Вакансии

Location: London, United Kingdom About Nebius: Nebius is leading a new era in cloud infrastructure for the global AI economy. We are building a full-stack AI cloud platform that supports developers and enterprises from data and model training through to production deployment, without the cost and complexity of building large in-house AI/ML infrastructure. Built by engineers, for engineers. From large-scale GPU orchestration to inference optimization, we own the hard problems across compute, storage, networking and applied AI. Listed on Nasdaq (NBIS) and headquartered in Amsterdam, we have a global footprint with R&D hubs across Europe, the UK, North America and Israel. Our team of 1,500+ includes hundreds of engineers with deep expertise across hardware, software and AI R&D. The role As a Senior Machine Learning Engineer on the Applied AI team at Nebius Token Factory, you will own model and endpoint optimization from model artifacts through production deployment. Your work will span model internals, quantization and model compression, speculative decoding, KV-cache optimization, inference engines, serving architecture, and benchmarking to improve latency, throughput, memory efficiency, GPU utilization, and cost per token while preserving quality and reliability. You will work with kernel and platform engineers to investigate serving problems, compare configurations, and resolve performance and quality regressions. You will evaluate model- and engine-level optimizations together with distributed inference designs, including request routing, scheduling, prefill/decode coordination, and multi-node GPU execution. Your improvements will be validated through reproducible benchmarks and safe production rollouts under real-world workloads. Your responsibilities Own optimization projects for specific model families, customer endpoints, or serving backends. Compare inference engines and recommend serving configurations suited to individual workloads. Diagnose model-quality and performance regressions during production rollouts. Improve LLM and VLM endpoint latency, throughput, memory efficiency, GPU utilization, quality, and cost per token. Deploy, configure, benchmark, and extend engines such as vLLM, SGLang, TensorRT-LLM, Triton Inference Server, and NVIDIA Dynamo. Develop production model-compression workflows covering quantization, quantization-aware training, distillation, low-bit serving, and accuracy recovery. Implement or integrate speculative decoding, draft-model approaches, KV-cache optimization, prefix caching, chunked prefill, continuous batching, and disaggregated prefill/decode serving. For prefill–decode disaggregation (PDD), evaluate KV-cache transfer, worker placement, and capacity balancing to determine which workloads benefit from the architecture. Design and improve LLM request routers and scheduling policies that balance worker utilization, queueing, request characteristics, and KV-cache locality while meeting latency and reliability targets. Scale dense and mixture-of-experts inference across multiple GPU nodes. Select and tune tensor, pipeline, data, and expert parallelism—including wide expert parallelism (WideEP)—with attention to hardware topology, expert load balance, and communication overhead. Build reproducible benchmark harnesses measuring TTFT, TPOT, tokens per second per GPU, p95/p99 latency, GPU memory, reliability, and cost per token. Validate architecture choices under representative traffic and consistent GPU budgets. Collaborate with GPU kernel and platform engineers to trace bottlenecks across model code, kernels, runtime, scheduler, gateway, and cluster layers. Produce design documents, performance reports, rollout plans, and customer-facing technical explanations. Must-haves Strong engineering skills in Python and PyTorch. Hands-on experience deploying or optimizing LLM, VLM, or high-throughput transformer inference. Practical knowledge of at least one modern inference stack, such as vLLM, SGLang, TensorRT-LLM, Triton Inference Server, NVIDIA Dynamo, Ray Serve, KServe, or an equivalent internal system. A strong understanding of transformer inference bottlenecks involving KV cache, attention, memory bandwidth, batching, parallelism, and long-context serving. Ability to quantify trade-offs among latency, throughput, quality, utilization, and cost. Experience designing or optimizing distributed inference system architecture, with hands-on work in one or more areas such as request routing, distributed scheduling, PDD, multi-node inference, or expert parallelism. Strong communication and collaboration skills across research, kernel, infrastructure, product, and customer teams. Nice-to-haves Experience with quantization-aware training, post-training quantization, FP8, INT8, INT4, NVFP4, MXFP4, AWQ, GPTQ, or SmoothQuant. Experience with distillation, speculative decoding, EAGLE, Medusa, multi-token prediction, or related inference acceleration methods. Experience supporting agentic workloads involving tool calling, structured outputs, streaming APIs, high concurrency, and multi-step orchestration. Familiarity with CUDA or Triton; the role does not require kernel engineering to be the candidate’s primary specialization. Contributions to open-source projects such as vLLM, SGLang, TensorRT-LLM, FlashInfer, LMCache, PyTorch, Triton, Ray, or KServe. Implementation experience with cache-aware request routing, PDD, or WideEP, including diagnosing communication bottlenecks and load imbalance. Familiarity with GPU communication libraries and interconnects, such as NCCL, NVLink, InfiniBand, or RoCE. Benefits & Perks: Competitive compensation Career growth and learning opportunities Flexibility and ownership Collaborative and innovative culture Opportunity to work on impactful AI projects International environment and talented teams What's it like to work at Nebius: Fast moving - Bold thinking - Constant growth - Meaningful impact - Trust and real ownership - Opportunity to shape the future of AI Equal Opportunity Statement: Nebius is an equal opportunity employer. We are committed to fostering an inclusive and diverse workplace and to providing equal employment opportunities in all aspects of employment. We do not discriminate on the basis of race, color, religion, sex (including pregnancy), national origin, ancestry, age, disability, genetic information, marital status, veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by applicable law. Applicants must be authorized to work in the country in which they apply and will be required to provide proof of employment eligibility as a condition of hire. If you need accommodations during the application process, please let us know.

Задача: изучать исторические торговые данные для двух связанных инструментов. Создать модель, которая предсказывает индикаторы движения цены одного из них по информации об ордербуке, сделках и дополнительным данным. Работа с реальными биржевыми данными. Когда: 11 сентября — 15 ноября Призовой фонд: $13,600 Победители получат денежные призы, приглашение на собеседование в Wunder Fund, а также интеллектуальное удовлетворение. Wunder Fund занимается высокочастотным трейдингом с 2014 года, с дневным оборотом более $10 млрд.

Задачи: — Разрабатывать и тестировать алгоритмы на базе LLM для генерации вопросов, ответов и кратких пересказов; — Работать с RAG-подходами: подготовка данных, индексация, поиск релевантного контекста, генерация ответа на его основе; — Участвовать в развитии виртуального ассистента и сценариев свободного диалога с пользователем; — Настраивать фактчекинг, фильтрацию и оценку качества ответов модели; — Разрабатывать пайплайны для суммаризации полезного контента; — Сравнивать результаты, подготовленные моделью, с редакционными версиями материалов, в том числе на примере «Картины дня»; — Создавать и развивать векторные поисковые индексы для задач RAG; — Тестировать промпты, гипотезы и подходы к улучшению качества ответов. Требования: — Опыт разработки на Python; — Опыт работы с задачами NLP: обработка текстов, классификация, генерация, суммаризация, NER, семантический поиск или похожие задачи; — Понимание принципов работы LLM и практический опыт их применения; — Опыт или хорошее понимание RAG-подходов; — Опыт работы с библиотеками и инструментами для ML/NLP: PyTorch, TensorFlow, Hugging Face, Transformers или аналогами; — Понимание fine-tuning/LoRA/prompt engineering на уровне, достаточном для прикладных задач; — Умение оценивать качество моделей и промптов: метрики, сравнение результатов, базовый benchmarking; — Знание SQL и опыт работы с базами данных; — Готовность разбираться в продуктовой задаче, а не только в модели. Условия: — Официальное трудоустройство; — ДМС со стоматологией, офисный врач, доплата больничного листа, корпоративные скидки; — Современный офис (Даниловская мануфактура, м. Тульская), бесплатный спорт в собственном спортзале от «Лиги Героев»; — Бесплатная подписка на сервисы партнеров; — Полный день в офисе, гибридный график или 100% дистанционный формат работы; — Льготные условия ипотеки в рамках зарплатного проекта; — Насыщенная корпоративная жизнь.

Location: United Kingdom (Hybrid); United Kingdom (Remote); Spain; Netherlands Workplace: Hybrid Most money apps talk at you. Cleo talks back ✨ We're not building another finance app. We're building the world's first AI financial assistant, one that actually understands your money and makes you better at it, and we're changing the world's relationship with money in the process 💰 For everyone, whatever their background or balance. The proof: profitable, fast-growing, a unicorn with over $300M in ARR, and millions of people who now feel differently about their money. We love original thinkers who challenge the status quo. We move fast, tell the truth, and there's nowhere to hide from good work here. If that excites you more than it scares you, you'll fit right in. Follow us on LinkedIn https://www.linkedin.com/company/cleo-ai/ for new features and the occasional roast 🔥 Meet the team 👥 Here at Cleo, we have ~45 ML engineers ✨ They are the builders behind the AI that powers our product. They design experiments, create models that personalise financial insights, deploy fraud detection systems and scale our conversational AI, making sure that what we build genuinely helps people feel better about their money. From shaping product features and building the models that personalise our users' money journeys, to tackling fraud, scaling our systems and giving leaders the insights they need, our data team is at the heart of it all. About the role ✍️ We're hiring an Tech Lead ML Manager to join the existing data science function at Cleo. You’ll be working on models that support our understanding of a user’s financial life, building our foundational capabilities upon which all our product innovation depends, from the chat based Financial Assistant to our credit products. You’ll take on technical leadership and ownership of a workstream within the Financial Understanding pillar. We work at the intersection of LLMs and classical machine learning, trying to bring the advanced capabilities that are now possible at the speed and scale necessary for the size of Cleo’s userbase. - Leading and managing a team of circa. 4 MLE’s - Deploying machine learning models into production - Collaborating with product managers, backend engineers and analysts to play an active role in the planning and sequencing of work for the squad. - Helping define the roadmap for the squad/workstream What we're looking for 🕵 - At least 5 years of experience in data science, ML engineering or related roles - At least 1 year of management experience, ideally of a small team. - Ability to write production quality python code. - Experience deploying machine learning models into production - Experience with both LLMs and classical machine learning - Comfortable with complexity & developing a holistic understanding of a system in order to propose & build solutions WHAT WE OFFER We offer benefits package built to support you in and out of work. Benefits vary by country 🌍 and include meaningful equity, comprehensive health insurance, mental health support, a paid one-month sabbatical after four years, a learning & development platform, pension or retirement contributions, and location-specific leave entitlements. For the full breakdown on what's available in your location, visit our Candidate Hub https://meetcleo.notion.site/Cleo-Candidate-Hub-3b95c63b874580e9a068fdc6c8929ebf 👈 WHAT MATTERS Cleo's mission is to change the world's relationship with money. We can't do that without building a brilliant, genuinely diverse team - and making sure our hiring process gives everyone a fair shot, running a fair and transparent recruitment process in which every candidate is considered on their merits. We're glad to make reasonable adjustments at any stage of the process. If there's anything we can do to support you in showing us your best, please let your recruiter or one of the team know. We may use AI-assisted tools during the recruitment process to take support the team in different ways like taking notes or referring back to conversations. They do not replace the team and all applications are reviewed by a Cleo employee thoroughly!

Location: Mississauga Workplace: hybrid At PointClickCare our mission is simple: to help providers deliver exceptional care. And that starts with our people. As a leading health tech company that’s founder-led and privately held, we empower our employees to push boundaries, innovate, and shape the future of healthcare.   With the largest long-term and post-acute care dataset and a Marketplace of 400+ integrated partners, our platform serves over 30,000 provider organizations, making a real difference in millions of lives. We also reinvest a significant percentage of our revenue back into research and development, ensuring our employees have the resources to innovate and make a lasting impact. Recognized by Forbes as a top private cloud company and honored as one of Canada’s Most Admired Corporate Cultures, we offer flexibility, growth opportunities, and meaningful work.    At PointClickCare, we empower our people to be the architects of a smarter healthcare future; one that is human-first and accelerated by AI to create meaningful and lasting change. Employees harness AI as a catalyst for creativity, productivity, and thoughtful decision-making. By integrating AI tools into our daily workflows, collaboration is enhanced, outcomes are improved, and every team member has the proficiency to maximize their impact. It all starts with our hiring practices where we uncover AI expertise that complements our mission, and we continue to invest in training and development to nurture innovation throughout the employee journey.   Join us in redefining healthcare — so it doesn’t just survive, it thrives. To learn more about PointClickCare, check out Life at PointClickCare and connect with us on Glassdoor and LinkedIn.     **Travel to Office expectations** For Remote Roles: If this role is remote, there will be in-office events that will require travel to and from the Mississauga and/or Salt Lake City office. These will include, but not limited to, onboarding, team events, semi-annual and annual team meetings.   For Hybrid Roles: If this role is Hybrid, there will be an expectation to reside within commutable distance to the office/location specified in the job listing. This will include, but not limited to, weekly/bi-weekly/monthly events in the office with your specific team. This is a requirement for this role. Team Summary This team will serve as the product owner for the machine learning platform capabilities within PointClickCare, working closely with other engineering teams across the organization to identify, build and support traditional machine learning (ML) and hybrid ML/LLMsolutions. This centralized team with deep specialization will closely integrate with key horizontal partners to ensure delivery of safe, scalable, and high-impact AI products. Job Summary The Senior Machine Learning Systems Engineer will work closely with the Product and Engineering teams to design, build, and operate the machine learning platform that enables teams across PointClickCare to develop, deploy, and scale ML solutions. The Senior AI Machine Learning Systems Engineer will also build and maintain the pipelines, tooling and infrastructure for model training, deployment, serving, and monitoring that underpin our AI products. Key Responsibilities   Collaborate with product and engineering teams to translate ML needs into reliable, reusable platform capabilities. Design and build scalable data and ML pipelines that support model training, evaluation, deployment, and serving. Develop and maintain ML Ops tooling and workflows, including CI/CD for models, model registry, feature stores, and experiment tracking. Ensure the reliability, observability, and performance of ML systems in production through monitoring, alerting, and automated remediation. Implement comprehensive security mechanisms for the ML platform, including authentication, role-based access control, audit logging, and compliance monitoring. Securely integrate the platform with existing systems, APIs, and data sources with secure communication protocols, and optimize infrastructure for cost, performance, and scale. Mentor engineers on the teams and promote reusable platform patterns and best practices.   Qualifications & Skills   Expert level in Python and Java, and strong software engineering fundamentals. Experience designing and building ML platforms and MLOps workflows, with familiarity with tools such as MLflow, Kubeflow, Ray, and model-serving frameworks. Experience with cloud platforms (Primarily Azure, secondarily AWS and GCP) Experience with ML runtime containerization, optimization, and orchestration (Docker, Kubernetes).   Preferred   Bachelor’s degree or higher in Computer Science, Machine Learning, or a related field. Working familiarity with Azure Machine Learning components and Databricks processing and serverless environments Experience implementing security at scale including role-based access control, multi-factor authentication, network security best practices, and compliance monitoring. Experience optimizing large model training and inference (including LLM serving) for performance and cost. #LI-AV1 #LI-Hybrid PointClickCare Benefits & Perks:   Benefits starting from Day 1! Retirement Plan Matching Flexible Paid Time Off Wellness Support Programs and Resources Parental & Caregiver Leaves Fertility & Adoption Support Continuous Development Support Program Employee Assistance Program Allyship and Inclusion Communities Employee Recognition … and more!   It is the policy of PointClickCare to ensure equal employment opportunity without discrimination or harassment on the basis of race, religion, national origin, status, age, sex, sexual orientation, gender identity or expression, marital or domestic/civil partnership status, disability, veteran status, genetic information, or any other basis protected by law. PointClickCare welcomes and encourages applications from people with disabilities. Accommodations are available upon request for candidates taking part in all aspects of the selection process. Please contact recruitment@pointclickcare.com should you require any accommodations. As part of our commitment to a streamlined and equitable hiring experience, PointClickCare uses AI tools to assist with candidate screening and assessment.   When you apply for a position, your information is processed and stored with Lever, in accordance with Lever’s Privacy Policy. We use this information to evaluate your candidacy for the posted position. We also store this information, and may use it in relation to future positions to which you apply, or which we believe may be relevant to you given your background. When we have no ongoing legitimate business need to process your information, we will either delete or anonymize it.  If you have any questions about how PointClickCare uses or processes your information, or if you would like to ask to access, correct, or delete your information, please contact PointClickCare’s human resources team: recruitment@pointclickcare.com    PointClickCare is committed to Information Security. By applying to this position, if hired, you commit to following our information security policies and procedures and making every effort to secure confidential and/or sensitive information. At PointClickCare, base salary is one of the many components that make up our total rewards package. The CAD base salary range for this position is $174,000-$218, 000 (not overtime eligible) + bonus + benefits. Compensation is assessed individually and aligned to experience, skills, and market context. The posted range reflects typical expectations for this role

Location: München, Germany; Berlin, Germany Workplace: Hybrid WHY THIS ROLE IS EXCITING Rohlik is the leading Central European e-grocer. More than a million customers buy their groceries from us through Rohlik.cz http://Rohlik.cz, Knuspr.de http://Knuspr.de, Kifli.hu http://Kifli.hu, Gurkerl.at http://Gurkerl.at and Sezamo.ro http://Sezamo.ro, choosing from over 17,000 items, delivered within a couple of hours in 15-minute windows. We are building the autonomous grocer. Maia, our AI assistant, already talks to customers and builds their orders; agents are moving into our buying and logistics; vision models are learning to watch quality. The next frontier is physical: robots taking over more and more of the work in our fulfilment centres, until fresh food moves from producers to households at a cost nobody else can match. We are setting out to record how our own people handle groceries and to turn those recordings into a training corpus for manipulation policies. Our warehouses already log what happened on every pick and every quality check, so the recordings can carry labels most datasets never get. Making that hold is part of the job. You decide what makes an hour of footage trainable — before we bank hundreds of them. WHAT YOU WILL OWN AND DELIVER - The capture spec. Cameras, mounts, calibration, and what disqualifies an episode. You write it before we buy the fleet and enforce it after. When the spec and the floor disagree, you go to the floor and find out which one is wrong. - Ground truth. Hand pose is the signal that transfers from human video to robot policies, and warehouse reality — work gloves, occlusion, cold halls — is exactly where the published models struggle. You own how we measure that and how we close the gap. - The evaluation harness. The corpus only counts if a policy can train on it. You build the harness that decides which hours make the bank, and you hold the trainability bar as the volume grows. - The first training runs. Once hours bank, you post-train open robot-learning models on our data and run the first task evaluations. - Judgment before spend. The field moves monthly. You read what is published, reproduce the claims we depend on, and steer our capture before the money is spent, not after. THE SCOPE This is a seat on a small, newly created team: an operations lead who runs capture on the floor, a data engineer who owns the pipeline, and you: the machine-learning voice in the room. This is not a research-scientist role, and no publication record is required. The job is to make a corpus trainable and to train the first policies on it. WHAT WE ARE LOOKING FOR - 3+ years of relevant full-time robotics experience. - PyTorch, and computer vision with real 3D geometry. Camera calibration, pose estimation, SLAM fundamentals. You have debugged an extrinsic calibration at least once in your life and know why it drifted. - You can read a paper and reproduce it. Our pipeline is assembled from published work, and your job is knowing which claims survive contact with a chilled warehouse hall. - You know what policies train on. Familiar with the current robot-learning stack and policy classes well enough to know what the training side will demand of the data before the data exists. - Dataset instincts. You have trained on data you collected yourself, and you know the failure modes that only show up at training tim.. - Comfortable as the only ML voice. You make the call, write it down, and revisit it when the pilot data says otherwise. - AI is how work gets done here. Our engineers use Claude Code and Devin daily, and we expect you to direct agents for the unglamorous parts — harness code, data plumbing, reproduction scripts — while keeping the quality bar exactly where it was. - Particularly relevant. Hand-pose estimation or egocentric video in production; multi-camera rigs and time sync; experience post-training robot-learning models; ROS 2. WHAT SUCCESS LOOKS LIKE IN 90 DAYS You have written the capture spec and we set up the first rigs against it. Your pilot gates have numbers, the first hours have passed your harness, and you can tell the people who sign the budget, with data, what the next hundred hours should look like. ARE WE A GOOD FIT? Take this role if you would rather own the spec that decides whether a dataset becomes an asset or a write-off than tune someone else's model on someone else's data, if a fulfilment centre at 6 a.m. sounds like a lab to you, and if you want the shortest possible line between your technical judgment and a capital decision. Read our culture code https://career.rohlik.group/group/our-culture-code and are we a good fit? https://career.rohlik.group/group/are-we-a-good-fit. WHAT WE OFFER - The physical-AI side of the autonomous grocer, with the capture spec and the first training runs yours from day one - A data asset few teams in the world get to build, in fresh groceries, a domain as hard as manipulation gets - Off-the-shelf hardware, a managed cloud estate, and a mandate that has already started - Benefits — see what we https://career.rohlik.group/group/what-we-offeroffer https://career.rohlik.group/knuspr/unser-angebot ARE YOU IN? Instead of a cover letter: pick a published human-video-to-robot-policy pipeline you rate, and tell us where it breaks on a warehouse floor where every hand wears a work glove. #LI-ML1 #Knuspr

Задачи: — Исследование и разработка LLM: полный цикл работы с языковыми моделями от гипотез и экспериментов до оптимизации и внедрения в продакшен. — Проведение A/B-экспериментов: валидировать гипотезы через масштабные A/B-тесты, анализировать влияние на ключевые продуктовые и бизнес-метрики. — Работа с продуктом: взаимодействовать с инженерами и продакт-менеджерами для превращения исследовательских решений в рабочие фичи. — Анализ и оптимизация: отслеживать качество моделей, искать точки роста и устранять слепые зоны в данных и алгоритмах для улучшения ключевых метрик. Требования: — Хорошее знание основ современного ML и DL. — Готовность работать с большими данными и распределёнными вычислениями. — Желание заниматься прикладными ML-исследованиями. — Практический опыт в NLP или RecSys. Условия: — Ежегодные медицинские чекапы, йога и психотерапия, а также другие бонусы.

Задачи: — Разработка и развитие сервисов промышленного инференса ML-моделей и LLM — Построение централизованного слоя доступа к моделям и AI-сервисам — Разработка платформенных компонентов для деплоя, версионирования и мониторинга моделей

Location: United States (Remote) Workplace: Remote ABOUT ONEPAY OnePay is the consumer fintech trusted by millions of Americans to make money better. Our financial system is broken. High fees, low rates, and too few ways to actually grow your money. We’re fixing it. And we’re moving fast. We’re an all-in-one financial services platform that brings together banking, high-yield savings, credit cards, point-of-sale lending, investing, and crypto in one place. We also partner with employers, HCM providers, gig platforms, and others to deliver embedded financial services to millions of employees and frontline workers. We’re backed by Walmart, the world’s largest retailer, and Ribbit Capital, one of fintech’s most respected investors, giving us rare scale, distribution, and the opportunity to build something truly category-defining. But what really sets OnePay apart is how we move. Our customers don’t have time to wait… and neither do we. This place moves fast, and we’re looking for people who are: - Ready to run - Hungry and driven by urgency - Exceptional at what they do, with low ego - Comfortable operating in motion THE ROLE As an AI Research Intern, your mandate is to research and prototype foundation models, both transformer-based and graph-based, applied to OnePay's customer, transaction, and merchant data. You'll work on real modeling problems and build a working prototype that demonstrates the approach. This role will impact OnePay's vision by providing a remarkable customer experience, driving positive business outcomes, and improving operational efficiency. You will work closely with the product, engineering and applied science team(s) working across all business units. This role reports to the Head of AI/ML. This role is responsible for: - Research, design, and train foundation models on OnePay's data, working with transformer and/or graph-based architectures. - Run experiments, evaluate results rigorously, and turn promising findings into a working prototype. - Opportunity to work in an extremely fast paced growing company - Partner with Product, Engineering and Analytics teams to ensure we are providing a remarkable customer experience. - Driving significant impact on the company’s bottom line. YOU BRING - Final-Year Graduate Student: You’re in the final year of a master’s or PhD program, expected to graduate in Fall 2027 or Spring 2028, majoring in Engineering or a related field. - Deep Learning Fundamentals: You have a strong grasp of how neural networks work under the hood, including backpropagation, attention, embeddings, optimization, and training dynamics. You've implemented, trained, or fine-tuned models yourself through research, coursework, or personal projects, and you're comfortable working in PyTorch or an equivalent framework. - Prior Experience: You've completed at least one previous internship in research, machine learning, or software engineering, or have built substantial personal or academic projects you're excited to talk through. - Availability: This is a full-time, 8-week program running June 21 - August 13, 2027. You are available to work in our New York City office at least 3-4 days a week throughout. - Proactive & Collaborative Attitude: You have an act-like-an-owner mentality, thrive in a fast-moving environment, and enjoy working with others to achieve collective goals. - Critical Thinking & Problem-Solving Skills: You enjoy tackling complex challenges, analyzing problems, and developing solutions. Clear communication of ideas is one of your strengths. - Organized & Detail-Oriented: You’re a keen listener with strong organizational skills, capable of managing multiple tasks efficiently. - Excited to Learn & Grow: You are passionate about being part of a rapidly growing start-up and look forward to expanding your skills in a real-world setting. NICE TO HAVE - Familiarity with transformer-based foundation model architectures, including pre-training, fine-tuning, inference, and the tradeoffs between approaches. - Exposure to graph-based learning or graph foundation models, such as GNNs, message passing, or representation learning on graph-structured data. - Coursework or projects involving Large Language Models or Generative AI. - Research output in any of the above: publications, workshop papers, or open-source contributions. WHAT WE OFFER - Early access to a high potential, high growth fintech - Hands on experience building product ready to ship - Beautiful NYC SoHo Office - Professional development and team social outings PROGRAM & APPLICATION TIMELINE - Program runs June 21 - August 13, 2027 (8 weeks, full-time, in our NYC office) - We review applications and conduct interviews on a rolling basis as they come in - No offer decisions will be made until the application window closes – all candidates are evaluated together before final decisions go out - We are targeting a finalized cohort by December 1, 2026 - If there are intern spots still available, we may reopen applications in Spring 2027 Work Authorization: OnePay will not sponsor applicants who need employment sponsored visas for this position. Future sponsorship will not be considered. STANDARD INTERVIEW PROCESS Our process varies by role. Most candidates go through: - AI-assisted initial screen - Interview with Talent Partner - Technical or Hiring Manager Interview - Team Interview - Executive Interview - Offer! EQUAL EMPLOYMENT OPPORTUNITY To build technology and products that are used and loved by people and solve real-world problems, we need to build a team with many different perspectives and experiences. We are an equal opportunity employer. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status. We encourage candidates from all backgrounds to apply. Applicants in need of special assistance or accommodation during the interview process or in accessing our website may contact us at talent@onepay.com.

Location: Москва Мы - команда GigaChat Pretrain Data, готовим pretrain данные для GigaChat и GigaChat Vision. Pretrain данные - это фундамент, с которого начинается путь современной LLM модели и то, от чего наиболее зависит ее итоговое качество. Сырых данных более 40Пб и основная задача заключается в том, чтобы из этого хаоса сделать датасет, на котором будет обучена лучшая LLM в России. Обязанности * генерировать синтетические данные: математика, код, произвольная синтетика с сидами - документами из Web * исследовать токенизацию и ее влияние на качество модели (возможно написание статей) * решать задачи кластеризации миллиардов документов * исследовать разные факторы, которыми обладают текстовые данные * генерировать Vision данные для прокачки VLM * разрабатывать новые алгоритмы парсинга HTML и исследовать его влияние на качество модели * исследовать зависимости между pretrain данными и agentic capabilities итоговой модели * разрабатывать стабильную инфраструктуру, которая будет поддерживать проведение сотен и тысяч экспериментов над данными. Требования * имеешь коммерческий релевантный опыт связанный с NLP или построением инфраструктуры для данных от двух лет. Будет плюсом: * навыки работы с генеративными AI-моделями; опыт создания AI-агентов и использования их в работе будет преимуществом * опыт использования GigaChat, Kandinsky и аналогов в продуктах, навыки создания и использования AI-агентов * инструментальное владение AI для анализа, генерации и автоматизации * опыт с MapReduce системами. Условия * комфортный современный офис рядом с м. Кутузовская * гибридный формат работы (2 дня в офисе, 3 дня на удалёнке) * ежегодный пересмотр зарплаты, годовая премия * корпоративный спортзал и зоны отдыха * система обучения для профессионального и карьерного развития * расширенный полис ДМС с первого дня работы и страхование для семьи * льготная программа ипотеки для сотрудников * бесплатная подписка СберПрайм+, скидки на продукты компаний-партнеров * вознаграждение за рекомендацию друзей в команду Сбера.

Location: Indianapolis, United States; Chicago, United States; Cincinnati, United States; Columbus, United States; West Lafayette, United States; Bloomington, United States Location: Indianapolis, IN Metro (Hybrid / 3-Day Onsite) (Open to Regional/EST Candidates with Onsite Travel) Contract Type: Contractor Full-Time / Enterprise Project Engagement (Outsourced via Xenon7) About Xenon7 Where elite tech talent meets world-class opportunities! At Xenon7, we work with leading enterprise clients and innovative startups on high-impact projects across Data, AI, Cloud, and Software Engineering. Our expertise in AI solution architecture and specialized technical talent allows us to partner with enterprise leaders on transformative initiatives, driving innovation and business growth. Job Summary We are seeking a Senior Machine Learning Engineer with extensive experience in production MLOps, model deployment, and system scaling to drive ML engineering initiatives for a top-tier life sciences client. This role sits at the critical intersection of production ML infrastructure, life science research, and manufacturing process engineering. In this position, you will own the architectural design and hands-on execution of production ML systems, model integration APIs, and scalable MLOps pipelines. You will bridge complex domains—from computational biology, small and large molecule research, and clinical trial analytics to active pharmaceutical ingredient (API) manufacturing processes, batch optimization, and industrial automation ML. Operating in a 3-day onsite hybrid capacity in Indianapolis, you will collaborate directly with process engineers, life science researchers, and platform engineering teams to build robust, low-latency ML systems that scale across the enterprise. Key Responsibilities Production MLOps & Systems Architecture Design, deploy, and maintain robust, production-grade MLOps pipelines and infrastructure for continuous model training, deployment, versioning, and monitoring. Implement automated model drift detection, performance monitoring, and self-healing inference pipelines in high-reliability environments. Process Engineering & Manufacturing ML Integration Operationalize and integrate production ML models into operational technology (OT), API manufacturing workflows, and chemical process control systems. Deploy predictive models for batch processing, process control optimization, real-time quality assurance, and facility automation use cases. Scalable Inference & System Integration Build low-latency, high-throughput microservices and serving architectures (FastAPI, Triton Inference Server, TorchServe) for model deployment into live production applications. Containerize and orchestrate ML workloads across distributed cloud and edge systems using Kubernetes, Docker, and modern pipeline engines (Kubeflow, MLflow). Technical Leadership & Domain Alignment Partner directly with chemical engineers, computational biologists, and software architects to translate operational friction into production-ready ML engineering solutions. Establish enterprise MLOps standards, model governance, and CI/CD best practices across the full machine learning operational lifecycle. Requirements Experience & Mindset Experience: Senior-level proficiency (10–20+ years) in software engineering, MLOps, production ML system deployment, and infrastructure scaling. Domain Adaptability: Demonstrated ability to deploy and maintain production ML systems across non-standard, highly specialized domains (e.g., transition between process/chemical engineering ML and clinical/scientific research applications). Location & Work Auth: Must hold unrestricted US Work Authorization (no sponsorship available) and be able to work 3 days per week onsite in the Indianapolis, IN area. Culture & Communication: Pragmatic problem-solving mindset, strong collaborative drive, and the ability to articulate complex MLOps architecture to cross-functional engineering teams. Must-Have Technical Stack Languages & Frameworks: Advanced Python, C++, and deep proficiency with PyTorch, TensorFlow, or Scikit-learn. MLOps & Serving: Proven expertise with Triton Inference Server, TorchServe, MLflow, Kubeflow, or Databricks ML runtime. Infrastructure & Orchestration: Hands-on expertise with Kubernetes, Docker, CI/CD pipelines, FastAPI/gRPC, and cloud platform ecosystems (AWS/Azure). Monitoring & Integration: Experience building real-time model monitoring, feature stores, drift detection systems, and integration with enterprise data pipelines. Domain Competency (Scientific & Process Focus) Deep exposure to applying ML models in either scientific/clinical domains (drug discovery, small/large molecule, computational biology) OR chemical/process engineering environments (API manufacturing, batch processing, SCADA/MES integration, process optimization). Nice-to-Haves & Certifications Academic background in Chemical Engineering, Bio-process Engineering, Computer Science, or a related STEM discipline. Direct experience operationalizing ML models inside regulated GxP environments in the Life Sciences or Specialty Chemicals sectors. Certifications: AWS Certified Machine Learning – Specialty, Databricks Certified Machine Learning Professional, or equivalent MLOps credentials. What This Role Is NOT Not a Data Scientist or Exploratory R&D Specialist: You will not be focusing on exploratory data analysis, academic algorithms, or standalone Jupyter notebook modeling; you are building, scaling, and maintaining production MLOps pipelines, inference engines, and model integration code. Not a non-coding Architect: This is a 100% hands-on MLOps and software engineering lead role requiring direct model deployment, infrastructure creation, and technical execution. Not a Fully Remote Position: This role requires a steady hybrid commitment of 3 days onsite per week at the client site in Indianapolis.

В данной роли предстоит заниматься разработкой и проектированием AI-агентов, которые автоматизируют процессы отчетности и выявления отклонений в бизнес-процессах. Задачи включают создание пайплайнов для обнаружения аномалий, интеграцию этих решений с большими языковыми моделями (LLM) для генерации текстовых отчетов, а также работу с большими данными: подготовку признаков, агрегацию и передачу данных в модели. Кроме того, необходимо оптимизировать, упаковывать и разворачивать агентные системы в продуктивной среде. Важной частью работы является тесное взаимодействие с бизнес-подразделениями и аналитиками для преобразования их требований в технические AI-решения. Кандидат должен иметь не менее трех лет опыта коммерческой разработки на Python, хорошо разбираться в языковых моделях, включая понимание их ограничений, а также иметь опыт работы с LangChain или аналогичными инструментами для интеграции LLM в бизнес-задачи. Требуется знание математической статистики и основ различных методов машинного обучения, а также понимание принципов DevOps и процессов CI/CD. Работа предполагает офисный формат в Москве.

Задачи: — Разработка и масштабирование World Models: проектирование и обучение массивных 3D/видеотокенизаторов и бэкбонов на базе Diffusion Transformers (DiT), Flow Matching и других технологий. — Построение пайплайнов для распределённого обучения тяжёлых foundation-моделей на вычислительном кластере. — Обучение Model-Based RL (MBRL) и политик IL + RL внутри замороженной латентной симуляции World Model. — Интеграция вспомогательных лоссов для perception-задач 3D-детекции, сегментации, трекинга. — Создание надёжного safety-контура поверх выходов модели и подготовка конструкции к реалтайм-инференсу на edge-девайсах робота. Требования: — Экспертное владение JAX и PyTorch. — Навыки масштабного распределённого обучения. — Глубокая математическая и ML-база. — Умение писать, генерировать и проверять быстрый оптимизированный код. Будет плюсом: — Опыт работы с Vision Foundation Models, генеративными видео- и image-моделями, синтезом лидарных облаков. — Опыт в Reinforcement Learning. — Опыт продвинутой квантизации тяжёлых трансформеров или диффузионных моделей для edge-девайсов. — Оптимизация инференса на борту робота. — Бэкграунд в Autonomous Driving, Motion Planning или Robotics. Условия: — Возможность обучения и компенсация 50% стоимости курсов по языку. — Другие бонусы, полный список которых доступен на сайте компании.

Location: London, England Our Global Structure Deliveroo is now part of DoorDash, bringing together teams with even greater reach, scale, and ambition. Depending on your role, you may collaborate with teammates, systems, and leaders across DoorDash and Wolt. Together, we’re unlocking new possibilities as one global team. Join us in our mission to transform the way people shop and eat, where impact, innovation and growth drive everything we do. Our Global Engineering teams tackle complex technical challenges across a global, three-sided marketplace, building and scaling systems that serve millions of customers, riders and partners every day. We're looking for a Machine Learning Engineer to join our London office as part of a Global DoorDash Engineering team (working hybrid, 3 days in the office). Our New Verticals team exists to grow monthly active usage outside of restaurant ordering — groceries, retail, and everything in between. That means turning someone who only orders restaurant food into someone who also uses the app for a grocery run, and turning dormant users into active ones. You'll work on the surfaces that make that happen: the homepage, vertical landing pages, and the notifications that bring people back. This role sits closer to the personalisation and ranking side of that mission, working alongside colleagues across DoorDash, Deliveroo and Wolt. What You'll Be Doing Build and improve ranking models for vertical landing pages (grocery, retail), deciding what a user sees first when they land on a store page Design models that blend new-vertical content into a homepage built around restaurant ordering, without disrupting the core experience Build personalisation models that translate a user's existing behaviour into relevant recommendations for verticals they haven't tried yet Contribute to notification targeting — who gets notified, with what message, and when, to drive adoption of new verticals What You'll Need to Thrive Minimum 3+ years of experience as an ML Engineer or Data Scientist, with a proven ability to write high-quality production code in Python Experience with end-to-end model productionisation A bias to simplicity, where you care most about achieving impact Background in personalisation, ranking, or recommender systems; exposure to causal inference or uplift modelling is a genuine plus but not expected Why Join Us? At Deliveroo, you'll do work that matters—solving real-world problems in a three-sided marketplace that's constantly evolving. We're food lovers, problem solvers, community builders and more, brought together by a shared drive to make things better. Working here you can expect to: 🔧 Solve meaningful problems at real scale Work on a complex, always-on marketplace that impacts millions every day. 🌱 See your impact, fast Ship, test and improve ideas quickly in a low-hierarchy, high-ownership environment. 🧠 Grow through challenge and ownership Take on big, ambiguous problems and accelerate your career with strong support. 🌎 A culture built for builders High standards, collaboration, flexible working and continuous learning. 💰 Share in the success you help create Competitive salary and equity options, so you're rewarded for the impact you make. ➡️ Want a deeper look at how we build? Check out our Tech Blog. Diversity, Equity and Inclusion At Deliveroo, we know that a great workplace reflects the world around us and that true diversity and inclusion make us stronger, more creative, and better at what we do. We’re committed to fostering an environment where everyone can do their best work and feel they belong. We believe in equality of opportunity and welcome candidates from all backgrounds regardless of age, gender, ethnicity, disability, sexual orientation, gender identity, socio-economic background, religion, or belief. If you have a disability or long-term health condition and need support to apply for one of our roles, or require any reasonable adjustments during the recruitment process, you’ll have the opportunity to let us know once you’ve submitted your application. We’ll share details on how to request support so we can ensure you have a fair and equitable experience. If you’re excited about making a real impact in a fast-moving marketplace and growing your career alongside ambitious, supportive teams, we’d love to hear from you! Beware of recruitment scams: DASH Brands will never ask you to pay money or share sensitive financial information during hiring — learn more about DASH Brands' legitimate recruiting process at careersatdoordash.com/recruitment-scam-awareness .

Location: London, England Our Global Structure Deliveroo is now part of DoorDash, bringing together teams with even greater reach, scale, and ambition. Depending on your role, you may collaborate with teammates, systems, and leaders across DoorDash and Wolt. Together, we’re unlocking new possibilities as one global team. Join us in our mission to transform the way people shop and eat, where impact, innovation and growth drive everything we do. Our Global Engineering teams tackle complex technical challenges across a global, three-sided marketplace, building and scaling systems that serve millions of customers, riders and partners every day. We're looking for a Machine Learning Engineer to join our London office as part of a Global DoorDash Engineering team (working hybrid, 3 days in the office). Our New Verticals team exists to grow monthly active usage outside of restaurant ordering — groceries, retail, and everything in between. That means turning someone who only orders restaurant food into someone who also uses the app for a grocery run, and turning dormant users into active ones. You'll work on the surfaces that make that happen: the homepage, vertical landing pages, and the notifications that bring people back. This role sits closer to the personalisation and ranking side of that mission, working alongside colleagues across DoorDash, Deliveroo and Wolt. What You'll Be Doing Build and improve ranking models for vertical landing pages (grocery, retail), deciding what a user sees first when they land on a store page Design models that blend new-vertical content into a homepage built around restaurant ordering, without disrupting the core experience Build personalisation models that translate a user's existing behaviour into relevant recommendations for verticals they haven't tried yet Contribute to notification targeting — who gets notified, with what message, and when, to drive adoption of new verticals What You'll Need to Thrive Minimum 3+ years of experience as an ML Engineer or Data Scientist, with a proven ability to write high-quality production code in Python Experience with end-to-end model productionisation A bias to simplicity, where you care most about achieving impact Background in personalisation, ranking, or recommender systems; exposure to causal inference or uplift modelling is a genuine plus but not expected Why Join Us? At Deliveroo, you'll do work that matters—solving real-world problems in a three-sided marketplace that's constantly evolving. We're food lovers, problem solvers, community builders and more, brought together by a shared drive to make things better. Working here you can expect to: 🔧 Solve meaningful problems at real scale Work on a complex, always-on marketplace that impacts millions every day. 🌱 See your impact, fast Ship, test and improve ideas quickly in a low-hierarchy, high-ownership environment. 🧠 Grow through challenge and ownership Take on big, ambiguous problems and accelerate your career with strong support. 🌎 A culture built for builders High standards, collaboration, flexible working and continuous learning. 💰 Share in the success you help create Competitive salary and equity options, so you're rewarded for the impact you make. ➡️ Want a deeper look at how we build? Check out our Tech Blog. Diversity, Equity and Inclusion At Deliveroo, we know that a great workplace reflects the world around us and that true diversity and inclusion make us stronger, more creative, and better at what we do. We’re committed to fostering an environment where everyone can do their best work and feel they belong. We believe in equality of opportunity and welcome candidates from all backgrounds regardless of age, gender, ethnicity, disability, sexual orientation, gender identity, socio-economic background, religion, or belief. If you have a disability or long-term health condition and need support to apply for one of our roles, or require any reasonable adjustments during the recruitment process, you’ll have the opportunity to let us know once you’ve submitted your application. We’ll share details on how to request support so we can ensure you have a fair and equitable experience. If you’re excited about making a real impact in a fast-moving marketplace and growing your career alongside ambitious, supportive teams, we’d love to hear from you! Beware of recruitment scams: DASH Brands will never ask you to pay money or share sensitive financial information during hiring — learn more about DASH Brands' legitimate recruiting process at careersatdoordash.com/recruitment-scam-awareness .

Задачи: — Управление командой из 4–6 человек: постановка масштабных целей, декомпозиция их в задачи, координация со смежными командами, контроль процесса от постановки задачи до внедрения в продакшен. — Обучение моделей и работа с данными: превращение данных, размеченных с помощью LLM, в датасеты для ML-моделей ранжирования, создание корпусов синтетических данных, дообучение открытых и внутренних LLM. — Создание пайплайнов для оценки обученных моделей, обеспечение многосторонней оценки моделей, работающих в продуктах с миллионами пользователей. Требования: — Опыт управления командой ML/DL-разработчиков. — Опыт обучения DL-моделей и их внедрения в продакшен. — Готовность работать руками, создавать прототипы и проверять гипотезы. Будет плюсом: — Опыт работы с высоконагруженными системами и создания моделей для них. — Опыт работы в кросс-функциональных командах. Условия: — ДМС с регулярными чекапами и вакцинацией. — Другие бонусы, полный список доступен по ссылке.