский B2 и выше для чтения статей и документации. Условия * владение современными инструментами разработчика (agent-driven development) * публикации, доклады, open source.

Задачи: — Автоматизация резервного копирования данных Hadoop. Требования: — Практический опыт разработки и сопровождения процессов обработки данных на Apache Flink, включая работу с потоками данных, состоянием задач, checkpoint/savepoint, обработкой ошибок и перезапуском заданий. — Опыт работы с экосистемой Hadoop, особенно с HDFS: организация хранения данных, работа с файлами и каталогами, понимание распределённого хранения и отказоустойчивости. — Опыт разработки пайплайнов данных, настройки расписаний и зависимостей, контроля успешности выполнения. — Уверенное владение SQL и одним из языков разработки пайплайнов: Python, Java или Scala. — Понимание принципов резервного копирования и восстановления данных, включая целостность копий, повторный запуск после сбоя, контроль пропущенных и дублирующихся данных. — Опыт диагностики ошибок обработки данных по логам и метрикам, поиск причин сбоев и оптимизация производительности пайплайнов. — Опыт работы с Git, участие в code review и ведение технической документации. Желательно: — Опыт работы с Hive и другими компонентами Hadoop. — Опыт интеграции Flink с Kafka и источниками данных в Hadoop. — Опыт работы с Apache Airflow, Spark, S3-совместимыми хранилищами. — Опыт настройки мониторинга и алертинга в Grafana и Prometheus. Условия: — Удалённая работа. — Готовность оформления на 0,5 к заказчику. Локация: РФ. Занятость: фулл тайм.

Задачи: — Развертывание, настройка, подключение и поддержка платформы и сервисов Big Data, включая Airflow, Spark, JupyterHub, Superset, Vault, Redis и связанные компоненты; — Мониторинг состояния платформы, расследование инцидентов в производственной среде, восстановление доступности сервисов и анализ первопричин; — Эксплуатация и устранение неполадок в производственной инфраструктуре платформы как в локальных, так и в физических средах; — Устранение неполадок, связанных с развертыванием, сетью, хранилищем, DNS, TLS, подключением к сервисам, производительностью, надежностью и использованием ресурсов; — Разработка и совершенствование конвейеров CI/CD с использованием GitLab CI и инструментов сборки контейнеров, таких как Docker, Kaniko, BuildKit или аналогичные; — Разработка и поддержка рабочих процессов развертывания на основе GitOps с использованием ArgoCD и/или FluxCD; — Создание и поддержка шаблонов развертывания приложений с использованием Helm и Kustomize; — Интеграция и поддержка решений для программно-определяемого хранилища для контейнеризированных рабочих нагрузок с сохранением состояния; — Управление инфраструктурой как кодом и автоматизация с использованием Terraform и Ansible; — Интеграция компонентов инфраструктуры, таких как контроллеры входящего трафика, управление сертификатами, DNS, управление секретами, мониторинг, логирование, оповещения и хранилище; — Улучшение автоматизации, документации, руководств по эксплуатации, мониторинга, оповещений и операционных стандартов на всей платформе. Требования: — Опыт работы более 5 лет в качестве инженера платформы, инженера DevOps, SRE, инженера инфраструктуры или аналогичной должности; — Уверенное владение Linux, сетевыми технологиями, DNS, TLS и навыками устранения неполадок на системном уровне; — Практический опыт расследования инцидентов в производственной среде и решения проблем с надежностью; — Опыт работы с производственной инфраструктурой в локальной или физической среде; — Уверенный опыт работы с GitLab CI и сложными конвейерами CI/CD; — Опыт работы с контейнерными технологиями и инструментами сборки образов, такими как Docker, Kaniko, BuildKit или аналогичными; — Опыт работы с инструментами GitOps, такими как ArgoCD и/или FluxCD; — Опыт работы с Helm и Kustomize; — Опыт работы с Terraform и Ansible; — Практический опыт мониторинга, логирования, оповещения и устранения неполадок в производственных системах; — Опыт работы с Kubernetes или аналогичными платформами оркестрации контейнеров в производственной среде; — Понимание принципов работы с хранилищем и рабочими нагрузками с сохранением состояния: PV/PVC, StorageClass, CSI, репликация, резервное копирование, восстановление и аварийное восстановление; — Практическое понимание развертывания, настройки, подключения и устранения неполадок в сервисах с сохранением состояния, таких как Kafka, ClickHouse, Vault, Redis или аналогичных; — Умение работать с неполной информацией во время инцидентов, изолировать проблемную область, четко сообщать о статусе и доводить проблемы до решения. Желательно: — Опыт работы с сервисами Big Data, такими как Airflow, Spark, JupyterHub, Superset, Trino, Flink или аналогичными; — Опыт работы с решениями SDS, такими как Ceph, Longhorn, OpenEBS/Mayastor, Portworx или аналогичными; — Опыт работы с выделенными серверами, PXE, MaaS, MetalLB или аналогичными технологиями; — Опыт работы с HashiCorp Vault или другими решениями для управления секретами; — Опыт работы со стеками мониторинга, такими как Prometheus, Grafana, Loki, Tempo, Mimir, OpenTelemetry, ELK или OpenSearch; — Опыт работы в многопользовательских или многоклиентских средах; — Опыт работы с усилением безопасности, RBAC, сетевыми политиками, сканированием образов и безопасностью цепочки поставок. Условия: — Офисный формат работы в Сербии (г. Белград); — Помощь с релокацией в Сербию, оформление рабочей визы, а также помощь в оформлении документов для членов семьи; — Возможность развивать свои компетенции и применять знания на практике; — Возможность поработать на крупных проектах; — Компания заинтересована в развитии своих сотрудников: готовы привлекать наставников и оплачивать курсы для повышения квалификации; — Отсутствие бюрократии, быстрое согласование необходимых вещей; — Оплачиваемые лист нетрудоспособности (больничный лист) и отпускные; — Частичная компенсация английского или сербского языков (на выбор); — Компенсация спортзала; — 4 day-off в год за счет компании; — Компания поддерживает сотрудника в важные периоды жизни; — Годовой бонус по результатам работы; — Молодой коллектив, средний возраст в компании — 27 лет; — Большая высококвалифицированная команда, к которой всегда можно обратиться за помощью; — Реферальная программа за успешную рекомендацию.