Данные и ML вакансии

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. We process your data in accordance with this Xsolla Privacy Notice for Job Applicants. Please direct any inquiries regarding your data privacy to careers@xsolla.com.

Location: New York, NY; San Francisco, CA Workplace: OnSite ABOUT US At LangChain, our mission is to make intelligent agents ubiquitous. We build the foundation for agent engineering in the real world, helping developers move from prototypes to production-ready AI agents that teams can rely on. We began as widely adopted open-source tools and have grown to also offer a platform for building, evaluating, deploying, and operating agents at scale. With $125M raised at Series B from IVP, Sequoia, Benchmark, CapitalG, and Sapphire Ventures, we’re at a stage where we’re continuing to develop new products, growth is accelerating, and all team members have meaningful impact on what we build and how we work together. LangChain is a place where your contributions can shape how this technology shows up in the real world. Today, our platform includes LangSmith (Observability, Evaluation, Deployment, Fleet, and Sandboxes), our open source frameworks (LangChain, LangGraph, and Deep Agents), and the newly launched LangSmith Engine for autonomous agent improvement. We have 100M+ monthly open source downloads, 6,000+ active LangSmith customers, and 5 of the Fortune 10 use LangSmith in production (+ 35% of the Fortune 500 overall), including teams at Klarna, Clay, Coinbase, Workday, Lyft, Cloudflare, Harvey, Rippling, Vanta, LinkedIn, Monday.com, Nvidia, and Bridgewater. ABOUT THE TEAM: The LangSmith Engine team is building a proactive agent engineer that analyzes production traces, identifies important failures, recommends and writes fixes, and helps prevent those issues from coming back. We’re building agents that can understand complex software systems and continuously improve the quality of other AI agents. ABOUT THE ROLE: We’re looking for an experienced research engineer to help make the Engine agent more capable and more efficient. You’ll study real agent failures, build benchmarks that capture what matters, run experiments to improve performance, and turn successful ideas into production. This may include prompting and agent-harness improvements, model selection, fine-tuning and post-training custom models. The focus is on measurable improvements to the overall agent. This role also requires a understanding of production engineering and system-level tradeoffs. Engine is a production system, so improving an agent is not just about maximizing benchmark performance—it also means understanding the impact on cost, latency, reliability, and scalability. You’ll work in the same team with production engineers to design, test, and ship improvements that work reliably in real-world environments. Location: SF and NYC WHAT YOU’LL DO: - Build and maintain benchmarks and evaluations that measure the quality and efficiency of Engine agents on real-world tasks. - Design and run experiments to improve agent performance across models, prompting, context, tools, orchestration, and agent strategies. - Explore and implement post-training and fine-tuning techniques when they can meaningfully improve agent capabilities, quality, or cost. - Turn successful experiments into production improvements, working closely with engineers and researchers to measure impact and prevent regressions. - Help define the ML roadmap and technical direction for improving Engine agents, and mentor other engineers through strong technical leadership. WHAT YOU’LL BRING: - 4+ years of experience in ML/AI research, or a closely related field. - Master’s or Ph.D. in a relevant scientific field. - Hands-on experience working with LLMs and AI agents, including analyzing model behavior and improving real-world performance - Strong experience designing benchmarks, evaluations, and experiments for AI/ML systems; you know how to tell whether a change actually made an agent better. - Strong software engineering skills, with a track record of taking ideas from research prototype to measurable production impact. - You have maximum agency and strong research judgment: you can identify high-impact problems, work through ambiguity, move quickly, and communicate your findings clearly. NICE TO HAVE: - Ph.D. in Machine Learning, Computer Science or Physics. - Hands on experience with LLM-as-a-judge, automated graders, synthetic data generation, or human evaluation. - Hands on experience with reinforcement learning, preference optimization, SFT, RLHF/RLAIF, or other post-training techniques for LLMs. - Experience optimizing LLM agents for cost, latency, or task efficiency on productions - Experience with model serving, inference optimization, distributed systems, or GPU infrastructure. Compensation Salary Range: $240,000-$280,000 USD Compensation Philosophy: We offer competitive compensation that includes base salary, variable compensation for relevant roles, meaningful equity, benefits, and perks. We are hiring for this role across a range of scope, and the compensation band reflects that. Your offer depends on what we learn about your impact, craft, leadership, and communication during the interview process. We will tell you early where you’re tracking and talk comp with you before offer stage. Team members in the EU, UK, and APAC receive locally competitive benefits aligned with regional norms and regulations. BENEFITS Benefits include medical, dental, and vision coverage, flexible vacation, a 401(k) plan, meals on in-office days in the US and more.

Location: Kazakhstan; Kyrgyzstan; Uzbekistan Workplace: Hybrid Salmon is a technology-driven financial company building a banking and lending platform across Southeast Asia, starting in the Philippines. We combine global fintech expertise with deep local market knowledge to make financial services simple, accessible, and useful for millions of people across the region. 7M+ app downloads. 2M+ monthly active users. 7,000+ partner stores. US$310M+ raised from leading global investors. Manila-based, globally distributed, and hybrid-first — our team spans 45+ countries. If you want to solve complex problems at scale and impact how millions of people access and manage money, come build with us. Southeast Asia's fintech moment starts here. About the role You'll build and own the data pipelines and lakehouse architecture that turn raw data into reliable inputs powering products used by millions of people. What you'll do - Design and run batch and streaming pipelines on Databricks and Apache Spark over AWS, ingesting from databases, APIs, and event streams. - Shape our lakehouse architecture on S3 and Delta Lake, including medallion (Bronze / Silver / Gold) layering and storage optimization. - Work directly with analytics, engineering, and product teams to turn raw data into reliable, usable inputs for the business. What you'll own - Own projects end to end — from stakeholder alignment and design review through implementation, launch, and ongoing support. - Build self-service data solutions that let other teams deliver their own data needs, designed to scale and extend easily as new sources and use cases are added. - Build and maintain scalable ETL/ELT pipelines that are fault-tolerant and cost-optimized. - Optimize Spark jobs end to end — cluster sizing, caching, joins, shuffles, partitioning, and storage formats (Parquet, Delta). - Manage Databricks Workflows, jobs, cluster configurations, and the underlying infrastructure — security policies, resources, and access — responding to failures and performance issues. - Implement data governance and access control with Unity Catalog across accounts and environments, including handling of PII. What makes you a strong fit - Hands-on experience with Databricks and strong proficiency in Apache Spark (PySpark and/or Scala). - Strong Python for data engineering, plus SQL and Spark SQL for transformation and analytics. - Experience with Delta Lake and lakehouse architectures, including performance optimization. - Proven ownership — you've run projects where you knew the weak points, gathered feedback, and made your solutions easy to scale and maintain. - Experience writing clean, testable, maintainable code with Git-based version control. - Experience with infrastructure-as-code such as Terraform or Ansible is a plus — not required, and easy to pick up on the job. What we offer Build what's next in Southeast Asian fintech. Ownership and flexibility - Stock option plan — four-year vesting with a 25% cliff after the first year - Hybrid-first, with fully remote work available for eligible roles and core collaboration hours from 12:00 to 19:00 Manila time (UTC+8) - Company-provided tools and equipment - Office in Bonifacio Global City, Manila — a modern, walkable district at the centre of the Philippines' growing tech scene Health and time off - Medical insurance support for you and your family through co-funding or reimbursement, depending on your location and subject to policy limits - Access to an internal mental health support specialist - 22 vacation days, Philippine public holidays, and 15 sick days - 105 days of paid maternity leave and up to 14 days of paid paternity leave for employees relocating to Manila Relocation and family support - Relocation support for you, your family — including flights, two weeks of accommodation, visas, paperwork, and logistics - Reimbursement of eligible pet relocation expenses - US$2,500 per year for flights home - Baby and family stipends for employees relocating to Manila: US$1,000 per month for each child under two and US$500 per month for each child aged two to 18 Growth and team experience - Opportunities to learn and share your expertise through internal expert meetups, external conferences, speaking opportunities, and industry publications - Company-sponsored trips to Manila to meet and work with your team in person - Regular team offsites, sports activities, and team events - High-performing teams can earn a dedicated beach house week in Southeast Asia Considering a move to Manila? See what life with Salmon looks like https://www.youtube.com/watch?v=nYQLKhuZHIE&t=34s

Location: 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.

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

Задачи: - разрабатывать надежные backend-сервисы для AI-помощников на основе GigaChat - создавать и поддерживать системы сбора и обработки данных: парсинг, логирование, аналитика - оптимизировать код, повышать стабильность и производительность сервисов под высокой нагрузкой - применять инструменты Data Science для улучшения работы систем и интеграции LLM в реальные бизнес-процессы. Требования: - рассматриваем студентов ВУЗа технической специальности, бакалавр 3-4 курс, магистратура 1-2 курс - уверенно владеете Python - знаете библиотеки FastAPI, aiohttp, SQLAlchemy, pandas, bs4 - имеете навыки асинхронного программирования - разбираетесь в SQL и ORM - понимаете принципы REST API, а также основы объектно-ориентированного проектирования (ООП, SOLID) - работаете с Git.

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

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

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

Задачи: — 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.

Обязанности: • Участие в проектах в качестве стажера – разработчика DWH, ETL, BigData • Разработка сценариев обработки данных ETL, ELT • Проектирование логических и физических моделей данных в DWH • Написание проектной технической документации • Знакомство с новыми продуктами и технологическими стеками Требования: • Высшее техническое образование или студент старших курсов • Базовые знания SQL и СУБД • Базовые знания DWH, Data Lake, ETL и желание развиваться в сфере работы с данными • Знания англ. языка на техническом уровне или выше • Пройденный офлайн/онлайн курс по любой из тем: СУБД, DWH, BigData, ETL (будет плюсом) • Опыт построения моделей данных для СУБД в т.ч. с применением CASE-инструментов (будет плюсом) • Знания принципов и методологий построения решений класса DWH (будет плюсом) • Знания в части работы ETL-процессов и инструментов (будет плюсом) • Знания в области БД (Hive, ClickHouse, GreenPlum, PostgreSQL, Oracle DB, MS SQL или другие) (будет плюсом) • Знания языков программирования Python, Java, Scala и т.д. (будет плюсом) Условия: • Зарплату, соответствующую опыту и навыкам • Прозрачную систему мотивации • Работу в компании с аккредитацией в Минцифре • Курсы и тренинги от корпоративного университета, внешнее обучение за счет компании • Расширенный пакет ДМС со стоматологией и психологом, телемедицина 24/7, HotLineCROC • Сезонные спортивные программы, современный спортзал для занятий в офисе, беговой клуб, приложение Croc Sport, командные соревнования • Совместные путешествия, праздники, тимбилдинги, клуб путешественников • Возможность гибко строить свой карьерный трек • Выступать на внешних и внутренних митапах и конференциях, писать в техноблоги, участвовать в образовательных программах • Компенсация питания в CROC Cafe, экосистема сервисов, парковка, корпоративный транспорт от метро

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