Данные и ML вакансии - удалённо

Location: Philippines; India; Cairo, Egypt; Mexico City, Mexico; Mexico; Brazil; Malaysia; Indonesia Workplace: remote About the Role  We are looking for detail-oriented annotators to help label robot manipulation videos for AI training purposes. You'll watch short videos of robots performing manipulation tasks (filmed from three synchronized camera angles) and produce precise, structured, natural-language descriptions of the actions taking place. This work directly supports the development of robotics AI models and requires strong written English, sharp observational skills, and the discipline to follow a detailed style guide consistently.  What You'll Do  1. Watch short robot manipulation videos, each filmed from three synchronized camera views (an overhead view and views from each of the robot's two wrist-mounted cameras).  2. Break each video into time segments and write clear, natural-language descriptions for each segment.  3. Apply labels at three levels of detail for each applicable segment:  - Atomic motion (a few seconds) — a single small movement (e.g., "close fingers around the red handle")  - Skill / subtask (several seconds to ~20 seconds) — a complete, meaningful action (e.g., "pick up the red block by its edge")  - Task / goal (up to ~1 minute) — the overall purpose of a sequence of skills (e.g., "place all blocks in the container")    4. Ensure every moment of video is covered by a label at two or more of these levels — no gaps, including idle or pause moments.  5. Accurately describe exactly what happens, including when something doesn't go as planned (a dropped object, a failed grasp, a slipped grip). Precision matters more than making the robot look successful.  6. Cross-reference all three camera angles: use the overhead view to understand the overall scene and object identity, and the close-up wrist views to confirm exact contact and grasp details.  7. Follow a detailed style guide covering vocabulary for actions, spatial relationships, object descriptions, and manner of movement, applying it consistently across many episodes.  8. Participate in periodic calibration sessions to align your labeling with the team and the client's reference examples.    What We're Looking For  Required:  1. Strong written English — you'll write dozens of short, precise descriptive sentences per video and need to vary your language rather than repeating the same phrases.  2. Sharp attention to detail — able to distinguish small differences (a successful grasp vs. a fumble, a push vs. a drag, which specific object part is being touched).  3. Comfort following a detailed, structured style guide and applying it consistently, even in ambiguous or edge-case scenarios.  4. Basic comfort with spatial/mechanical description (left/right, above/below, naming object parts like handles, lids, or edges).  5. Reliable, self-directed work habits — this is often heads-down work with periodic check-ins rather than close supervision.  Nice to Have:  1. Prior experience with video annotation, data labeling, transcription, or QA work.  2. Familiarity with robotics terminology (grippers, end-effectors, manipulation) — helpful but not necessary, as the style guide is self-contained.  3. Experience with annotation tools such as Label Studio.    #L1-CC1 Why Join Welo Data?   ✨ Limitless Flexibility   Project-based opportunities that fit your availability. Choose when and how much you want to contribute—fully remote, with complete autonomy.    🌱 Limitless Growth   Optional access to AI and Large Language Model workshops designed specifically for professionals like you. No coding required—just your expertise.    🌍 Limitless Support   Be part of a global contributor community with responsive guidance and support.    💡 Real Impact   Apply your expertise in the Legal field to influence the AI systems shaping the future of your industry—while collaborating with data professionals and expanding your skills.     How to Apply?  Apply now by answering a few quick questions to join our database and become part of our growing community.    About Welo Data  Welo Data, part of Welocalize, is a global AI data company with 500,000+ contributors delivering high-quality, ethical data to train the world’s most advanced AI systems. We’re building smarter, more human AI with a diverse community in 100+ countries.   At Welo Data, Limitless AI. Limitless You. isn’t just a slogan—it’s our promise. We build smarter AI through the power of human contribution, offering limitless opportunities for our global community to grow, contribute, and work on their terms.

Location: European Union; Ukraine Workplace: Remote ABOUT US Ruby Labs is a leading tech company that creates and operates innovative consumer products. We offer a diverse range of opportunities across the health, education, and entertainment industries. Our innovative teams are driving the future of consumer-led products, and we're always looking for passionate individuals to join us. Learn more about our story at: https://rubylabs.com/about-us/ ABOUT THE ROLE We are looking for a Data Analytics Engineer to build, optimize, and maintain the analytical data layer that powers decision-making across the organization. You will transform business requirements into reliable, well-modeled datasets using dbt, SQL, and cloud data warehouses (BigQuery, ClickHouse). This role combines data modeling, documentation, data quality design, and close collaboration with analysts, engineers, and product teams to ensure consistent, trustworthy reporting. You will serve as the bridge between raw data ingestion and analytics — shaping how data is structured, validated, and delivered. Responsibilities - Design, build, and maintain dbt models (staging, core, marts) based on business needs - Develop scalable SQL transformations and ensure efficient, cost-effective query performance - Implement data quality tests (dbt tests, custom checks, anomaly detection) and maintain high data reliability. - Create and maintain documentation for datasets, data lineage, business logic, and transformation rules - Collaborate with Data Engineers on improving raw data structures, ingestion patterns, and source readiness - Partner with Data Analysts, Product Managers, and Marketing teams to translate business requirements into clean, reusable data models - Monitor data pipelines and marts, resolve issues, and prevent inconsistencies in reporting - Support governance initiatives: naming conventions, taxonomy, schemas, metric definitions, and standardization - Contribute to continuous improvement of our data modeling frameworks, performance tuning, and technical decision-making Requirements - Strong SQL experience with a deep understanding of analytical modeling concepts - Hands-on experience with dbt (models, tests, docs, macros) - Experience working with cloud data warehouses such as BigQuery or ClickHouse - Understanding of data modeling patterns - Familiarity with data engineering concepts (ELT/ETL, orchestration, version control, CI/CD) - Experience with data quality frameworks or implementing validation logic - Ability to translate complex business processes into clean analytical datasets. - Strong documentation skills and attention to detail - Experience collaborating with cross-functional teams and supporting analysts with reliable data - Knowledge of Python is a plus (but not required) Nice To Haves - Experience with orchestration tools (Airflow, Dagster) - Experience working with marketing, product, or billing datasets - Exposure to metric layers, semantic layer design, or BI governance - Basic understanding of observability tooling (Grafana, Prometheus, ... ) - Experience optimizing BigQuery costs or ClickHouse performance LOCATION Ruby Labs operates within the CET (Central European Time) zone. Applicants from any country are welcome to apply for the position as long as they are located within approximately ± 4 hours of CET. This ensures optimal collaboration and communication during working hours. BENEFITS Discover the perks of being part of our vibrant team! We offer: - Remote Work Environment: Embrace the freedom to work from anywhere, anytime, promoting a healthy work-life balance. - Unlimited PTO: Enjoy unlimited paid time off to recharge and prioritize your well-being, without counting days. - Paid National Holidays: Celebrate and relax on national holidays with paid time off to unwind and recharge. - Company-provided MacBook: Experience seamless productivity with top-notch Apple MacBooks provided to all employees who need them. - Flexible Independent Contractor Agreement: Unlock the benefits of flexibility, autonomy, and entrepreneurial opportunities. Benefit from tax advantages, networking opportunities, reduced employment obligations, and the freedom to work from anywhere. Read more about it here: https://docs.google.com/document/d/1nkrN76JlZkbKj9WSOhlT1_mni_CZeDkHdwfIjPXVwvk/preview?tab=t.0#heading=h.ndsdl4wapxtt Be part of our fast-growing team and seize this excellent opportunity for personal and professional growth! INTERVIEW PROCESS After submitting your application, we conduct a thorough review which typically takes 3 to 5 days, but may occasionally take longer due to the volume of applications received. If we see a potential fit, we proceed with the following steps: - Recruiter Screening (40 minutes) - Technical Interview (90 minutes) - Final Interview (60 minutes) LIFE AT RUBY LABS At Ruby Labs, we move fast, aim high, and expect the same from our team. We’re not here to play small—we’re here to build, grow, and win. That means we look for people who are ambitious, driven, and ready to give their best every single day. This is a place for individuals who thrive under pressure, embrace challenges, and see opportunity in every obstacle. If you’re hungry to achieve, motivated by impact, and want to grow at the speed of your own ambition, Ruby Labs offers the platform to make it happen. Here, effort is matched with reward. We recognize those who go all in and deliver results, and we create space for people who want more—more responsibility, more growth, and more success. #LI-Remote

Location: United Kingdom; Spain; Germany Workplace: remote Help Us Build The Future of Travel At Airalo, we're making it easier for people to stay connected wherever they travel. As the world's first eSIM store, we help millions of travelers access affordable mobile data in 200+ countries and regions around the world. Today, we're a team of 400+ people across 60+ countries, building a product used by travelers every day. We've grown quickly, but we've worked hard to keep what matters: trust, ownership, and the freedom for people to do great work without unnecessary layers or bureaucracy. We're fully remote by design, genuinely global, and united by a shared mission to make travel simpler for everyone. Your Next Destination Location: Remote. Contract: Full-time, permanent. Benefits: Learn more about our benefits here in this link - https://airalo-public.notion.site/Benefits-25396a97ffca81fb9bc1f0be479f1be3?pvs=74  Languages: English is our main working language day to day, so you'll need to be comfortable communicating in it both in meetings and async.   We're looking for a Staff Data Scientist, Pricing to build the analytical and machine learning capability that pricing runs on, from demand modelling through to the systems that recommend price. You won't be starting from zero. We've invested in the foundational data and infrastructure that pricing decisions depend on. What's missing is the capability on top: models the business trusts, experiments that settle pricing questions with evidence, and economics defined once so they hold consistently across Commercial, Product and the customer experience. The arc of the role runs from measurement to prediction to prescription - understanding how demand responds to price, forecasting how it will respond, and ultimately building the models that recommend the price itself. What You'll Do: Build and own the demand and elasticity modelling capability, quantifying how price affects volume across destination, duration, data tier, and customer segment. Develop machine learning models for demand forecasting and willingness-to-pay, and take them from exploration through to production with the monitoring and retraining that keeps them honest. Move us from predictive to prescriptive by building the optimisation layer that turns forecasts and elasticities into recommended prices under margin, competitive and partner constraints. Design and analyse pricing experiments with statistical rigour, and apply causal methods where clean randomisation isn't possible. Define pricing within our data ecosystem, owning the governed definitions of price, cost and package economics that reporting, analysis and product surfaces all read from. Partner with Commercial and Finance to connect pricing decisions to margin and revenue, and to size opportunities before we commit. Set the analytical standard for pricing at Airalo, from what counts as evidence through to how a model or recommendation gets validated before it influences live pricing. What You'll Bring: 7+ years in data science, quantitative economics, or applied research, including pricing, monetisation, or marketplace economics work that demonstrably changed decisions. Experience with dynamic or algorithmic pricing systems in production. An advanced degree in Econometrics, Statistics, Operations Research or similar, or equivalent applied depth in demand estimation and the identification problems that make naive price-quantity regressions wrong. Hands-on machine learning experience across the full lifecycle, from feature engineering and model selection through to deployment Familiarity with optimisation and decision-science methods that turn predictions into recommended actions, whether through constrained optimisation, bandits, or reinforcement learning approaches. Proven experimentation expertise, having designed and defended experiments with a clear view on decision frameworks and common failure modes. Experience building analytical capability where none existed before, turning raw data and a business question into something a commercial team uses repeatedly. Strong data modelling instincts, thinking in reusable definitions and single sources of truth rather than standalone analyses. The ability to move comfortably between financial, product and operational data and connect the analysis to a financial outcome. Genuine partnership instincts, translating between analytical rigour and commercial reality so that Commercial and Product come to you early rather than after the decision. Fluency in the Python ML stack alongside strong SQL, with the engineering hygiene to hand over code that others can run and maintain. Excellent communication skills, including the ability to make a methodological argument to people who won't check your standard errors. A self-starter mindset that thrives in ambiguity and brings structure without waiting for permission. Comfort using AI tools to augment analytical work, with a point of view on where they help and where they don't Nice to have: Experience in marketplace, telecom, travel, or subscription/usage-based businesses. Bayesian or hierarchical modelling for sparse segments and long-tail SKUs. Competitive price response modelling, or working with scraped competitor pricing data. Familiarity with dbt, LightDash, or similar semantic/BI layers. Experience designing semantic or metric layers consumed by both analytics and production systems. Experience working in cross-functional teams spanning commercial, finance and product disciplines. If you are interested in this position, please apply via the link. Why People Join Us We started Airalo to make staying connected effortless, wherever you are in the world. Today, millions of travelers rely on our eSIMs, and many of the people building the product were customers first. Our team spans 60+ countries, united by a shared belief that great work happens when people are trusted to own what they do. There's trust to own what you do, space to focus without constant layers of process, and a clear sense of what you're responsible for from day one. As Airalo continues to grow, so do the opportunities to learn, take on new challenges, and make a real impact - from anywhere in the world. Before You Hit Apply By applying, you acknowledge and agree that, in case of successful application, Airalo may request to run background checks as a condition for entering into an agreement with you. Rest assured that these checks will only occur upon your prior consent and at the end of the selection process, and will be strictly limited to what is allowed under the laws that are applicable to you. All data that you share or that we collect in connection with such checks will be processed in accordance with our Privacy Policy, available here: www.airalo.com/more-info/privacy-policy?srsltid=AfmBOooBT0rXAj1FaNelZ3VfN0wvhwzvAoxdtHnOKSVETpiSjiXVuycy    We sincerely thank all applicants in advance for submitting their interest in this opportunity. Airalo is an equal-opportunity employer and values diversity, equity & inclusion. We do not discriminate on the basis of race, religion, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status. We are committed to providing reasonable accommodations upon request for individuals with disabilities throughout our job interview process. At Airalo, we believe in roaming-free recruitment. Just as we provide clear connectivity for travelers, we provide clear salary ranges for our candidates. 🌍 This role is remote and open to candidates across our supported locations in Europe. The range below reflects the full salary range for this role across the countries where it’s available: 📍UK: £93,200 - £139,800 📍Spain: €77,600- €116,400 📍Western/Northern Europe (Germany, France, Austria, Denmark, Sweden, Finland, Norway): €66,400 - €166,800 📍Southern Europe (Portugal, Italy, Greece, Malta): €70,000 - €137,000) 📍Central & Eastern Europe (Czechia, Hungary, Poland, Lithuania, Estonia): €60,000 - €117,000 📍South-Eastern Europe / Balkans (Romania, Serbia, Bulgaria): €56,000 – €100,000 Your Home Destination: 🛫  Local market rates and cost of living vary. Your specific range within this band depends on where you’re based, and there’s room to discuss what’s right for the person and the role. We’ll walk through your range together on your first call with our Talent team so we’re aligned for takeoff! 🎒✨

Задачи: — Участвовать в формировании модели данных — Проектировать и разрабатывать витрины и потоки данных на основе потребностей бизнес-пользователей — Участвовать в проектах по развитию Data Platform — Писать скрипты SQL и Python (опционально) — Прорабатывать интеграции с внешними системами для Data Platform Требования: — Свободно владеть SQL — Опыт работы с DWH в качестве аналитика или разработчика минимум 5 лет — Понимать теорию СУБД и ETL-процессов — Знание Python или другого языка программирования — Опыт проектирования объектов БД на основании бизнес требований Условия: — Работа в офисе или удаленно по договоренности — Платформа обучения и развития «Т‑Банк Апгрейд» (курсы, тренинги, вебинары, базы знаний) — Поддержка менторов и наставников, помощь в карьерном развитии — Оформление полиса ДМС со стоматологией и страховки от несчастных случаев — Льготное страхование близких — Возможность работы в аккредитованной ИТ-компании — Льготные тарифы на продукты Т‑Банка — Частичная компенсация затрат на спорт — Well-being-программа для улучшения психологического и физического здоровья, а также юридической и финансовой поддержки — Три дополнительных дня отпуска в год — Достойная зарплата, обсуждаемая на собеседовании

Задачи: — Решать сложности бизнес-заказчиков и оценивать полученный результат. — Планировать работу команды (Data Scientists, ML-engineers). — Проектировать развитие системы прогнозирования спроса. — Проектировать с руководителями смежных технических команд совместные интеграции. — Программировать и проводить code review. Требования: — Опыт работы в ML и DS проектах от 5 лет. — Опыт решения задач прогнозирования временных рядов. — Управленческий опыт работы. — Умение писать на Python. — Умение работать с данными на PySpark, SQL или Pandas. — Знание и понимание классических ML алгоритмов (линейная регрессия, деревья решений и градиентный бустинг). — Знание Deep Learning фреймворков (Pytorch, Transformers).

Location: Remote USA Workplace: remote ABOUT YOU We are looking for a Head of Fund Analytics & Automation who is a hands-on builder, rigorous with data, and fluent in finance conversations to join our Credit Fund team in the office of the Chief Credit Officer. The best candidate will be someone who thrives in a fast-paced, highly collaborative, and exceptionally dynamic setting and is excited to design, build, and operate the fund's entire data and automation stack end to end — and then own it as the fund goes live. The credit fund is being built with a deliberately small team. Instead of hiring several analysts and operations staff, we want one senior technical hire who can do both: engineer the platform and sit across the table from investors and borrowers. Underwriting here is built on real payment telemetry — including Xsolla transaction data — and that data edge is the foundation of this role. Strong SQL, Python, and production automation skills are essential, along with real experience in portfolio analytics, market risk, or credit. The ability to ship audit-grade systems and then defend the numbers in front of LPs and borrowers will be key to your success in this role. The role starts as a builder and becomes the owner-operator of the platform, with a growing seat in underwriting decisions and, as the platform and deal volume grow, scope to hire and lead a small team. If you're passionate about applying AI-governed automation to private credit and love building the systems that let a lean team punch far above its weight, we would love to hear from you! ABOUT US Xsolla is a global commerce company with robust tools and services to help developers solve the inherent challenges of the video game industry. From indie to AAA, companies partner with Xsolla to help them fund, distribute, market, and monetize their games. Grounded in the belief in the future of video games, Xsolla is resolute in the mission to bring opportunities together, and continually make new resources available to creators. Headquartered and incorporated in Los Angeles, California, Xsolla operates as the merchant of record and has helped over 1,500+ game developers to reach more players and grow their businesses around the world. With more paths to profits and ways to win, developers have all the things needed to enjoy the game. For more information, visit xsolla.com. Responsibilities First 6 months — priorities in order - Deal pipeline live: origination intake through credit committee, with the full audit trail. - Scoring data layer: borrower financials and payment telemetry ingested, stored, and under documented data contracts. - LP / fundraising pipeline instrumented, and first investor reporting shipped. Explicitly out of scope: legal documentation, fund administration, and accounting — these sit with external providers. Phase 1: Build (through first close) Deal pipeline management - Design and run the full deal workflow: origination intake, screening, scoring, credit committee, closing. - Implement it as governed automation (workflow orchestration such as n8n or similar, with LLM-assisted steps where they add value): every automated output validated against a defined schema, low-confidence results routed to human review, and a complete audit trail suitable for LP due diligence. Scoring and underwriting data layer - Build the ingestion and storage layer for borrower financials and payments telemetry (including Xsolla transaction data) — PostgreSQL or equivalent, ETL pipelines, materialized views, documented data contracts. - Develop credit scoring and forecasting models in Python, with proper train/test discipline, leakage and PII exclusion, and ongoing monitoring for drift and degradation. - Maintain evaluation and regression checks so model and automation quality is measured continuously, not assumed. Fundraising / LP pipeline - Build and operate the investor pipeline: CRM automation, conference and referral pipeline tracking, follow-up orchestration, and data room preparation and upkeep. - Instrument the pipeline so we always know conversion rates, stage aging, and next actions per LP. Investor and borrower dashboards - Ship reporting for both audiences: fund-level metrics for investors, facility-level metrics for borrowers. - Numbers must reconcile to source systems and be reproducible outside the BI tool — audit-grade, not demo-grade. Tableau / Power BI or a lightweight web dashboard, whichever fits. Reliability, cost, and security of the stack - Budget caps and cost monitoring on all AI-assisted automation; regression canaries before changes ship. - Access control and data protection appropriate for fund data: deny-by-default permissions, audit logs, strict handling of LP identities, borrower financials, and deal terms. - Documented runbooks and handover-ready documentation as part of "done" — the stack must be operable by someone other than its author. Phase 2: Operate (post-close) - Move into an operating role on the underwriting side: portfolio monitoring, covenant and collateral tracking, scenario and stress analysis. - Extend the platform to other investment types and support the capital formation team with the same pipeline and reporting infrastructure. Client and investor facing This is not a back-office role. The person will join investor and borrower meetings, present the dashboards and the numbers behind them, and field diligence questions directly. Fluency in finance conversations is as important as engineering. The person will also respond to LP operational due diligence questionnaires on the data and automation stack. Qualifications and Skills Required - 7+ years across data analytics / data engineering, including recent hands-on experience building and running production automation (not prototypes or notebooks). - Proven production experience with LLM-based automation: schema-validated outputs, human review gates, evaluation and regression testing, cost-tiered model routing. - Strong SQL and Python; ownership of a PostgreSQL (or similar) data platform end to end — ETL, materialized views, performance tuning, data contracts. - Workflow orchestration experience (n8n, Airflow, or comparable). - BI and dashboarding: Tableau, Power BI, Qlik, or equivalent web dashboards; a track record of reporting that executives actually used for decisions. - Security discipline for sensitive data: role-based access, deny-by-default policies, audit trails, PII handling. - Finance background: degree in finance or quantitative field plus real experience in portfolio analytics, market risk, or credit — able to hold their own in an underwriting or investor conversation. - Strong written and spoken English; comfortable presenting to senior external audiences. Preferred - Direct exposure to private credit, lending, or fund operations. - Forecasting and statistical modeling track record (capacity planning, SLA/risk forecasting, or similar). - Experience with embeddings / semantic search and multi-model AI setups. - Web development ability (React / TypeScript or similar) for internal tools and dashboards. - Experience in audited or regulated environments (SOC 2, fund audits, or equivalent). Benefits We are passionate about fostering a supportive environment for our team, so we prioritize the physical, mental, and emotional well-being of our employees and their families through a comprehensive Benefits Program. This includes 100% company-paid medical, dental, and vision plans, unlimited Flexible Time Off, and a personalized career roadmap for each employee. By investing in professional development through training and educational opportunities, we ensure that our team thrives both personally and professionally. Together, we’re not just building a business; we’re cultivating a community that values creativity, collaboration, and the transformative power of play. Equal Employment Opportunity Statement Xsolla is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. We do not discriminate based on race, color, religion, sex, national origin, age, disability, sexual orientation, gender identity, or any other characteristic protected by law. We consider qualified applicants with criminal histories in accordance with the Fair Chance Act. Criminal History Consideration For the Head of Fund Analytics & Automation — Credit Fund position, we will conduct a background check that may include the following: Criminal history check Employment verification Education verification Credit history check Relevance to Job Responsibilities The background check is relevant to this position because of the following role responsibilities: Handling sensitive financial information and supporting fund analytics, financial models, and deal data Accessing confidential company data Ensuring compliance with regulatory requirements Rights Under the Fair Chance Act Applicants are encouraged to inquire about their rights under the Fair Chance Act. If you have questions regarding our hiring practices, please contact careers@xsolla.com. By submitting the following job application form, you consent to Xsolla processing your data for career-related inquiries and potential employment opportunities. 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Location: New York; Canada; United States Workplace: Remote 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! Why this role? We're building the foundational infrastructure that will define how the world thinks about and deploys AI, and we want the sharpest, most curious people to help us do it. As a member of our Analytics & Data Insights team, you'll tackle the kind of problems that don't have textbook answers yet, launch products that didn't exist a year ago, and help enterprises understand what foundational AI actually means for their bottom line. As a Data Engineer, you will: - Work directly on new customer experiences built on one of the most advanced AI systems in the world - Collaborate daily with researchers and engineers who are some of the best in the world at what they do - Run implementations end-to-end and see initiatives through to real outcomes - Partner across research, marketing, sales, and finance to help define how Cohere grows, with your recommendations feeding directly into products and strategy You may be a good fit if you have: - 5+ years of experience working on production-grade data processing systems - Strong command of Python and SQL - Experience with distributed data processing frameworks such as Apache Beam, Spark, or Flink, alongside large-scale data system design - The ability to transform unstructured data into performant datasets in relational databases and blob storage - Familiarity with modern analytics stack tooling such as BigQuery, Airflow, or dbt - Knowledge of Java or Golang (nice to have) - Experience with Kubernetes (nice to have) - Genuine excitement about AI - you follow the research, have opinions, and enjoy being in the weeds - Comfort operating at the edge of what's known, with a desire to build something genuinely new rather than optimise what already exists 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.

Simple Life is the #1 AI-powered health coaching app for adults who want to lose weight and enjoy a healthier lifestyle—without the stress or extremes. Our mission is to empower people to feel their best every day. By challenging traditional, restrictive approaches, Simple offers a more sustainable method grounded in ease, personalization, and real-life support. Simple has had over 17 million downloads and more than 300,000 5-star reviews, having helped millions lose weight successfully and sustainably. Simple has earned recognition as Best Virtual Coach and one of the Top 100 AI companies — all thanks to a dedicated global team driving real impact. With SIMPLE as a partner in their pocket, users feel cared for and empowered to embrace — and stick to — new healthy habits. Simple is looking for a Senior Data Engineer to join our Tech team. Push the pace of innovation and build a future of a healthier world with us! Задачи: — Design, build, and maintain streaming pipelines. — Develop custom ingestion pipelines from third-party APIs, webhooks, and SDKs. — Migrate existing data ingestion pipelines from SaaS tools to our own infrastructure. — Deliver data to the raw layer of our Snowflake warehouse with schema consistency, idempotency, and resilience to malformed or late data. — Evolve the existing AWS streaming stack (Kinesis, SQS, Lambda, API Gateway), improving cost efficiency, observability, and resilience. — Maintain infrastructure as code (Terraform) for ingestion and processing resources. — Monitor pipelines, troubleshoot, and resolve incidents. — Contribute to our ongoing work on safe data access for AI agents and tooling. — Continuously improve ingestion architecture, tooling, and engineering practices. Требования: — 5+ years of commercial Python experience in a backend or data engineering role. — Solid understanding of distributed systems architecture, including load balancing, sharding, and trade-offs between synchronous and asynchronous processing. — Hands-on experience with cloud services, preferably AWS (Lambda, API Gateway, containers in ECS/EKS). — Experience building streaming pipelines (Kinesis, SQS, or equivalents). — Experience with infrastructure as code (Terraform or equivalents). — Working knowledge of SQL: ability to read and write queries, including joins and window functions. — Commercial experience with real-world production data: ability to design systems that gracefully handle messy, duplicated, delayed, or malformed input. — Solid understanding of how data flows through production systems, including the ability to anticipate and prevent downstream issues. — Operational mindset: comfortable owning production systems and monitoring them. — Ability to make architectural decisions independently and review the work of teammates. Желательно: — Strong SQL and data modeling skills. — Experience with Snowflake and dbt. — Experience with workflow orchestrators (Airflow, Dagster, Prefect, or equivalents). — Experience working with analytics and product teams as a data provider. — Experience working with sensitive or regulated data. Условия: — Open-minded teams, a welcoming and inclusive company culture. — Competitive salary package based on expertise, skillset, and impact plus stock options. — In-office, remote and hybrid work opportunities. — Equipment needed for productivity. — Premium SIMPLE subscription. — 21 days annual leave plus bank holidays. — Flexible hours focusing on results, not desk time. О компании и ценностях: — Data-driven decision making. — Focus on impact and speed. — Ownership and responsibility. — Encouragement to push limits and innovate. — Team accountability and collaboration.

Location: Europe; Canada; New York Workplace: Remote 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: At Cohere, we're pushing the boundaries of what these technologies can achieve for production environments, and we're seeking a senior engineer who understands the practical challenges of making AI systems work at scale. This role demands deep technical expertise in production environments and the ability to bridge research concepts with real-world implementations. This position is hands-on and engineering-driven. You'll focus on production systems, experiment with frontier models, and collaborate with talented engineers to build scalable, production ready solutions. We strive for excellence in both engineering and science by meticulously designing experiments and design docs. We blend engineering and research seamlessly. Everyone contributes to both, depending on organizational needs. We provide access to world class compute resources, data, and talent to ensure you can do your best work. This role is perfect for you if you: - Have a deep passion for quality work. - Enjoy tuning and optimising large LLM models. - Comfortable working with people with different levels of software engineering skills, from beginner to more advanced. - Comfortable diving into complex ML codebases to identify and resolve issues, ensuring the smooth operation of our systems. - Thrive in a fast-paced, technically challenging environment, where you can contribute your innovative ideas and solutions. Key responsibilities: - Design and write high-performing and scalable software for training models. - Develop new tools to support and accelerate research and LLM training. - Coordinate with other engineering teams (Infrastructure, Efficiency, Serving) and the scientific teams (Agent, Multimodal, Multilingual, etc.). - Research, implement, and experiment with ideas on our cluster and data infrastructure. - Collaborate, Collaborate, and Collaborate with other scientists, engineers, and teams! Qualifications: - Extremely strong software engineering skills. - Value test-driven development methods, clean code, and strive to reduce technical debts at all levels. - Proficiency in Python and related ML frameworks such as JAX, Pytorch and/or XLA/MLIR. - Experience using and debugging large-scale distributed training strategies (memory/speed profiling). - Experience with distributed training infrastructures (Kubernetes) and associated frameworks (Ray). - Experience in ML and LLM academic research. 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: — Проектирование, разработка и сопровождение ETL/ELT-пайплайнов с использованием Snowflake и SQL — Управление облачной инфраструктурой на AWS (S3, Glue, Lambda, CloudWatch, IAM) — Написание чистого и поддерживаемого кода на Python — Автоматизация развертывания и настройки инфраструктуры через Terraform — Оркестрация рабочих процессов с помощью Apache Airflow — Мониторинг и устранение неполадок в пайплайнах, контроль качества данных и производительности систем — Совместная работа с дата-сайентистами, аналитиками и разработчиками для создания масштабируемых решений Требования: — 5+ лет коммерческого опыта в Data Engineering или смежных областях — Глубокие знания Snowflake и продвинутый уровень владения SQL — Реальный опыт работы с AWS (S3, Glue, Lambda, CloudWatch, IAM) — Уверенное владение Python для обработки данных и автоматизации — Практический опыт использования Terraform (инфраструктура как код) — Опыт работы с Apache Airflow для оркестрации пайплайнов — Отличные коммуникативные навыки и английский язык уровня C1 — Способность работать самостоятельно в распределённой команде Будет плюсом: — Опыт интеграции и разработки REST API — Знание CircleCI или других CI/CD-инструментов — Опыт работы в полностью удалённых международных командах — Понимание принципов управления данными и безопасности Локация: Удалённо (предпочтительно за пределами РФ и РБ, желательно страны Латинской Америки или Канада) График работы: по восточному времени США (EST) до 17:00, с привязкой к американскому рабочему календарю. Уровень английского языка: C1 (продвинутый профессиональный)

Location: North America Workplace: Remote At Linear, we're building the product development system for teams and agents. AI is fundamentally changing how software gets built, and we’re shaping the tools this new era requires. Founded in 2019, Linear has become the platform of choice for more than 40,000 companies (including OpenAI, Coinbase, and Ramp) to plan, build, and ship their products. Today, our team is distributed across North America, Europe, and Australia, and we’re continuing to grow internationally. What unites us is relentless focus, fast execution, and a deep care for software craftsmanship. The Data team works across Linear, supporting Product, Engineering, and GTM. We own our data pipelines, warehouse, dashboards, analysis, and integrations with third-party tools. As a small team, we focus on building systems that make data accessible and useful across Linear. We’re looking for someone who wants to help shape how we architect, build, and use data as we grow. Location & work mode Linear is a remote-first company, with optional co-working offices in San Francisco, New York, and London. This role is open to candidates based in North America. You can work from anywhere within this region. We value deep focus and async collaboration, with intentional moments to connect in person through team off-sites, optional co-working, and occasional travel. What you’ll do - Work across Product and GTM (Marketing, Sales, Customer Success, and Finance) to turn ambiguous questions and operational needs into useful metrics, models, analyses, and workflows - Build and maintain dbt models and pipelines that create trusted views of our product, customers, and business - Design clear, maintainable data models and improve the testing, documentation, performance, and reliability of our data stack - Build dashboards and self-service reporting in Metabase and Hex, and dig deeper when the answer requires more than a chart - Operationalize data through reverse ETL and partner with GTM Engineering on the scoring, segmentation, automations, and internal tools that help our teams scale - Balance fast, pragmatic answers with durable solutions, recognizing when a one-off request should become a reusable model or workflow - Find ways to leverage emerging tools, LLMs, and coding agents to accelerate development, analysis, testing, and documentation while maintaining a high bar for correctness What we're looking for - 8+ years of experience in analytics engineering, data analytics, or data engineering, ideally at a fast-moving software company - Exceptional SQL and strong hands-on experience with dbt and a modern cloud data warehouse - Track record of owning data projects end-to-end, from shaping an ambiguous problem to shipping something people rely on - Strong data modeling judgment: you think clearly about grain, reusable components, interfaces, dependencies, and maintainability without relying on a single prescribed methodology - Analytical judgment: you know when to answer quickly, when to investigate deeply, and how to turn complex findings into a clear recommendation - Comfortable moving between technical implementation and business context, from debugging a data model to understanding product adoption or sales efficiency - High ownership mentality: self-directed, pragmatic, and willing to challenge a request or approach when something does not make sense - Strong communication skills and experience partnering directly with both technical and non-technical teams - Comfortable using LLMs and coding agents as part of your day-to-day development workflow Our tech This stack reflects the systems you’ll work in. You’re not expected to have experience with everything listed, but you should be comfortable learning quickly and working across the full lifecycle of data. - Data Warehouse: Snowflake, dbt Cloud - Dashboards / Analysis: Metabase, Hex - ETL / rETL: Hevo, Fivetran - GTM tools: Hubspot, Pocus, Clay What we offer We're a small, focused team that cares deeply about the quality of our work and the people we do it with. Here's what you can expect: - Competitive salary and equity - Employee-friendly equity terms including early exercise in the US and extended exercise windows - Daily meal and coffee stipend on every workday - Paid co-working space or desk - Health coverage (based on country requirements) - 5 weeks paid vacation, plus local statutory holidays - 4 months paid parental leave - Paid month off after 4 years & every 2 years thereafter - Regular team events and off-sites - Remote-first with no required commute Learn how we think and work - A story about our mission: Read Me https://linear.app/readme - Our hiring process: How we hire at Linear https://linear.app/now/how-we-hire-at-linear - How we work: Designing remote work at Linear https://linear.app/now/designing-remote-work-at-linear - How Linear uses Linear: How our Customer Experience team works in Linear https://linear.app/now/cx-in-linear - A video series: Conversations on Quality https://linear.app/quality - Building our teams: Why and how we do work trials at Linear https://linear.app/blog/why-and-how-we-do-work-trials-at-linear - Our recent tender offer: Sharing Linear’s growth with the people building it https://linear.app/now/sharing-growth-with-the-people-building-linear Linear is an equal opportunity employer. We do not discriminate based on race, color, religion, gender identity or expression, sexual orientation, national origin, age, disability, veteran status, or any other protected characteristic under applicable law.

Кого ищем: — Сильного ML-инженера с опытом в трейдинге, который уже имеет опыт разработки прибыльных ML-моделей и торговых стратегий. — Способного самостоятельно генерировать и валидировать торговые идеи, строить исследовательские пайплайны и доводить стратегии до продакшн-состояния. Чем заниматься: — Разрабатывать и улучшать торговые стратегии. — Строить ML и количественные модели для прогнозирования и генерации сигналов. — Анализировать временные ряды и рыночные данные в реальном времени. — Тестировать гипотезы и улучшать существующие стратегии. — Работать с бэктестинговой инфраструктурой. Условия: — Достойная заработная плата в долларах США или криптовалюте. — Удалённый формат работы из любой точки мира, полная занятость. — Оформление через юридическое лицо в Республике Беларусь. — Небольшая команда с минимумом бюрократии, что обеспечивает быстрое принятие решений и оперативную проверку гипотез.

Задачи: - Разработка и обучение ML/DL-моделей для задач NLP и Computer Vision, возможен также classic ML. - Анализ и обработка данных. - Работа с PyTorch. - Тестирование и оптимизация моделей. - Подготовка модели к запуску в продакшене, участие в A/B тестах, поэтапных раскатках и мониторинге качества. - Поддержка существующих моделей. Требования: - Широкий прикладной ML-бэкграунд: опыт работы минимум с двумя направлениями из Computer Vision, NLP / LLM, classic ML (обучение моделей, интеграция их в реальные проекты). - Коммерческий опыт в Data Science / ML от 4 лет. - Уверенное знание Python и PyTorch. - Хорошее знание классического ML и современного DL (включая математику за ними). - Опыт самостоятельного ведения задач на разных этапах проекта. - Умение переводить бизнес-задачу в ML-постановку и предлагать практичные решения.

Задачи: - Разработка и обучение ML/DL-моделей для задач NLP и Computer Vision, возможен также classic ML. - Анализ и обработка данных. - Работа с PyTorch. - Тестирование и оптимизация моделей. - Подготовка модели к запуску в продакшене, участие в A/B тестах, поэтапных раскатках и мониторинге качества. - Поддержка существующих моделей. Требования: - Широкий прикладной ML-бэкграунд: опыт работы минимум с двумя направлениями из Computer Vision, NLP / LLM, classic ML (обучение моделей, интеграция их в реальные проекты). - Коммерческий опыт в Data Science / ML от 4 лет. - Уверенное знание Python и PyTorch. - Хорошее знание классического ML и современного DL (включая математику за ними). - Опыт самостоятельного ведения задач на разных этапах проекта. - Умение переводить бизнес-задачу в ML-постановку и предлагать практичные решения

Задачи: — разрабатывать и внедрять современные решения в области компьютерного зрения Требования: — опыт коммерческой разработки в области Computer Vision от 3 лет — хорошее понимание классического машинного обучения, статистического анализа, классических алгоритмов Computer Vision — практический опыт работы с нейросетевыми фреймворками PyTorch — умение читать научные статьи, воспроизводить решения и адаптировать их под реальные задачи — уверенное владение библиотеками NumPy, scikit-learn, Matplotlib, OpenCV — уверенная работа с Linux (предпочтительно Ubuntu, Mint или Debian) — опыт контейнеризации с использованием Docker и Docker Compose — уверенное владение Git — английский язык — Upper-Intermediate (B2) или выше (обязательно) — наличие проектов, которые были самостоятельно доведены до production Будет преимуществом: — опыт разработки систем видеоаналитики и/или видеонаблюдения — опыт оптимизации скорости инференса моделей с использованием ONNX Runtime и TensorRT — практический опыт работы с Graph Neural Networks (GraphNN/GNN) и Metric Learning — опыт работы с Kubernetes (K8s) Мы ищем человека, который: — любит решать сложные инженерные задачи — следит за современными исследованиями в области AI и Computer Vision — умеет самостоятельно принимать технические решения и доводить проекты до результата — готов работать над задачами полного цикла — от исследования и прототипирования до внедрения в production

RELOCATION TO GERMANY adjoe builds the technologies behind mobile apps growth and monetization. With our core product Playtime Arcade, we’ve become the global leader in rewarded advertising, an ad unit built on a simple premise: users earn real in-app rewards for engaging with new apps. The result is one of the most effective value exchanges in adtech, connecting advertisers and publishers with over 770 million users annually. Architecting Intelligence to Optimize 200M+ Daily Decisions As the intelligence core of our engineering organization, our Data Science team doesn’t just deploy models, we engineer the fundamental decision engine that powers our platform. At a scale of 770 million users and 100,000+ predictions per second, we are solving a multi-objective optimization problem that balances user incentives, advertiser ROI, and long-term platform health in real time. Our architecture is built on a 1PB+ behavioral data lake, providing the high-fidelity input necessary to train deep learning models that predict individual user engagement with precision. We aren’t just optimizing clicks, we are dynamically calculating optimal reward structures to sustain a global value exchange. Engineered for performance, our stack leverages Tensorflow and PyTorch for model training, NVIDIA Triton to achieve sub-100ms inference. We own the full ML lifecycle from high-level research and feature engineering to deployment and A/B experimentation. Here, you will find the autonomy, the data depth, and the massive scale required to solve the most complex optimization challenges in the adtech ecosystem. Your Mission & Who We Are Looking For: — Proven track record in production ML with 5+ years in Data Science and deployed models that moved real business metrics. — Building models that predict LTV, conversion, and user behavior across 770 million users, impacting advertiser ROAS and publisher revenue. — Deep learning as primary tool with strong hands-on experience in PyTorch or TensorFlow and production deployment handling 1M+ daily predictions. — Full ML lifecycle ownership from data extraction using Trino, Spark, or AWS Athena, through model development, A/B validation, to production deployment. — Experience with Airflow or model hosting is a strong plus. — Fluent in Python and data at scale, comfortable with pandas/polars, numpy, scikit-learn, LightGBM, CatBoost, XGBoost, and advanced SQL skills. — Ability to translate product requirements into ML logic and explain model behavior and impact to non-technical stakeholders. — AdTech experience is a strong plus. What’s in It for You? — Relocation support to Hamburg, Germany for worldwide talent. — Opportunity to build infrastructure behind impactful adtech platform reaching hundreds of millions of users and powering billions of decisions daily. — Ownership of projects impacting 770M users and pushing adtech boundaries. — Fast-paced environment with multiple daily solution deployments and learning from results. — Collaborative culture with diverse global team from over 40 countries. — Enjoy daily victories and energetic work environment.