Вакансии

Location: Ukraine; Spain; Slovakia; Serbia; Yerevan; Slovenia; Poland Workplace: remote Provectus is an AWS Premier Partner and an Anthropic Strategic Partner, working at the frontier of applied AI. We help enterprises turn Claude, agentic systems, and their own data into measurable business outcomes — through bespoke applications, managed services, and advisory engagements. With offices in North America, LATAM, and EMEA, we partner with clients worldwide. Our work centers on two verticals — Financial Services & Insurance and Healthcare & Life Sciences — where we deploy five pre-built AI Blueprints: Submission Flow, Portfolio Lens, Asset Flow, Revenue Flow, and Evidence Lens. Each Blueprint rebuilds a critical business process front to back, shipped from working code and tuned to a client's specific book, regulators, and operating posture. Our team holds 100+ AWS certifications, is Claude Code certified, and co-delivers Anthropic's Agentic SDLC program, Cowork Activation, and AI Blueprint engagements. Where this role sits You will work in a small, senior pod alongside an FDE and an FDX,  Forward Deployed Executives (FDX) own the commercial relationship and the business outcome. Works alongside the client's leadership or C-suite level to move the client's KPIs. Forward Deployed Engineers (FDE) embed with a client, map the client workflow, identify the business problem underneath it, design and build a working AI solution, present to the client, and transfer the knowledge to the client's team. Owns technical direction of the whole solution. Senior AI Engineer. When an FDE comes back from the client with the business problem, you will help turn that into an agentic system that runs in production and will be responsible for evaluation, observability, and guardrails.  You'll have real ownership of components and of the technical decisions inside them. Requirements: Mindset Proactive and self-directed; you push for clarity rather than waiting for a ticket Excellent communication and problem-solving skills Comfort with ambiguity and ownership. B2+ English, comfortable collaborating across distributed, multicultural teams. Technical depth 5+ years in software or ML engineering, with production systems you were accountable for. Solid AI/ML foundations. You understand what the models do well enough to reason about failure modes. Shipped to production LLM applications and agentic workflows — not demos, not POCs, not notebooks. Agentic orchestration: multi-step workflows, graph-based orchestration, tool use, state management, and recovery from partial failure Experience with LLM APIs (Anthropic, AWS Bedrock, or OpenAI) and agent frameworks. Experience building and optimizing RAG systems in production. Strong engineering fundamentals. Full-stack mindset, comfortable across AI, backend development, and cloud infrastructure. Python and/or TypeScript proficiency; depth matters more than stack. Dropped into an unfamiliar codebase, you're productive. Hands-on AWS in production: Bedrock, Bedrock AgentCore, Lambda, ECS, S3, SQS, ECR, or similar. GCP or Azure is a plus. Cloud-native delivery: containers, ECS or Kubernetes, IaC, and CI/CD applied to AI pipelines. You evaluate. You have built or owned an eval suite for a non-deterministic system, and you can explain what you measured, how you produced ground truth, and what gated a release. Model and agent monitoring, drift detection. Cost and latency discipline: model tiering, caching, and the ability to say what a workload costs to run before it runs. Hands-on production experience with the Claude ecosystem —  Claude Code, CLAUDE.md, hooks, skills files.  Spec-driven development — writing the intent, constraints, and acceptance criteria before you let an agent build — is a strong plus. MCP: you can say why an agent would prefer it to a REST integration. Having authored a server is a plus. Nice to Have: Experience in one of the industries: financial services, insurance, healthcare. Consulting, professional services, or other embedded customer-facing delivery. AWS and Claude Code Certifications A2A: you can explain agent-to-agent interoperability CI/CD pipeline experience (GitHub Actions, GitLab CI) Practical experience with one or more use cases from the following: NLP, LLMs, and Recommendation engines. Experience in an additional language (Go, TypeScript, or Rust). Experience with Apache Spark, Apache Airflow, Kafkа Responsibilities: Work in a pair with an FDE and an FDX. Build and ship production GenAI systems into the customer’s environment (cloud-native data, LLM-based, and agentic AI solutions). Build and optimize RAG systems for production use cases Build the evaluation harness before you build the feature. Write production code across the stack — AI, backend services, data pipelines. We choose tools to fit the customer. Integrate AI components into backend services and RESTful APIs Take systems to production on AWS (GCP or Azure where the customer requires it): containerised, CI/CD. Implement LLMOps and AgentOps practices: agent tracing, prompt and version management, cost and latency monitoring, regression testing, drift detection Start from the blueprint, contribute to enablement and handover: clear documentation, runbooks, and pairing with the client engineers who will inherit the system. Feed reusable components and lessons back into the Provectus Blueprints Participate in technical discussions and architectural decisions Conduct model evaluation, improve failure modes you find, optimize model performance, efficiency, and reliability Mentor junior and mid-level AI engineers, conduct code reviews and share knowledge across the team through documentation, presentations, and workshops. What We Offer: The chance to shape how leading enterprises across LATAM, Europe, and North America adopt AI, from strategy through first deployment A forward-deployed model working in small, senior teams alongside FDE and FDX A growing AI delivery practice where you help build the tooling and frameworks, not just use them Remote-friendly culture Internal training programs with full support for Claude, AWS, and other professional certifications, conference attendance Career growth; we actively develop our engineers Access to the latest AI tools and premium subscriptions Long-term B2B collaboration Private medical insurance or a budget for your medical needs Paid sick leave, vacation, and public holidays Equipment and all the tech you need for comfortable, productive work How we hire: Intro conversation. The role, your background and aspirations, tech questions. Technical interview with live engineering sessions. Real problems, your own editor, you may use an LLM assistant HR Interview. Soft skills and expectations HM interview. Tech questions; a live engineering session is also possible

Location: North Macedonia; Bosnia and Herzegovina; Czechia; Croatia; Moldova; Bulgaria; Montenegro Workplace: remote Provectus is an AWS Premier Partner and an Anthropic Strategic Partner, working at the frontier of applied AI. We help enterprises turn Claude, agentic systems, and their own data into measurable business outcomes — through bespoke applications, managed services, and advisory engagements. With offices in North America, LATAM, and EMEA, we partner with clients worldwide. Our work centers on two verticals — Financial Services & Insurance and Healthcare & Life Sciences — where we deploy five pre-built AI Blueprints: Submission Flow, Portfolio Lens, Asset Flow, Revenue Flow, and Evidence Lens. Each Blueprint rebuilds a critical business process front to back, shipped from working code and tuned to a client's specific book, regulators, and operating posture. Our team holds 100+ AWS certifications, is Claude Code certified, and co-delivers Anthropic's Agentic SDLC program, Cowork Activation, and AI Blueprint engagements. Where this role sits You will work in a small, senior pod alongside an FDE and an FDX,  Forward Deployed Executives (FDX) own the commercial relationship and the business outcome. Works alongside the client's leadership or C-suite level to move the client's KPIs. Forward Deployed Engineers (FDE) embed with a client, map the client workflow, identify the business problem underneath it, design and build a working AI solution, present to the client, and transfer the knowledge to the client's team. Owns technical direction of the whole solution. Senior AI Engineer. When an FDE comes back from the client with the business problem, you will help turn that into an agentic system that runs in production and will be responsible for evaluation, observability, and guardrails.  You'll have real ownership of components and of the technical decisions inside them. Requirements: Mindset Proactive and self-directed; you push for clarity rather than waiting for a ticket Excellent communication and problem-solving skills Comfort with ambiguity and ownership. B2+ English, comfortable collaborating across distributed, multicultural teams. Technical depth 5+ years in software or ML engineering, with production systems you were accountable for. Solid AI/ML foundations. You understand what the models do well enough to reason about failure modes. Shipped to production LLM applications and agentic workflows — not demos, not POCs, not notebooks. Agentic orchestration: multi-step workflows, graph-based orchestration, tool use, state management, and recovery from partial failure Experience with LLM APIs (Anthropic, AWS Bedrock, or OpenAI) and agent frameworks. Experience building and optimizing RAG systems in production. Strong engineering fundamentals. Full-stack mindset, comfortable across AI, backend development, and cloud infrastructure. Python and/or TypeScript proficiency; depth matters more than stack. Dropped into an unfamiliar codebase, you're productive. Hands-on AWS in production: Bedrock, Bedrock AgentCore, Lambda, ECS, S3, SQS, ECR, or similar. GCP or Azure is a plus. Cloud-native delivery: containers, ECS or Kubernetes, IaC, and CI/CD applied to AI pipelines. You evaluate. You have built or owned an eval suite for a non-deterministic system, and you can explain what you measured, how you produced ground truth, and what gated a release. Model and agent monitoring, drift detection. Cost and latency discipline: model tiering, caching, and the ability to say what a workload costs to run before it runs. Hands-on production experience with the Claude ecosystem —  Claude Code, CLAUDE.md, hooks, skills files.  Spec-driven development — writing the intent, constraints, and acceptance criteria before you let an agent build — is a strong plus. MCP: you can say why an agent would prefer it to a REST integration. Having authored a server is a plus. Nice to Have: Experience in one of the industries: financial services, insurance, healthcare. Consulting, professional services, or other embedded customer-facing delivery. AWS and Claude Code Certifications A2A: you can explain agent-to-agent interoperability CI/CD pipeline experience (GitHub Actions, GitLab CI) Practical experience with one or more use cases from the following: NLP, LLMs, and Recommendation engines. Experience in an additional language (Go, TypeScript, or Rust). Experience with Apache Spark, Apache Airflow, Kafkа Responsibilities: Work in a pair with an FDE and an FDX. Build and ship production GenAI systems into the customer’s environment (cloud-native data, LLM-based, and agentic AI solutions). Build and optimize RAG systems for production use cases Build the evaluation harness before you build the feature. Write production code across the stack — AI, backend services, data pipelines. We choose tools to fit the customer. Integrate AI components into backend services and RESTful APIs Take systems to production on AWS (GCP or Azure where the customer requires it): containerised, CI/CD. Implement LLMOps and AgentOps practices: agent tracing, prompt and version management, cost and latency monitoring, regression testing, drift detection Start from the blueprint, contribute to enablement and handover: clear documentation, runbooks, and pairing with the client engineers who will inherit the system. Feed reusable components and lessons back into the Provectus Blueprints Participate in technical discussions and architectural decisions Conduct model evaluation, improve failure modes you find, optimize model performance, efficiency, and reliability Mentor junior and mid-level AI engineers, conduct code reviews and share knowledge across the team through documentation, presentations, and workshops. What We Offer: The chance to shape how leading enterprises across LATAM, Europe, and North America adopt AI, from strategy through first deployment A forward-deployed model working in small, senior teams alongside FDE and FDX A growing AI delivery practice where you help build the tooling and frameworks, not just use them Remote-friendly culture Internal training programs with full support for Claude, AWS, and other professional certifications, conference attendance Career growth; we actively develop our engineers Access to the latest AI tools and premium subscriptions Long-term B2B collaboration Private medical insurance or a budget for your medical needs Paid sick leave, vacation, and public holidays Equipment and all the tech you need for comfortable, productive work How we hire: Intro conversation. The role, your background and aspirations, tech questions. Technical interview with live engineering sessions. Real problems, your own editor, you may use an LLM assistant HR Interview. Soft skills and expectations HM interview. Tech questions; a live engineering session is also possible

Location: Москва, удалённая работа, RU; Россия О команде Наша команда развивает LLM Gateway — единую точку доступа в Авито к языковым моделям. Мы помогаем продуктовым командам быстро и безопасно получать доступ к подходящим моделям, прозрачно управлять лимитами, видеть стоимость каждого запроса и мониторить поведение моделей в продакшене. Какие задачи вас ждут: развивать платформу LLM Gateway для всего Авито, чтобы создавать продукты с LLM было быстро и удобно; автоматизировать выдачу и настройку доступов к моделям; развивать управление потреблением LLM: рассчитывать стоимость LLM-вызова; настраивать бюджетные лимиты для клиентов и приоритеты; развивать динамическую балансировку трафика между LLM-моделями; определять SLA платформы и методику его расчёта: настраивать мониторинг и алёрты; поддерживать выполнение SLA; интегрироваться с платформой инференса: автоматизировать развёртывание моделей; открывать доступ к моделям для пользователей в инфраструктуре Авито; взаимодействовать с DS-инженерами, погружаться в работу LLM, следить за развитием индустрии и внедрять новые технологии. Мы ждём, что вы: лидировали крупные стримы с высокой степенью неопределённости: собирали требования; планировали работу; декомпозировали задачи; отвечали за реализацию и мониторинг результатов; поддерживали критическую высоконагруженную инфраструктуру: настраивали мониторинг и алёрты; участвовали в дежурствах 24/7; программируете на Python или Go от 5 лет; имеете опыт в MLOps или LLMOps: поднимали и поддерживали инфраструктуру для автоматизации работы с ML- и LLM-моделями; понимаете базовые принципы работы LLM: чем prefill отличается от decode, зачем нужен KV-cache, что такое TTFT. Будет здорово, если вы: работали с LLM API и LLM Gateway или аналогичными решениями, например LiteLLM; создавали внутренние платформенные решения; работали с системами мониторинга LLM, например Langfuse или Opik; создавали и поддерживали административные интерфейсы для внутренних пользователей; работали с Kubernetes и GPU; работали с DWH, например ClickHouse или Trino. Работа у нас — это: расширенная забота о здоровье: ДМС, страхование жизни, онкострахование; сервис психологической поддержки, а также финансовые, юридические и ЗОЖ-консультации для вас и ваших близких; корпоративный спорт: групповые тренировки, велбиинг-приложение; спецпредложения от партнёров и скидки на сервисы Авито: бронирование жилья, услуги продвижения, ипотечное страхование; компенсация питания.

– Уверенно владеешь Python и понимаешь связанные с ним фреймворки: tf, pytorch, numpy, librosa, opencv.

Senior Python, алгоритмы, структуры данных, Big O, ООП. SQL, PostgreSQL, Git, юнит- и интеграционное тестирование.

Предстоит исследовать LLM и RAG, работать с неструктурированными данными, готовить датасеты и обучать chat- и embedding-модели. Среди задач также AI-инструменты и агенты для корпоративных продуктов.

- Развивать LLM-платформу как внутренний продукт: шлюз к моделям с учётом квот и стоимости, self-hosted инференс, выбор и оценка моделей (evals, бенчмарки), защита чувствительных данных, базовая инфраструктура для агентских сценариев. - Переводить продуктовые интеграции с внешних LLM на модели в собственном контуре и модели российских провайдеров.

Waymo is looking for a Quantitative Software Engineering Intern to research theories, evaluate results, and implement software.

Location: London Workplace: Hybrid ABOUT NIANTIC SPATIAL We are building for the >80% of economic activity that happens beyond the reach of screens. AI has learned from the text, images and video online, but it still lacks an accurate understanding of physical space. That’s one of the biggest unsolved problems in AI, and it’s the one we’re solving at Niantic Spatial. We build spatial AI models and services so people and machines can reason, learn and act safely in the physical world. We turn camera imagery, video, sensor data and archives into accurate, AI-readable representations of real places that robots can train in and machines can use to know exactly where they are. In physical AI, mistakes have real consequences. That’s why we build AI grounded in measured reality, not guesswork, so the people and machines relying on it can trust it to get things right. Our team built some of the world’s most widely used maps. Now we’re building something bigger: a living, measured record of the real world that machines can query, learn from and use to understand how places change over time. If you want to help AI understand the world we actually live in, come build with us. ABOUT THE ROLE We are looking for a Research Engineer to help develop our Geospatial Visual Localization technologies, which allows people and robots to accurately localize themselves anywhere on the planet. This is an end-to-end applied research role. You will develop new approaches to open problems alongside our researchers in 3D vision and machine learning, and publish your work at leading conferences. You will help build and train large models on large-scale multi-modal datasets, including our proprietary collection of 30 billion posed images and ship them into production, to be used by people and robots from all over the world. Our team works with heterogeneous captures from phones and other handheld devices, 360 cameras, vehicles, and drones, as well as aerial and satellite imagery, LiDAR, and external geographic data. Your focus will be to improve accuracy and robustness across urban and rural locations, cameras, seasons, weather, and capture quality. The system must remain reliable with noisy GPS, in GPS-denied environments, and with outdated reference maps. Each pose estimate comes with an uncertainty that our customers rely on, and keeping it well calibrated across these conditions is part of the work. WHAT YOU'LL DO - Design, implement, and evaluate novel visual localization algorithms, combining 3D vision and deep learning. - Train large models on multi-modal data at scale, and build the benchmarks that measure their accuracy and robustness. - Develop robust solutions for difficult real-world conditions: noisy or denied GPS, changing appearance, varying capture quality, large geographic extents, and imperfect reference maps. - Work side-by-side with leading researchers in visual localization, 3D computer vision, and mapping, as well as engineering and product teams, to identify the highest-impact problems and turn research into end-to-end solutions that operate at production scale. - Communicate results clearly through technical writing, design reviews, internal presentations, and publications. WHAT YOU'LL BRING - Strong grounding in 3D computer vision, deep learning, robotics, photogrammetry, or a closely related field. - Demonstrated experience with multi-view geometry and related problems, such as camera pose estimation, monocular depth estimation, SfM, SLAM, georeferencing, point-cloud registration, or sensor fusion. - Knowledge of transformer models for 3D vision, such as vision foundation models, feed-forward 3D reconstruction, or multi-view and multi-modal attention. - Knowledge of geospatial data and coordinate reference systems. - Ability to formulate ambiguous real-world problems, choose appropriate mathematical or learning-based tools, and evaluate solutions rigorously. - Strong Python skills and experience training deep learning models at scale, with libraries such as PyTorch, NumPy, and SciPy. - Strong software engineering skills, with the ability to turn research prototypes into reliable, scalable, and maintainable systems. - Experience taking an algorithm from research idea and experimental prototype through evaluation, benchmarking, and deployment on real-world data. - Clear written and verbal communication and the ability to collaborate across research and engineering disciplines. NICE TO HAVE - A PhD in computer vision, robotics, machine learning, photogrammetry, geodesy, remote sensing, or a related field, or equivalent research or industry experience. A PhD is not required. - A strong publication record or substantial active involvement in the research community, especially at venues such as CVPR, ICCV, ECCV, NeurIPS, ICLR, SIGGRAPH, SIGGRAPH Asia, 3DV, ICRA, RSS, or related conferences and journals. - Experience with aerial or satellite imagery, GNSS/GPS, IMUs, LiDAR data, geodesy, mapping systems, or large-scale spatial data. - Evidence of technical leadership, mentorship, or successful cross-functional delivery. COMPENSATION & BENEFITS Base salary: Competitive market rate. Compensation also includes an annual bonus, equity, and a comprehensive benefits package. LOCATION & WORK MODEL This role is based in our London office, with three days per week in office. INCLUSIVE APPLICATION We know the strongest candidates don't always tick every box. If you're excited about this role and believe you could do it well, we encourage you to apply even if your experience doesn't match every qualification listed - you may be exactly who we're looking for. EQUAL OPPORTUNITY Niantic Spatial is an equal opportunity employer. Individuals seeking employment at Niantic Spatial are considered without regard to race, color, ancestry, national origin, religion, creed, age, gender (including pregnancy, childbirth, breastfeeding or related medical conditions), marital status, physical or mental disability, medical condition, genetic information, military or veteran status, gender identity, gender expression, sexual orientation, or any other protected category under applicable laws. Niantic Spatial will also consider qualified applicants with criminal histories in accordance with applicable laws. Please contact your recruiter if you want to request an accommodation for the job application or interview process. CANDIDATE PRIVACY I understand that by submitting my job application, the information I provide as part of that application will be used in accordance with Niantic Spatial's Privacy Notice for Job Applicants and Candidates https://www.nianticspatial.com/applicant-privacy-notice.

Location: Remote - United States Reddit is a community of communities. It’s built on shared interests, passion, and trust, and is home to the most open and authentic conversations on the internet. Every day, Reddit users submit, vote, and comment on the topics they care most about. With 100,000+ active communities and approximately 130 million daily active unique visitors, Reddit is one of the internet’s largest sources of information. For more information, visit www.redditinc.com . The opportunity Build the machine learning capabilities that help advertisers understand what they could achieve on Reddit and make better investment decisions. You will lead models that estimate how campaign outcomes could change with different budgets, bids, audiences, creative performance, or objectives. The work connects supply and demand forecasting, audience availability, response prediction, and marketplace simulation to help advertisers compare alternatives and recognize valuable opportunities they may be missing. This is a hands-on technical leadership role with ownership of modeling direction, evaluation standards, and reusable production capabilities across advertiser planning and recommendations. What you’ll do Lead forecasting and scenario modeling. Translate advertiser questions into models of delivery, reach, performance, and investment tradeoffs. Set technical direction and guide execution across the team. Connect inventory, demand, and response. Combine forecasts of available advertising opportunities, audience activity, marketplace competition, and predicted response to estimate achievable delivery and performance. Estimate outcomes under alternative choices. Develop models that account for changes in budget, bid, audience, and optimization objective, including interactions among these choices, auction dynamics, and campaign constraints. Quantify unrealized opportunity. Incorporate creative-performance estimates from partner teams to assess how stronger creative could have improved campaign results. Translate those inputs into potential gains in delivery, efficiency, or advertiser value. Build models that generalize. Apply probabilistic forecasting, multitask learning, transfer learning, hierarchical modeling, or related approaches to handle sparse segments, cold start, delayed feedback, and changing conditions. Establish trustworthy comparisons. Evaluate numerical accuracy, calibration, uncertainty, and cohort-level performance. Define supported scenarios and identify when additional evidence or experimentation is needed. Close the learning loop. Compare recorded predictions with mature outcomes, diagnose systematic errors, and validate scenario estimates through experiments and observed results. Own production delivery and technical leadership. Partner with engineers on reliable training, serving, monitoring, and iteration. Lead designs, mentor engineers, and collaborate with Product, Data Science, Ads Delivery, Audience, and Creative teams. What we’re looking for A track record of developing and shipping predictive ML systems in advertising, marketplaces, recommendations, search, demand forecasting, or similarly complex domains. Strong foundations in statistical learning, probability, and optimization, with experience modeling large-scale behavioral or transactional data. Depth in one or more relevant areas: probabilistic or hierarchical forecasting, calibrated conversion/value prediction, multitask or transfer learning, delayed-feedback or survival modeling, or response modeling . Strong judgment in evaluation, including temporal validation, calibration, segment-level performance, and the assumptions required to estimate outcomes under changed conditions. Experience addressing sparse observations, cold start, seasonality, distribution shift, selection bias, or partially observed labels. Strong programming and software engineering skills, including experience building and operating production ML systems. Demonstrated ability to lead ambiguous technical initiatives, make practical tradeoffs, and develop other engineers. Preferred experience Complementary depth in one or more of: Demand, supply, and inventory forecasting: Advertising inventory, marketplace demand, traffic, retail demand, or capacity forecasting. Relevant methods include probabilistic time-series models, hierarchical/global forecasting, external-signal modeling, and multi-horizon prediction. Auction and investment modeling: Auction simulation or replay, conditional win-rate and price models, bid/budget response curves, or constrained optimization. Scenario evaluation: Counterfactual or off-policy evaluation, sensitivity analysis, uncertainty propagation, or composition of multiple predictive models. Reach and audience estimation: Population and overlap estimation, probabilistic reach/frequency models, sampling correction, or saturation modeling. Advanced predictive methods: Mixture-of-experts, distributional or quantile prediction, temporal sequence models, or state-space models. What success looks like Accurate and calibrated forecasts across priority advertiser populations; credible comparisons of supported campaign alternatives; reusable capabilities across planning and recommendations; and evidence that advertisers identify valuable opportunities and make better investment decisions. Benefits: Comprehensive Healthcare Benefits and Income Replacement Programs 401k with Employer Match Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support Family Planning Support Gender-Affirming Care Mental Health & Coaching Benefits Flexible Vacation & Paid Volunteer Time Off Generous Paid Parental Leave #LI-AK1 Pay Transparency: This job posting may span more than one career level. In addition to base salary, this job is eligible to receive equity in the form of restricted stock units, and depending on the position offered, it may also be eligible to receive a commission. Additionally, Reddit offers a wide range of benefits to U.S.-based employees, including medical, dental, and vision insurance, 401(k) program with employer match, generous time off for vacation, and parental leave. To learn more, please visit https://www.redditinc.com/careers/ . To provide greater transparency to candidates, we share base salary ranges for all US-based job postings regardless of state. We set standard base pay ranges for all roles based on function, level, and country location, benchmarked against similar stage growth companies. Final offer amounts are determined by multiple factors including, skills, depth of work experience and relevant licenses/credentials, and may vary from the amounts listed below. The base salary range for this position is: $230,000 — $322,000 USD In select roles and locations, the interviews will be recorded, transcribed and summarized by artificial intelligence (AI). You will have the opportunity to opt out of recording, transcription and summarization prior to any scheduled interviews. During the interview, we will collect the following categories of personal information: Identifiers, Professional and Employment-Related Information, Sensory Information (audio/video recording), and any other categories of personal information you choose to share with us. We will use this information to evaluate your application for employment or an independent contractor role, as applicable.  We will not sell your personal information or disclose it to any third party for their marketing purposes.  We will delete any recording of your interview promptly after making a hiring decision.  For more information about how we will handle your personal information, including our retention of it, please refer to our Candidate Privacy Policy for Potential Employees and Contractors . Reddit is proud to be an equal opportunity employer, and is committed to building a workforce representative of the diverse communities we serve.  Reddit is committed to providing reasonable accommodations for qualified individuals with disabilities and disabled veterans in our job application procedures. If, due to a disability, you need an accommodation during the interview process, please let your recruiter know.

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

Location: Paris; London Workplace: Hybrid ABOUT MISTRAL Mistral provides full-stack AI solutions: from frontier models to developer tools, applications, and compute. We partner with enterprises tackling the hardest problems across high-stakes industries like finance, manufacturing, defense, healthcare, and the public sector, co-creating customized AI systems that they can run on their terms. We are a dynamic, collaborative team passionate about AI and its potential to transform society. Our diverse workforce thrives in competitive environments and is committed to driving innovation. Our teams are distributed between Europe, North America, Asia and the Middle East. We are creative, low-ego and team-spirited. ABOUT THE JOB Mistral AI is seeking Applied Scientists and Research Engineers focused on time series. You will design, train, and deploy state-of-the-art time-series foundation models, and apply them to the most demanding forecasting problems in industry, from power grids and plants to trading desks. Timestral, our family of time-series foundation models, ranked first on amongst all benchmarks at release (tech report https://arxiv.org/pdf/2610.37585v1: https://arxiv.org/pdf/2610.37585v1 ). The role has two sides: building the next generations of Timestral with our Science team, and taking them into production with our industrial and financial clients, adapted to their data. Working cross-functionally with both external and internal science, engineering, and product teams you will deliver high-impact AI solutions. We are made of researchers, engineers, product builders, ex-founders who love tackling the most complex real world problems in the industry. WHAT YOU WILL DO - Run pre-training, post-training and deploy state of the art time-series foundation models on clusters with thousands of GPUs. - Generate and curate large-scale time-series datasets, real and synthetic, and build evaluators and benchmarks for forecasting and other time-series tasks. - Push the frontier of Timestral: architectures, training recipes, scaling, and new capabilities such as anomaly detection, classification and embeddings. - Fine-tune and adapt Timestral on client data, in the cloud or on-premise, and benchmark it against the client's existing models. - Develop the necessary tools and frameworks to facilitate data generation, model training, evaluation and deployment. - Collaborate with cross-functional teams to tackle complex use cases, including agents that combine Timestral with Mistral LLMs. - Manage research projects and communications with client research teams. ABOUT YOU - Fluent in English with excellent communication skills, at ease explaining complex technical concepts to both technical and non-technical audiences. - Expert with PyTorch or JAX. - Solid knowledge of modern deep learning and large language models: architectures, pre-training, post-training and evaluation. - Experience pre-training or fine-tuning time-series foundation models or large transformer models at scale. - Strong background in time-series modelling: probabilistic forecasting, multivariate series and covariates, and classical baselines (statistical models, gradient boosting). - Not afraid of contributing to a big codebase; finds their way around independently with little guidance. - Writes clean, readable, high-performance, fault-tolerant Python code. - Doesn't need roadmaps: moves independently and is comfortable with uncertainty. - Low-ego, collaborative and eager to learn. - Track record of success through personal projects, professional projects or academia. NICE TO HAVE - Ex-founder or builder - PhD / Master in a relevant field (Machine Learning, Statistics, Applied Mathematics, Physics). Exceptional candidates from other backgrounds welcome. - Research on time-series foundation models: pre-training, scaling, synthetic data or evaluation. - Hands-on experience training or fine-tuning LLMs, building agents and tool use, or models that combine text and time series. - Industry experience in energy, industrial operations, telecom or finance, or experience deploying models for industrial clients. - Experience working on large code bases or large machine learning models at scale. - Contributions to a large open-source or industry codebase. - Publications in top academic journals or conferences. - Enjoys improving existing code: typing, tests, CI pipelines. WHAT WE OFFER We offer a comprehensive benefits package designed to support your well-being, growth, and work-life balance. Benefits vary by country and may include healthcare coverage, parental leave, retirement plans, relocation support, wellness programs, meal and transportation allowances, and other location-specific perks. For the most up-to-date details on benefits available in your location, please refer to our Benefits page https://app.notion.com/p/mistralai/Benefits-at-Mistral-36e6ba59a7fe836b93dd01737fcc27ef?source=copy_link. PRIVACY POLICY Your privacy matters to us. You can learn more about how we handle your personal data in our Applicant Privacy Policy https://legal.mistral.ai/terms/applicant-privacy-policy.

We are looking for a Senior Machine Learning Engineer to develop, deploy, and maintain machine learning and AI solutions for advanced financial data processing and reconciliation platforms. You will work with large and complex financial transaction datasets to build solutions for transaction matching, reconciliation, and exception handling.

Ищем специалиста, который умеет не только разрабатывать ML-модели, но и доводить их до production, отвечать за их работу и влиять на реальные бизнес-решения.

-развёртыванием ML-моделей и AI-сервисов, локальных моделей (LLM, ML-модели) в различных средах. -управлением версиями моделей и их хранением (NFS/MinIO).

В роли предстоит развивать DWH и ML-платформу, строить пайплайны от сбора данных до деплоя моделей, настраивать мониторинг и CI/CD. Сейчас усиливаем Data/ML-направление и ищем Senior MLOps / DataOps инженера, который поможет выстроить стабильную и масштабируемую инфраструктуру для работы с данными и моделями.

Задачи: — Участвовать в разработке моделей для продуктивной среды — Строить модели машинного обучения, улучшать качество — Писать код, проводить code-review

Location: Москва Мы команда DS Департамент «Занять и сберегать» (Управление искусственного интеллекта и исследования данных), мы умеем быстро и качественно проверять гипотезы, строить ML-модели, консультировать бизнес, проводить A/B-тесты и защищать финансовые результаты. Вас встретит дружная команда, мощные кластеры, много данных и самое главное – интересные, амбициозные и сложные задачи. Мы разрабатываем и внедряем решения от идеи до вывода в промышленную эксплуатацию, включая исследования источников, построение витрин, разработку моделей и встраивание в существующую инфраструктуру исполнения моделей. У нас вы сможете поработать с действительно большими данными и внедрить решения масштаба страны, используя самый современный технологический стек. ### Обязанности Ключевые направления, в котором вы будете работать: * ценообразование * uplift-моделирование * задачи ранжирования * модели склонности * look-a-like * response модели * работа с регрессионными таргетами * кластерный и факторный анализ * матчинг. Требования * студент последних курсов университета * понимание классического ML на хорошем уровне * знания статистики и теории вероятностей * хорошие знания Python/SQL/Bash/Spark. Будет плюсом: * опыт работы с Transformers * успехи в ML-соревнованиях, хакатонах * опыт в A/B тестах. ### Условия * работа в удобном и современном офисе недалеко от станции метро Кутузовская * корпоративный спортзал и зоны отдыха * более 400 образовательных программ СберУниверситета для профессионального и карьерного развития * подписка Прайм с возможностью совместного использования на трёх близких * вознаграждение за рекомендацию друзей в команду Сбера.