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Location: Larnaca, Cyprus This is an onsite role, and we're seeking someone who will work directly from the client's office in Nicosia. Relocation conditions: A 2,000-euro relocation bonus (3,000 euros if relocating with family). This project focuses on enabling enterprise grade AI and GenAI capabilities across banking systems by implementing robust MLOps practices and cloud native infrastructure. The goal is to standardize how AI models are deployed, monitored, and governed in a secure and compliant way. Design and maintain CI/CD and MLOps pipelines for AI/ML solutions. Deploy and operate AI workloads on Azure Machine Learning, Azure OpenAI, AKS, Azure Functions, and Azure DevOps. Automate infrastructure using Terraform, Bicep, or ARM templates. Implement secure deployment practices: RBAC, Key Vault, managed identities, private endpoints, network isolation. Set up monitoring, logging, alerting, and operational support for AI services and model endpoints. Support model versioning, release management, rollback, and production governance. Work closely with data scientists, AI engineers, cloud architects, security, and banking IT teams. Ensure solutions meet banking standards for security, auditability, resilience, and compliance. 7+ years of DevOps, Cloud Engineering, Platform Engineering, or related experience. Strong experience with Azure DevOps, CI/CD pipelines, Git, YAML, and release management. Experience with Azure Machine Learning and/or MLOps platforms. Practical knowledge of Azure OpenAI, GenAI applications, or AI service deployment. Experience with Docker, Kubernetes, and AKS. Experience with Infrastructure as Code: Terraform, Bicep, or ARM. Good understanding of monitoring tools such as Azure Monitor, Application Insights, Log Analytics, Grafana. Scripting skills in PowerShell, Python, or Bash. Experience working in regulated enterprise environments, preferably banking, fintech, or insurance. Experience with RAG pipelines, prompt orchestration, MLflow, or Prompt Flow. Knowledge of Microsoft data platforms: Databricks, Synapse, Data Factory, Microsoft Fabric, Purview. Understanding of AI governance, model risk, data privacy, and audit requirements. Experience with Microsoft security tools such as Defender for Cloud or Sentinel.

Location: Hybrid About Us At Cloudflare, we are on a mission to help build a better Internet. Today the company runs one of the world’s largest networks that powers millions of websites and other Internet properties for customers ranging from individual bloggers to SMBs to Fortune 500 companies. Cloudflare protects and accelerates any Internet application online without adding hardware, installing software, or changing a line of code. Internet properties powered by Cloudflare all have web traffic routed through its intelligent global network, which gets smarter with every request. As a result, they see significant improvement in performance and a decrease in spam and other attacks. Cloudflare was named to Entrepreneur Magazine’s Top Company Cultures list and ranked among the World’s Most Innovative Companies by Fast Company. At Cloudflare, we’re not looking for people who wait for a polished roadmap; we’re looking for the builders who see the cracks in the Internet that everyone else has simply learned to live with. We value candidates who have the instinct to spot a "normalized" problem and the AI-native curiosity to create a solution using the latest tools. Our culture is built on iteration, leveraging AI to ship faster today to make it better tomorrow, while ensuring that every improvement, no matter how small, is shared across the team to lift everyone up. If you’re the type of person who values curiosity over bureaucracy, and that AI is a partner in solving tough problems to keep the Internet moving forward, you’ll fit right in. Available Locations: Austin, TX | New York, NY | San Francisco, CA or London, UK (Hybrid) About the Role You’ll help define how machine learning models run across Cloudflare’s global network, from frontier open LLMs and real-time voice models to customer-deployed models served on heterogeneous GPUs and next-generation accelerators. You’ll work with systems engineers, product teams, hardware partners, and AI/ML engineers to bring models into production with low latency, strong reliability, and efficient resource use. This role combines applied ML, inference optimization, evaluation, and production engineering, with a focus on benchmarking models, improving serving performance, validating quality, and building tooling that helps Cloudflare and its customers ship AI applications at Internet scale. Responsibilities Develop, optimize, and productionize machine learning models for Cloudflare’s serverless inference platform, with a focus on performance, reliability, and model quality. Build benchmarking and evaluation frameworks to measure latency, throughput, cost efficiency, and model behavior across LLMs, speech, vision, and other model families. Improve inference performance through quantization, batching, caching, model compilation, runtime tuning, and accelerator-aware optimization. Partner with systems engineers to integrate models into Cloudflare’s distributed inference infrastructure across a heterogeneous fleet of GPUs and next-generation accelerators. Drive improvements to model deployment workflows, including validation, rollout safety, observability, regression testing, and operational readiness. Collaborate with product and engineering teams to translate customer requirements into scalable ML capabilities for Workers AI. Mentor engineers, contribute to technical direction, and raise the quality bar for production ML engineering practices across the team. Desirable Skills, Knowledge, and Experience Experience building, optimizing, and operating machine learning models in production environments. Strong proficiency with Python and modern ML frameworks such as PyTorch, TensorFlow, JAX, or equivalent. Hands-on experience with inference optimization techniques for large-scale models, including quantization, batching, caching, compilation, and serving runtime tuning. Experience with large-scale inference serving frameworks or runtimes such as SGLang, vLLM, TensorRT-LLM, ONNX Runtime, Triton, llama.cpp, or similar. Familiarity with LLMs, speech models, vision models, embeddings, multimodal models, retrieval-augmented generation, or other modern deep learning architectures. Experience optimizing models for GPUs or specialized accelerators. Strong understanding of production ML concerns, including evaluation, monitoring, model regressions, rollout safety, and reliability. Ability to work across ML and systems boundaries, including familiarity with distributed systems, networking, or serverless platforms. Track record of leading complex technical projects and mentoring other engineers. Bonus Points Experience contributing to open source ML tooling, model serving frameworks, or inference runtimes. Compensation Compensation may be adjusted depending on work location. F or Bay Area based hires: Estimated annual salary of $194,000 - $266,000 For New York City, New Jersey, Washington, Washington DC, and California (excluding Bay Area) based hires: Estimated annual salary of $185,000 - $254,000 Equity This role is eligible to participate in Cloudflare’s equity plan. Benefits Cloudflare offers a complete package of benefits and programs to support you and your family.  Our benefits programs can help you pay health care expenses, support caregiving, build capital for the future and make life a little easier and fun!  The below is a description of our benefits for employees in the United States, and benefits may vary for employees based outside the U.S. Health & Welfare Benefits Medical/Rx Insurance Dental Insurance Vision Insurance Flexible Spending Accounts Commuter Spending Accounts Fertility & Family Forming Benefits On-demand mental health support and Employee Assistance Program Global Travel Medical Insurance Financial Benefits Short and Long Term Disability Insurance Life & Accident Insurance 401(k) Retirement Savings Plan Employee Stock Participation Plan Time Off Flexible paid time off covering vacation and sick leave Leave programs, including parental, pregnancy health, medical, and bereavement leave What Makes Cloudflare Special? We’re not just a highly ambitious, large-scale technology company. We’re a highly ambitious, large-scale technology company with a soul. Fundamental to our mission to help build a better Internet is protecting the free and open Internet. Project Galileo : Since 2014, we've equipped more than 2,400 journalism and civil society organizations in 111 countries with powerful tools to defend themselves against attacks that would otherwise censor their work, technology already used by Cloudflare’s enterprise customers--at no cost. Athenian Project : In 2017, we created the Athenian Project to ensure that state and local governments have the highest level of protection and reliability for free, so that their constituents have access to election information and voter registration. Since the project, we've provided services to more than 425 local government election websites in 33 states. 1.1.1.1 : We released 1.1.1.1 to help fix the foundation of the Internet by building a faster, more secure and privacy-centric public DNS resolver. This is available publicly for everyone to use - it is the first consumer-focused service Cloudflare has ever released. Here’s the deal - we don’t store client IP addresses never, ever. We will continue to abide by our privacy commitment and ensure that no user data is sold to advertisers or used to target consumers. Sound like something you’d like to be a part of? We’d love to hear from you! Please note that applicants who progress to the offer stage of the interview process may be asked to attend an in-person interview within one of the Cloudflare Offices or Cloudflare Hubs.  More details about this will be available at that stage of the interview process. This position may require access to information protected under U.S. export control laws, including the U.S. Export Administration Regulations. Please note that any offer of employment may be conditioned on your authorization to receive software or technology controlled under these U.S. export laws without sponsorship for an export license. Cloudflare is proud to be an equal opportunity employer.  We are committed to providing equal employment opportunity for all people and place great value in both diversity and inclusiveness.  All qualified applicants will be considered for employment without regard to their, or any other person's, perceived or actual race, color, religion, sex, gender, gender identity, gender expression, sexual orientation, national origin, ancestry, citizenship, age, physical or mental disability, medical condition, family care status, or any other basis protected by law. We are an AA/Veterans/Disabled Employer. Cloudflare provides reasonable accommodations to qualified individuals with disabilities.  Please tell us if you require a reasonable accommodation to apply for a job. Examples of reasonable accommodations include, but are not limited to, changing the application process, providing documents in an alternate format, using a sign language interpreter, or using specialized equipment.  If you require a reasonable accommodation to apply for a job, please contact us via e-mail at hr@cloudflare.com or via mail at 101 Townsend St. San Francisco, CA 94107.

Location: Worldwide We are looking for a Principal Research Scientist to lead the development of foundation models for Risk & Decisioning, the team that powers credit decisions for Plata’s 3M+ customers. You will define the technical direction of our modeling work and take models from research all the way to production, partnering closely with Risk, Product, and Data teams on priorities and roadmap. This is a hands-on principal role: you will set the engineering standard for the team, build production-grade models yourself, and see their impact directly in cost of risk, approval rates, and portfolio NPV. Challenges that await you: Build an end-to-end foundation model over financial event sequences - transactions, credit bureau data, and in-app behavioral events  with subsequent fine-tuning for downstream business tasks: underwriting (PD), credit limit strategy, fraud detection, collections, and propensity models Drive technical decisions end-to-end: methodology → implementation → performance and latency → robustness, interpretability, and regulatory compliance Take models from research to production: training infrastructure, evaluation frameworks, model serving, latency/cost optimization, and monitoring Research state-of-the-art approaches in the industry, publish your own work, and speak at leading conferences Mentor senior engineers and scientists; own technical standards for model development across the team (design reviews, evaluation methodology, deployment practices). Communicate results clearly to cross-functional stakeholders: product, risk, business, and leadership What makes you a great fit: Proven experience applying deep learning to sequential data - transformer architectures on event/transaction sequences strongly preferred, but not required Strong foundation in mathematical statistics and probability theory Deep understanding of machine learning algorithms (GBM, MLP, CNN, RNN, Transformers, etc.) Experience taking large models to production: distributed training, model serving, latency/cost trade-offs Ability to strike a reasonable balance between solution complexity and practical applicability Strong mathematical or technical education - degree in mathematics, physics, or CS from a top technical university Kaggle Competitions Master/Grandmaster or equivalent (a plus); experience developing models in banking or consumer lending (a plus) Strong communication skills Our ways of working: Innovative Spirit: a commitment to creativity and groundbreaking solutions Honest Feedback: valuing open, transparent communication Supportive Team: a strong, collaborative community Celebrating Achievements: recognizing our wins together High-Tech Environment: a team full of smart and revolutionary people who date to challenge the status quo of incumbent finances Our benefits: Relocation support to one of our hubs - Mexico, Cyprus, Spain with assistance for the employee and their family Flexible work from one of our offices or remote Healthcare coverage Education budget: language lessons, professional training and certifications Wellness budget : mental health and fitness activity reimbursements Vacation Policy: Annual leave and paid sick leave as per applicable policies

Meet Arago and the Aragonians Arago is an AI and computer hardware company whose mission is to drive the course of history forward. We do so by accelerating breakthroughs at the intersection of AI and semiconductors. Founded in 2024 by AI researchers and physicists with deep expertise in photonics, electronics, software, mathematics, and machine learning, Arago brings together a lean team of engineers and scientists from the world’s top companies and research labs. Composed of nine nationalities and operating from hubs in France, North America, and Israel, we believe in great science and fast achievements. Our work is guided by these core principles: Do great things: we deliver work we’re proud to sign our name to. High velocity: speed matters. We move quickly, one step at a time. One unit: we’re all in this together, with relationships grounded in trust, respect, and camaraderie. Arago is backed by executives from Apple, Arm, Nvidia, Microsoft, and Hugging Face, as well as prominent US and European deeptech venture firms and exited founders. Задачи: — Develop Arago’s quantization strategy for modern AI models, including precision studies, numerical format exploration, and model-level techniques to preserve accuracy under constrained precision. — Study the impact of analog noise on the performance and inference accuracy of machine learning models, including language models, diffusion, video generation, and world models. — Develop simulation tools to model analog behavior with minimal computational overhead. — Build and maintain Arago's internal experimentation pipelines for precision and performance studies on both physical and emulated prototypes. — Act as a technical point of contact between clients and Arago: analyze, port, and optimize client models onto Arago hardware, balancing throughput and accuracy. — Collaborate with the software and digital architecture teams to support architectural enhancements based on the operator content of frontier model architectures. Требования: — Background in mathematics, physics, computer science, or a related quantitative field. — Strong interest and skills in applied mathematics and statistics. — Strong experience with quantization of machine learning and AI models. — Strong understanding of modern model architectures, including language models, diffusion models, video generation models, and/or world models. — Experience studying the impact of numerical precision, noise, or approximation on model accuracy. — Experience with inference performance optimization. — Good knowledge of computer architecture, GPU architecture, or parallel computing. — Ability to build rigorous experimentation pipelines and translate research insights into practical engineering decisions. — GitHub profile with recent and relevant projects. — Language: English at a proficient level. French is a plus. Условия: — Competitive cash compensation, with final package based on location, experience, and the pay of team members in similar positions. — Meaningful stock option plan offered at the earliest stage of the company (included in the majority of full time offers). — Relocation bonus and coverage of moving expenses for relocation within 20 minutes of the company’s location. — Healthcare coverage (including family-friendly options), pension contributions, professional development support, and 25 days of PTO, in addition to public holidays. — Ownership of a key technical domain, with significant vertical and/or horizontal growth opportunities, based on performance and individual drive. — Reimbursement of 50% of the public transport subscription fee. — A high-paced, multicultural (with 10 nationalities), and engineering-led environment.

Задачи: — Разработка с нуля мультиязычного голосового ассистента (обязательно: английский + турецкий) для мобильного приложения и личного кабинета клиента. — Проектирование архитектуры: оценка решений «готовые API vs локальные модели» и последующий вывод open-source LLM в продакшн. — Интеграция AI-сервисов с текущим бэкендом платформы (основной стек: PHP/Laravel, есть элементы legacy на Yii/C#). — На перспективу: задачи на стыке Computer Vision (умный процессинг оплат) и телематики (анализ данных с датчиков топлива, предсказание поломок спецтехники). Требования: — Отличный Python (коммерческий опыт Middle+ / Senior). — Понимание архитектуры бэкенда (будет плюсом опыт соприкосновения с PHP/Laravel или Go). — Опыт разработки голосовых решений (STT/TTS, ASR/NLP), опыт развертывания и кастомизации локальных моделей (LLM) на своих серверах в проде. — English: уровень B2 и выше (чтение документации, работа с зарубежными AI-ресурсами и проектами). Условия: — Локация: Алматы, офис (для сильных кандидатов возможен гибрид или удаленка). — Занятость: Full-time.

Location: New York; San Francisco ABOUT US: AI NEEDS A NEW INFRASTRUCTURE LAYER. WE'RE BUILDING IT AT MODAL. Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now. Our customers include category-defining companies like Ramp https://modal.com/blog/how-ramp-built-a-full-context-background-coding-agent-on-modal, Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale. We've passed $1B in annualized run rate, up 15x from a year ago. In October 2026 we raised a $750M Series D led by Accel at a $15.75B valuation. Our team includes creators of popular open-source projects (e.g.,Seaborn https://github.com/mwaskom/seaborn,Luigi https://github.com/spotify/luigi), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience. THE ROLE: We’re building a platform that covers the whole life of an LLM: training it, deploying it, and observing it in production. We already run multi-node training, elastic inference, sandboxes, and distributed volumes, and we control the infrastructure underneath. We’re looking for research depth in post-training to sit alongside our systems and product work. We are looking for research scientists with a strong track record in reinforcement learning, machine learning, and foundation models, including large language and multimodal models, to join our research team. WHAT YOU’LL WORK ON: - Improve existing methods and develop new techniques for large-scale model training, optimization, and inference - Extend models to long-context and long-horizon tasks - Improve inference-time efficiency, reliability, and robustness in high-stakes real-world deployments YOU MAY BE A FIT IF: - You have a PhD in computer science, machine learning, or a related field - You have a demonstrated record of research accomplishments in reinforcement learning, machine learning, foundation models, or related fields - You have experience with large-scale training and inference infrastructure, including distributed systems and multi-node GPU clusters - You’ve developed, trained, optimized, or deployed state-of-the-art large-scale models - You have first-author publications at leading venues such as NeurIPS, ICML, ICLR, CoRL, CVPR, UAI, JMLR, or TMLR - You have a mission-driven mindset and a strong desire to translate research advances into meaningful product impact - You have a collaborative spirit and can work effectively across research and engineering teams Candidates with a master’s degree and significant research or industry experience will also be considered.

Location: Paris; London ABOUT MISTRAL Mistral provides full-stack AI solutions: from frontier models to developer tools, applications, and compute. We partner with enterprises tackling the hardest problems across high-stakes industries like finance, manufacturing, defense, healthcare, and the public sector, co-creating customized AI systems that they can run on their terms. We are a dynamic, collaborative team passionate about AI and its potential to transform society. Our diverse workforce thrives in competitive environments and is committed to driving innovation. Our teams are distributed between Europe, North America, Asia and the Middle East. We are creative, low-ego and team-spirited. THE ROLE Mistral AI is seeking Applied Scientist Interns and Research Engineer Interns to drive innovative research and collaborate with clients on complex research projects. You will develop SOTA models across different modalities such as text, image, and speech. By developing novel methods and research ideas you will apply these models across a diverse set of use cases and domains. Working cross-functionally with both external and internal science, engineering, and product teams you will deliver high-impact AI solutions that turn the needle. WHAT YOU WILL DO - Run pre-training, post-training and deploy state of the art models on clusters with thousands of GPUs. - Generate and curate data for pre-training and post-training, working on evaluations and making sure the model's performance beats expectations. - 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 using agents and retrieval pipelines. - Manage research projects and communications with client research teams. WHAT WE'RE LOOKING FOR - 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. - 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 navigates uncertainty with confidence. - Low-ego, collaborative and eager to learn. - Track record of success through personal projects, professional projects or academia. NICE TO HAVE - Pursuing a PhD / Master in a relevant field (Mathematics, Physics, Machine Learning) — exceptional candidates from other backgrounds welcome. - Preferred duration: at least 6 months. Priority to candidates about to finalise their studies. - Research experience in agents, multimodal models, world models, robotics, diffusion models, cybersecurity, 3D understanding/generation, or time-series analysis. - Contributions to a large open-source or industry codebase. - Publications in top academic journals or conferences. - Enjoys improving existing code: typing, tests, CI pipelines. Location & work authorization: This internship is available only in France or the UK. Please apply only if you have the required work authorization to complete the internship in the relevant country for its full duration. WHAT WE OFFER We offer a comprehensive benefits package designed to support your well-being, growth, and work-life balance. Benefits vary by country and may include healthcare coverage, parental leave, retirement plans, relocation support, wellness programs, meal and transportation allowances, and other location-specific perks. For the most up-to-date details on benefits available in your location, please refer to our Benefits page https://app.notion.com/p/mistralai/Benefits-at-Mistral-36e6ba59a7fe836b93dd01737fcc27ef?source=copy_link. PRIVACY POLICY Your privacy matters to us. You can learn more about how we handle your personal data in our Applicant Privacy Policy https://legal.mistral.ai/terms/applicant-privacy-policy.

Location: San Francisco, CA About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the Role We're seeking an exceptional Research Engineer to join our Life Sciences team at Anthropic. Our team is organized around the north star goal of accelerating progress in the life sciences, from early discovery through translation, by an order of magnitude. Our team likes to think across the whole model stack. In this role, you'll combine your deep expertise in machine learning engineering to develop novel evaluation frameworks and training strategies that push the frontier of what AI can achieve in biology. You'll work at the intersection of cutting-edge AI and the biological sciences, developing rigorous methods to measure and improve model performance on complex scientific tasks. You'll collaborate closely with world-class researchers and engineers to build AI systems that can engage in all phases of research and development, while maintaining our commitment to safety and beneficial impact. Previous experience in life sciences is welcome, but not required for this role. Minimum Qualifications Demonstrated experience training and evaluating large language models Proficiency in Python and familiarity with modern ML development practices Experience building and managing data pipelines for large-scale datasets Comfortable navigating ambiguity and developing solutions in rapidly evolving research environments Strong written and verbal communication skills, with the ability to work independently while collaborating effectively across cross-functional teams Preferred Qualifications 8+ years of machine learning experience Prior work experience in AI and biology, including graduate studies (molecular biology, biochemistry, computational biology, or related fields) Experience working with large-scale biological datasets Published research or practical experience in scientific AI applications or long-horizon reasoning Background in reinforcement learning and/or pretraining Knowledge of containerization technologies (e.g., Docker, Kubernetes) and cloud deployment at scale Demonstrated ability to work across multiple domains, such as language modeling, systems engineering, and scientific computing Contributions to open-source scientific software or databases The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: $350,000 — $500,000 USD Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. For roles performing work in California: We will consider for employment all qualified applicants, including those with arrest or conviction records or criminal histories, in a manner consistent with the requirements of applicable state and local laws, including the San Francisco Fair Chance Ordinance, the City of Los Angeles’ Fair Chance Initiative for Hiring Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed.  Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings. How we're different We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences. Come work with us! Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.

Location: Hybrid About Us At Cloudflare, we are on a mission to help build a better Internet. Today the company runs one of the world’s largest networks that powers millions of websites and other Internet properties for customers ranging from individual bloggers to SMBs to Fortune 500 companies. Cloudflare protects and accelerates any Internet application online without adding hardware, installing software, or changing a line of code. Internet properties powered by Cloudflare all have web traffic routed through its intelligent global network, which gets smarter with every request. As a result, they see significant improvement in performance and a decrease in spam and other attacks. Cloudflare was named to Entrepreneur Magazine’s Top Company Cultures list and ranked among the World’s Most Innovative Companies by Fast Company. At Cloudflare, we’re not looking for people who wait for a polished roadmap; we’re looking for the builders who see the cracks in the Internet that everyone else has simply learned to live with. We value candidates who have the instinct to spot a "normalized" problem and the AI-native curiosity to create a solution using the latest tools. Our culture is built on iteration, leveraging AI to ship faster today to make it better tomorrow, while ensuring that every improvement, no matter how small, is shared across the team to lift everyone up. If you’re the type of person who values curiosity over bureaucracy, and that AI is a partner in solving tough problems to keep the Internet moving forward, you’ll fit right in. Available Locations: Austin, TX - Hybrid About the team The Data Intelligence & Analytics organization builds the core data platform and internal products that power decision-making across the company. We design and operate large-scale data systems, own the company’s data lake, ingestion infrastructure, and platform tooling, and develop end-to-end applications that transform complex datasets into fast, reliable, business-critical products used daily by go-to-market, product, and engineering teams. Our work sits at the intersection of data platforms, distributed systems, and product development, giving engineers the opportunity to own meaningful problems across the stack and build systems that truly run the business. About the role We are looking for a visionary and hands-on Lead Machine Learning Engineer to join our Austin team. In this role, you will be the principal architect behind the next generation of our unified AI/ML platform, designing and building the infrastructure that powers everything from traditional predictive models to generative AI, large language models (LLMs), and autonomous agent frameworks. You will own the end-to-end technical strategy, blueprint, and execution of scalable backend services and data pipelines that support AI-driven applications across go-to-market, engineering, and product teams. Because our products are initiated and owned entirely within the team, you will drive the vision from initial requirements and system design to global deployment, optimization, and long-term evolutionary ownership. What you'll do Architect and evolve a highly scalable, multi-tenant AI/ML platform that seamlessly unifies traditional ML (classification, regression, forecasting) and Generative AI/LLM orchestration. Design and implement robust production-grade AI Agents and Advanced Chatbots. Build reliable execution environments for Multi-Agent Systems, including state management, long-term memory architectures, and Model Context Protocol (MCP) server integrations. Build high-throughput, low-latency application backends and orchestration layers. Partner closely with data, platform, and full-stack engineers to ensure seamless feature delivery and reliable production operations. Act as a technical anchor for the Data Science team – enforcing rigorous engineering standards, leading design and security reviews, evaluating build-vs-buy decisions, and mapping business requirements to robust technical designs. Evaluate trade-offs and drive adoption of modern AI infrastructure tools, optimized embedding pipelines, vector databases, and serverless compute paradigms (such as Workers AI).\ Must-Have Skills Extensive experience as a Senior or Lead ML Engineer, with a proven track record of architecting and operating production-grade ML platforms, services  and distributed backends. Strong competency in Traditional ML lifecycles (feature stores, training pipelines, model monitoring) alongside deep experience in Generative AI patterns (RAG pipelines, context engineering, fine-tuning, guardrailing, and agentic AI systems). Mastery of Python and robust experience with modern backend ecosystems. Familiarity with (or willingness to collaborate on) full-stack technologies like React and TypeScript is highly valued. A builder's mindset. You are comfortable navigating ambiguity, shaping your own technical roadmap, adapt as needed and taking extreme ownership of system reliability, costs, and model performance. Nice-to-Have Skills Technical Leadership & Systems Architecture 3+ years of dedicated ML Engineering experience within a large-scale, enterprise environment (handling petabyte-scale data and working across globally distributed teams). Proven ability to architect, scale, and secure reliable, highly observable distributed systems, with a track record of leveling up platform foundations. Experience mentoring engineers, leading by example through high-quality code and rigorous design reviews, and fostering a culture of technical excellence. Strong problem-solving skills with a demonstrated ability to independently drive complex projects through ambiguous spaces and collaborate cross-functionally with data engineers, full-stack teams, and analysts. AI, LLMOps & Agentic Engineering Hands-on proficiency in building production-grade GenAI applications and multi-agent systems using advanced LLM frameworks like LangGraph, LangChain, or Autogen. Deep understanding of agent harness primitives, state management, memory architectures, and tool-calling loop mechanics. Experience establishing LLMOps foundations, including automated prompt tracking, LLM evaluation pipelines (e.g., Ragas, TruLens), vector database optimization, context/token management, and real-time guardrailing/moderation layers. Deep experience in scientific computing using Python (Scikit-Learn, PyTorch, or TensorFlow) and deploying traditional systems for end-to-end training, batch/real-time inference, and model observability. Infrastructure, Cloud & Data Platforms Strong experience with Docker and Kubernetes for containerization and orchestration, alongside Infrastructure-as-Code tools like Terraform and public cloud ecosystems (GCP, AWS, or Azure). Hands-on experience with modern MLOps platform tools (e.g., Airflow, Argo Workflows, ArgoCD) and data systems including BigQuery, Postgres, and robust ETL/ELT practices. Experience with full-stack web technologies and serverless/edge environments (FastAPI, TypeScript/JavaScript, Cloudflare Workers), with the agility to contribute across a multi-language stack. Strong foundation in continuous integration/continuous deployment (CI/CD), testing frameworks (Pytest), and robust version control practices. Education & Communication M.S. or Ph.D. in Computer Science, Statistics, Mathematics, or a related quantitative field. Exceptional written and verbal communication skills, with the ability to translate complex technical architectures into clear concepts for both engineering peers and business stakeholders. Equity This role is eligible to participate in Cloudflare's equity plan. Benefits Cloudflare offers a complete package of benefits and programs to support you and your family. Our benefits programs can help you pay health care expenses, support caregiving, build capital for the future and make life a little easier and fun! The below is a description of our benefits for employees in the United States, and benefits may vary for employees based outside the U.S. Health & Welfare Benefits Medical/Rx Insurance Dental Insurance Vision Insurance Flexible Spending Accounts Commuter Spending Accounts Fertility & Family Forming Benefits On-demand mental health support and Employee Assistance Program Global Travel Medical Insurance Financial Benefits Short and Long Term Disability Insurance Life & Accident Insurance 401(k) Retirement Savings Plan Employee Stock Participation Plan Time Off Flexible paid time off covering vacation and sick leave Leave programs, including parental, pregnancy health, medical, and bereavement leave What Makes Cloudflare Special? We’re not just a highly ambitious, large-scale technology company. We’re a highly ambitious, large-scale technology company with a soul. Fundamental to our mission to help build a better Internet is protecting the free and open Internet. Project Galileo : Since 2014, we've equipped more than 2,400 journalism and civil society organizations in 111 countries with powerful tools to defend themselves against attacks that would otherwise censor their work, technology already used by Cloudflare’s enterprise customers--at no cost. Athenian Project : In 2017, we created the Athenian Project to ensure that state and local governments have the highest level of protection and reliability for free, so that their constituents have access to election information and voter registration. Since the project, we've provided services to more than 425 local government election websites in 33 states. 1.1.1.1 : We released 1.1.1.1 to help fix the foundation of the Internet by building a faster, more secure and privacy-centric public DNS resolver. This is available publicly for everyone to use - it is the first consumer-focused service Cloudflare has ever released. Here’s the deal - we don’t store client IP addresses never, ever. We will continue to abide by our privacy commitment and ensure that no user data is sold to advertisers or used to target consumers. Sound like something you’d like to be a part of? We’d love to hear from you! Please note that applicants who progress to the offer stage of the interview process may be asked to attend an in-person interview within one of the Cloudflare Offices or Cloudflare Hubs.  More details about this will be available at that stage of the interview process. This position may require access to information protected under U.S. export control laws, including the U.S. Export Administration Regulations. Please note that any offer of employment may be conditioned on your authorization to receive software or technology controlled under these U.S. export laws without sponsorship for an export license. Cloudflare is proud to be an equal opportunity employer.  We are committed to providing equal employment opportunity for all people and place great value in both diversity and inclusiveness.  All qualified applicants will be considered for employment without regard to their, or any other person's, perceived or actual race, color, religion, sex, gender, gender identity, gender expression, sexual orientation, national origin, ancestry, citizenship, age, physical or mental disability, medical condition, family care status, or any other basis protected by law. We are an AA/Veterans/Disabled Employer. Cloudflare provides reasonable accommodations to qualified individuals with disabilities.  Please tell us if you require a reasonable accommodation to apply for a job. Examples of reasonable accommodations include, but are not limited to, changing the application process, providing documents in an alternate format, using a sign language interpreter, or using specialized equipment.  If you require a reasonable accommodation to apply for a job, please contact us via e-mail at hr@cloudflare.com or via mail at 101 Townsend St. San Francisco, CA 94107.

MDiet — early-stage HealthTech стартап, разрабатывающий AI-powered платформу, которая помогает снижать количество повторных госпитализаций пациентов. Продукт ориентирован на рынок здравоохранения США. Возможна релокация в США при успешном росте компании, обсуждается индивидуально. Tech Stack: • LangGraph + LangChain / Agentic AI • vLLM + Hugging Face • PyTorch + PEFT / LoRA / QLoRA • Vector databases: pgvector, Qdrant, Weaviate, Chroma, Pinecone • Advanced RAG, Prompt Engineering, Embeddings • FastAPI + PostgreSQL • Docker + Kubernetes + async processing: Arq / Celery / Redis Streams • Apache Airflow для data pipelines • AWS Cloud: EC2, S3, SageMaker, Lambda, RDS, Bedrock • Structured output: Outlines / Guidance / Instructor • Evaluation & observability: Langfuse, RAGAS / DeepEval, Prometheus + Grafana • MLOps practices Задачи: • Проектирование и разработка multi-step LLM agents и pipelines для обработки медицинских данных • Разработка production RAG-систем с высоким уровнем grounding и качеством citation • Деплой и оптимизация self-hosted LLM inference через vLLM с фокусом на cost и throughput optimization • Fine-tuning open-source моделей на domain-specific medical data с измеримым улучшением результата • Построение data pipelines и versioning для документов, embeddings и training data • Создание evaluation frameworks, guardrails и систем контроля hallucinations • Реализация reliable structured output и data extraction • Тесное взаимодействие с physicians и Platform Architect Требования: • Сильный hands-on опыт с production LLM systems: RAG, agents, inference • Опыт fine-tuning open-source LLMs: LoRA / QLoRA / PEFT, с измеримыми результатами • Опыт построения data pipelines и versioning для LLM / RAG workloads • Сильные навыки в evaluation, hallucination reduction и structured output • Production Python + FastAPI + async processing • Опыт с self-hosted inference: vLLM или аналогичные решения • Опыт GPU optimization • Английский язык на уровне C1: strong written and spoken communication skills • Bachelor’s degree или выше в Computer Science, Artificial Intelligence, Machine Learning, Data Science или смежной технической области • 5+ лет опыта в ML/AI • 1.5–2+ года опыта в production LLM projects • Опыт с MLOps Будет плюсом: • Опыт в medical / clinical NLP или regulated domains • Cost modeling и optimization of LLM workloads • MLOps и production fine-tuning pipelines • Apache Airflow, Prometheus / Grafana или AI Solution Architecture • Publications in relevant fields Условия: • Работа в early-stage HealthTech startup с продуктом для US healthcare market • Возможность участвовать в создании AI-powered платформы с реальным влиянием на healthcare-процессы • Офисный формат в Астане первые 7–9 месяцев, далее возможен hybrid/remote • Зарплата $4 000 – $5 000 USD gross + equity • Возможность релокации в США при успешном росте компании, обсуждается индивидуально

Location: Kuala Lumpur, Malaysia About Us Established in 2018, Bybit is one of the world’s leading cryptocurrency exchanges and digital financial platforms, serving over 80 million users across more than 200 countries and regions. Powered by world-class technology and a user-first mindset, Bybit delivers a seamless ecosystem across trading, payments, wealth management, custody, institutional services, and Web3 — connecting users to the future of digital finance. Our core values define how we build. We listen, care and improve to create products and experiences that put users first. Backed by a global team of ambitious builders, problem-solvers, and innovators, we foster a high-performance and fast-moving environment where talent is empowered to drive real impact at the global scale. Supported by 24/7 multilingual customer service and a strong commitment to innovation, we are shaping the future of finance through technology, collaboration, and bold execution. Today, Bybit is recognized as one of the most trusted and transparent platforms in the digital asset industry, continuing to expand its global presence while building the infrastructure for the next generation of financial services. Key Responsibilities 1. Recommendation Engine & Full-Pipeline Development Multi-stage Engine Development: Own the development and refactoring of high-concurrency, low-latency recommendation serving engines, powering the full pipeline of multi-channel recall (two-tower/collaborative filtering/ANN vector retrieval) → coarse ranking → fine ranking → re-ranking. Compute Tiering & Strategy Engine: Implement dynamic compute trimming and degradation mechanisms for dynamic Ul personalization and global strategy dispatch, ensuring core engine stability under extreme traffic spikes. 2. Real-time Feature Engineering & Data Consistency Governance Real-time Feature Pipeline: Build high-throughput, low-latency real-time feature streams on Kafka/Flink, enabling minute-level/second-level user pehavioral feature updates and dynamic sliding-window aggregations. Feature Store: Contribute to the development of unified online/offline feature storage with stream-batch convergence architecture; deeply govern industrial-grade pain points such as "online/offline feature inconsistency" and "feature time-travel leakage," systematically improving online/offline feature consistency. 3. Large-scale Retrieval & Vector Infrastructure Optimization Vector Retrieval System: Own the construction and optimization of large-scale vector retrieval systems (Faiss/Milvus/NSW) supporting candidate pools from tens of thousands to milions, lead index structure parameter tuning, achieving P99 retrieval latency < 100ms. Multi-source Heterogeneous Indexing: Build unified Embedding Pipelines and high-performance inverted index services covering trading products, news, KOL content, on-chain signals, and other heterogeneous data sources. 4. High-performance Model Inference & Compute Optimization Deep Model Engineering: Own high-performance online deployment and operator optimization of complex deep ranking models (e.g., DIN/SIM sequential models, MMoE/PLE multi-objective deep models). Inference Graph & Compute Governance: Solve compute explosion in multi-task/multi-objective online inference through inference optimization (quantization, graph optimization, batching strategies) - targeting fine-ranking P99 latency < 200ms. 5. System Performance, Global Observability & Experimentation Infrastructure High Availability & SLO: Ensure global recommendation service P99 latency < 200ms and system availability > 99.9%; write comprehensive overload protection, thread isolation, and disaster recovery degradation code. Global Distributed Tracing: In complex multi-region/multi-site/multi-language cross-border IDC environments, build and maintain end-to-end distributed tracing and monitoring systems (e.g.,Jaeger/Prometheus), establishing minute-level root cause attribution and fault localization mechanisms. Experimentation Platform Engineering: Contribute to A/B experiment traffic-splitting platform upgrades, engineering CUPED variance reduction and sequential testing mechanisms to reduce sample size requirements and accelerate algorithm iteration pipelines. Requirements Industrial-grade Hands-on Experience: 5+ years of recommendation system engineering at consumer-scale internet companies; must have deeply participated in or led architecture refactoring or launches of realtime recommendation systems serving tens of millions of users at scale. Proven experience building recommendation systems from 0→1 (not just maintaining mature systems) is strongly preferred. Mastery of Engineering Foundations: Exceptionally solid low-level CS fundamentals; proficient in at least one of Go/Java/C++ (Go preferred given current stack; C++ experience a plus for future engine optimization); familiar with PyTorch/TensorFlow model online inference and deployment optimization. Deep Expertise in Big Data & Retrieval: Hands-on with Spark/Flink/Kafka stack, with real experience solving stream computing latency and data backlog issues; proficient in Milvus/Faiss cluster deployment and tuning. Engineering Perspective on Algorithms: Deep understanding of computational complexity and online bottlenecks of core algorithms (collaborative filtering, two-tower recall, multi-objective optimization MMoE/PLE); ability to interface smoothly with algorithm teams for high-quality, efficient engineering translation. Excellent System Design Capability: Proven experience in actual development and core module design of recommendation platforms, feature platforms, experimentation platforms, or high-performance RPC frameworks at top-tier companies. Why Join Us At Bybit, we are committed to fostering a supportive and enriching work environment. Our benefits include: - Study Growth Fund: We support your professional development and continuous learning. - Internal Events: Participate in regular team-building activities, workshops, and events designed to promote collaboration and innovation. - Global Collaboration: Be part of a diverse, international team, working alongside colleagues from around the world. - Career Advancement: Access opportunities for growth and advancement within a rapidly expanding global company. - Internal Mobility: Grow with us- Your long-term development is important to us. We offer internal job opportunities to help build your career path.

Location: San Francisco, CA | New York City, NY | Seattle, WA About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role The Computer Use team focuses on teaching Claude to see, use, and understand computer interfaces. As a Research Engineer on the team, you'll work on advancing our models' ability to reliably and safely operate real software. We're looking for someone who's genuinely excited about both the research and the product sides of computer use. Your work will translate directly into model improvements in our own and our customers' products. You can try Claude's computer use capabilities today through the Claude in Chrome extension and Claude Cowork. Key Responsibilities: Design and run experiments to improve Claude's perception and agentic capabilities Develop robust, reliable evaluation frameworks for measuring our models' ability to complete complex computer tasks Build and improve computer use and vision reinforcement learning training environments Create pipelines and tools to test and validate complex RL environments Collaborate with teams across the model training and infrastructure stack to improve our production training setup Partner with product teams to bring research advances into production Minimum Qualifications: Software engineering experience and proficiency in Python Experience training, fine-tuning, or evaluating machine learning models Strong communication skills and a collaborative working style Care about the societal impacts and safety of your work Preferred Qualifications: Experience training models for computer use or other agentic capabilities Experience with reinforcement learning, particularly in long-horizon or sparse-reward settings Familiarity with multimodal model training Experience building evaluations or benchmarks for agentic systems Experience building reinforcement learning environments, simulation systems, or large-scale ML infrastructure Experience working closely with product teams to drive model improvements The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: $500,000 — $850,000 USD Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. For roles performing work in California: We will consider for employment all qualified applicants, including those with arrest or conviction records or criminal histories, in a manner consistent with the requirements of applicable state and local laws, including the San Francisco Fair Chance Ordinance, the City of Los Angeles’ Fair Chance Initiative for Hiring Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed.  Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings. How we're different We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences. Come work with us! Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.

Задачи: — Развертывать и оптимизировать локальные AI-модели. — Разрабатывать AI-агентов и RAG-решения. — Интегрировать AI-сервисы с корпоративными системами. — Выводить AI-продукты в промышленную эксплуатацию. Требования: — Практический опыт локального развертывания LLM. — Опыт работы с RAG, LoRA/QLoRA, Hugging Face, PyTorch. — Умение доводить AI-продукты до промышленной эксплуатации. — Для Strong Middle обязателен подтвержденный опыт самостоятельной разработки и внедрения AI-решений в production. — Действующее право на трудовую деятельность в Республике Казахстан (оформление разрешений на привлечение иностранной рабочей силы не осуществляется). Условия: — Работа в офисе в Астане, full-time. — Трудовой договор. — Возможность участвовать в создании собственной AI-платформы компании. — Зарплата до 2 000 000 тенге net по результатам интервью.

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. On the Forecasting team, you will develop models that estimate how campaign outcomes could change with different budgets, bids, audiences, creative performance, or objectives. Your work will connect supply and demand forecasting, audience availability, and response prediction to help advertisers compare alternatives and identify valuable opportunities. This is a hands-on role with end-to-end ownership of forecasting models and production capabilities. You will drive projects from problem formulation and technical design through evaluation, deployment, and iteration, working closely with engineers and cross-functional partners. What you’ll do Own forecasting and scenario modeling projects. Translate advertiser questions into modeling problems involving delivery, reach, performance, and investment tradeoffs. Develop technical approaches, make practical tradeoffs, and drive execution. Connect inventory, demand, and response. Use signals about available advertising opportunities, audience activity, marketplace competition, and predicted response to improve estimates of achievable campaign outcomes. Model outcomes under alternative choices. Build and improve models that estimate the effects of changes in budget, bid, audience, or optimization objective, accounting for relevant auction dynamics and campaign constraints. Help advertisers identify opportunity. Incorporate predictions from partner teams, including creative-performance estimates, to evaluate potential improvements in delivery, efficiency, or advertiser value. Improve model generalization. Develop approaches for sparse segments, cold start, seasonality, delayed feedback, and changing marketplace conditions. Choose methods that balance predictive quality, complexity, and production requirements. Build trustworthy evaluations. Measure forecast accuracy, calibration, uncertainty, and performance across advertiser cohorts. Document modeling assumptions and limitations, and work with partners to determine which scenario comparisons the evidence supports. Close the learning loop. Compare predictions with mature outcomes, investigate systematic errors, and use experiments and observed results to improve forecasts and validate scenario estimates. Deliver reliable production systems. Own training, inference, monitoring, and iteration for your models. Collaborate with Product, Data Science, Ads Delivery, Audience, and Creative teams; contribute to design and code reviews; and mentor engineers through project work. What we’re looking for Experience independently designing, shipping, and improving predictive ML systems in advertising, marketplaces, recommendations, search, forecasting, or similarly complex domains. Strong foundations in statistical learning, probability, and optimization, with experience working with large-scale behavioral or transactional data. Practical experience in at least one relevant area, such as forecasting, response prediction, probabilistic modeling, multitask learning, or transfer learning. Sound evaluation judgment, including temporal validation, segment-level analysis, and an understanding of calibration, uncertainty, and the limits of predictions under changed conditions. Experience diagnosing and addressing modeling challenges such as sparse observations, cold start, seasonality, distribution shift, selection bias, or delayed labels. Strong programming and software engineering skills, including experience building and operating production ML pipelines and services. Demonstrated ability to turn ambiguous product questions into concrete technical plans, lead projects through rollout, and collaborate effectively across functions. Preferred experience Experience in one or more of: Demand, supply, and inventory forecasting: Advertising inventory, marketplace demand, traffic, retail demand, or capacity forecasting, including time-series methods, hierarchical or global models, and multi-horizon prediction. Auction and investment modeling: Auction simulation or replay, win-rate and price prediction, bid/budget response curves, or constrained optimization. Scenario evaluation: Counterfactual or off-policy evaluation, sensitivity analysis, uncertainty propagation, or combining predictive models to estimate campaign outcomes. Reach and audience estimation: Population and overlap estimation, reach/frequency models, sampling correction, or saturation modeling. Probabilistic and response modeling: Quantile or distributional prediction, calibrated conversion/value prediction, or delayed-feedback modeling. What success looks like You independently deliver and operate forecasting capabilities that improve accuracy and calibration for priority advertiser populations. Your models support credible comparisons of campaign alternatives, have clear evaluation and monitoring, and help advertisers make better planning and 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: $216,700 — $303,400 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.

Задачи: - Управление AI-продуктами от идеи до вывода в прод. - Участие в проработке AI-стратегии кластера (Roadmap, KPI, приоритизация инициатив). - Проработка архитектуры AI-решений. - Координация команд: Data Scientists, ML-инженеры, Data Engineers. - Решение проблем масштабирования моделей и качества данных. - Запуск PoC и MVP AI-продуктов. - Оптимизация инференса моделей (скорость, стоимость, точность). - Дообучение open-source моделей под задачи бизнеса. Требования: - Образование: высшее. - Опыт работы: от 5 лет на релевантной позиции. - Опыт в предметной области AI. - Опыт коммерческой разработки, развитый технический кругозор. - Навыки презентации для C-level с ценностными тезисами. - Agile/Scrum для ML-проектов. - Языки: Python, SQL. - ML/DS: scikit-learn, TensorFlow/PyTorch, Hugging Face, LangChain. - MLOps: MLflow, Kubeflow, Airflow, Docker/Kubernetes. - Данные: Spark, DWH (BigQuery, Snowflake), векторные БД (FAISS, Pinecone). - Опыт управления коллективом: обязателен. Требования к личным качествам: - Лидерство, выстраивание коммуникаций, синхронизация работ, критическое мышление, предпринимательский mindset, адаптивность. Знание стека: - LangChain, Kafka, K8s, LLM-модели, NLP-модели — embedding, NER, classification.

Location: Cardiff, London or Remote (UK) 🚀 We’re on a mission to make money work for everyone. We’re waving goodbye to the complicated and confusing ways of traditional banking. After starting as a prepaid card, our product offering has grown a lot in the last 10 years in the UK. As well as personal and business bank accounts, we offer joint accounts , accounts for 16-17 year olds , a free kids account and credit cards in the UK, with more exciting things to come beyond. Our UK customers can also save , invest and combine their pensions with us. With our hot coral cards and get-paid-early feature, combined with financial education on social media and our award winning customer service, we have a long history of creating magical moments for our customers! We’re not about selling products - we want to solve problems and change lives through Monzo ❤️ 📍London/Cardiff/UK Remote | 💰 £138,000 to £176,000 + Incentive Awards tied to your performance + benefits | Hear from the team ✨ About the role Borrowing is one of Monzo’s most important growth engines. We’ve built strong scale in the UK, and the next phase is even more ambitious: grow in the UK, expand into new customer segments, and build the underwriting capability needed to scale into Europe. We’re looking for a senior ML leader to help shape that next phase. You’ll lead a high-calibre team of ML scientists and help define Borrowing’s ML roadmap across UK growth, new customer segments, and EU expansion. You’ll work across underwriting, pricing, limits, and lifecycle decisioning — improving today’s models while helping build the next generation of capability for Monzo. This role combines near-term commercial impact with long-term strategic advantage : improving performance across our range of products unlocking new data and faster model development helping Monzo build underwriting that scales into new markets shaping how advanced ML and AI can create real value in Borrowing You’ll partner closely with leaders across Credit Strategy, Product, Engineering, Model Validation, Data, and Platform, and influence decisions that shape both business performance and the long-term architecture of credit decisioning at Monzo. What you’ll own Borrowing ML roadmaps across UK optimisation, new customer segments, and EU expansion Faster model improvement through new data and feature engineering How Monzo uses Open Banking, transaction intelligence, and external data in credit decisioning More portable underwriting capabilities across products, customer segments, and markets Hiring, leading, and developing a high-performing ML team Raising the bar on model quality, governance, tooling, and speed of iteration What you’ll be working on Examples of the challenges you could help lead: Improving underwriting models across Loans, Flex, and Overdrafts Expand the data we use in our models Building better feature engineering and model shipping capability Improving transaction intelligence for credit use cases Developing underwriting capabilities for new customer segments and EU markets Shaping Monzo’s approach to advanced ML and AI Our technology stack We rely heavily on the following tools and technologies, although we do not expect applicants to have prior experience of all of them: Google Cloud Platform BigQuery SQL and dbt Python and the PyData stack Google AI Platform AWS Python for ML services Go for backend services AI tooling for productivity Google Workspace including Gemini ChatGPT Enterprise Claude Code You should apply if You want to shape the next phase of credit decisioning at one of Europe’s most ambitious fintechs You enjoy combining strategic leadership with strong technical judgment You’re excited by the intersection of underwriting, data, product, and platform capability You like building teams as much as building models You care about real customer and business impact You must have Credit and machine learning Strong knowledge of machine learning, statistics, and decisioning systems Deep experience in credit risk and lending Experience building, deploying, and iterating credit models in production Experience pushing the innovation frontiers of the credit industry Leadership and delivery Experience leading and developing senior ML scientists Strong cross-functional leadership with Product, Credit Strategy, Engineering, and Validation Track record of turning strategy into practical delivery in a regulated environment It would be great if you also have Experience across more than one market Experience with advanced ML methods and AI on structured financial data The interview process 30 min Recruiter Call 45 min Initial Call 60 min ML technical interview 60 min People Leadership interview 60 min Product / Credit ML interview All interviews will be conducted through Google Meet. What’s in it for you 💰 £138,000 to £176,000 ➕ plus stock options & benefits ✈️ We can help you relocate to the UK ✅ We can sponsor visas 📍This role can be based in our London office with a hybrid working pattern, or fully remote within the UK with occasional travel to London. 🏡 We’ll set you up to work from home, including support for remote setups. ⏰ We offer flexible working hours and trust you to work in a way that lets you do your best work. 📚 Learning budget for books, training courses and conferences ➕ And much more — see our full list of benefits here #LI-DC2  #LI-Remote Equal opportunities for everyone Diversity and inclusion are a priority for us and we’re making sure we have lots of support for all of our people to grow at Monzo. At Monzo, we’re embracing diversity by fostering an inclusive environment for all people to do the best work of their lives with us. This is integral to our mission of making money work for everyone. You can read more in our blog , 2026 Diversity and Inclusion Report and 2025 Gender Pay Gap Report. We’re an equal opportunity employer. All applicants will be considered for employment without attention to age, ethnicity, religion, sex, sexual orientation, gender identity, family or parental status, national origin, or veteran, neurodiversity or disability status. If you have a preferred name, please use it to apply. We don't need full or birth names at application stage 😊

Задачи: — активно участвовать в разработке микросервисов на Python; — участвовать в проработке технических требований, архитектуры и подходов к решению; — участвовать в управлении бэклогом команды, проводить приоритезацию и контролировать выполнение задач; — заниматься people-менеджментом: участвовать в найме, развитии сотрудников и т.д. Требования: — опыт работы в роли MLOps Engineer от 4-х лет; — опыт работы в роли Team Lead/ проведение code review – не менее 1 года; — опыт коммерческой backend-разработки на Python от 3-х лет; — понимание способов интеграций IT-систем (шины, очереди, API); — практический опыт с Kubernetes, Redis; — опыт работы с реляционными базами данных, уверенные знания SQL; — понимание принципов CI/CD; — понимание микросервисной архитектуры; — опыт работы с ИИ моделями и их внедрением. Будет плюсом: — знания в области BigData (Hadoop, Spark), OpenSearch; — знание планировщиков процессов (AirFlow); — опыт работы в банковской сфере; — понимание гибких методологий разработки (Agile); — знание основных инструментов управления разработкой (например: Jira, Confluence); — знания и опыт в Machine learning; — опыт управления IT проектами, включая разработку и внедрение ML моделей в продакшн; — опыт разработки ML моделей в роли data scientist, data engineer, ML Ops. Условия: — Хорошая зарплата: уровень обсуждается индивидуально, есть доплаты за менторство и профактивности. — Развитие: долгосрочные проекты от российских заказчиков, возможность менять направления, прозрачная система Performance Review. — Комфорт и свобода: релокейт между офисами, выбор формата работы (удалённо, офис, гибрид), адаптация для новых сотрудников. — Обучение: доступ к корпоративному порталу, митапам, конференциям (и как гость, и как спикер). — Социальный пакет: ДМС со стоматологией, частичная компенсация спорта, бесплатный английский, оплачиваемый отпуск и больничные. — Корпоративная жизнь: тимбилдинги, детские праздники, внутренние мероприятия.