Вакансии

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: 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: — разработка, обучение и оптимизация ML/AI-моделей на PyTorch и/или TensorFlow — проектирование end-to-end ML-пайплайнов — работа с LLM, NLP, Computer Vision и Generative AI — подготовка моделей к production и масштабированию — интеграция моделей с backend — оптимизация inference и производительности — построение MLOps-процессов — работа с Docker и cloud-инфраструктурой — принятие архитектурных решений и техническое руководство AI-командой Требования: — от 4 лет коммерческого опыта в Machine Learning / AI Engineering — практический опыт разработки и внедрения ML-моделей в production — уверенный Python — PyTorch и/или TensorFlow — понимание Machine Learning и Deep Learning — знание ML-метрик и методов оптимизации моделей — Docker, Git — AWS или аналогичные cloud-платформы — практический опыт с MLflow, Kubeflow, AWS SageMaker или аналогичными MLOps-инструментами — свободный английский язык Условия: — full-time — офис в Ташкенте — официальное трудоустройство по ТК Узбекистана — вилка от $3,500

Location: Remote Workplace: Remote Our Team Dandelion Health unlocks a layer of unexplored clinical depth that has been previously inaccessible at scale. By combining all the raw multimodal data from community health systems with validated AI models, we deliver regulatory-grade scientific insights and products that give teams the evidence to design better trials, enroll the right patients, and generate AI-powered insights that drive confident decisions across the development and commercial lifecycle. Your Role You are a healthcare data scientist who knows your way around clinical and text-based data. Your primary responsibility will be to leverage and build upon large language models (LLM) and other ML-based approaches for meaningful data abstraction from both unstructured and structured healthcare data. You will join a team of data scientists who own the creation and maintenance of AI-ready datasets for clients in our environment. You will work across the organization to curate datasets by identifying patient subpopulations or disease cohorts, developing methodology to abstract information from multimodal healthcare data sources to support patient phenotyping, and pooling data to enable rapid exploratory AI/ML analyses, model experimentation and/or model validation. You will use your data expertise, programming abilities, and critical thinking skills to support our technical product team, and develop your own analyses to derive insights and enhance our datasets based on the use case. Your team’s ultimate goal is to deliver the highest-quality data possible to our clients, who are building products that improve patient health. You will report to the Data Science Manager, under the Head of Data.   Responsibilities Your day-to-day responsibilities will include the following: - Develop Natural Language Processing (NLP), Large Language Model (LLM) and other ML-based pipelines to abstract relevant labels from text-based healthcare data and store them in scalable data models; - Query complex source systems in a range of health data sources (e.g., EMRs, semi-structured reports, free-text clinical provider notes) to identify key data elements and create and enrich high-quality datasets for real-world evidence analyses and training AI algorithms; - Own data extraction, wrangling, labeling and QC tasks to create analytical datasets that include abstracted clinical concepts and provide a range of solutions to support customers’ AI activities; - Stay current on the latest in applied NLP and generative AI methods and proactively leverage these technologies where applicable; - Support the design, testing, validation, analysis, and merging of multimodal data structures from a wide variety of source systems; - Develop code and documentation to deliver high-quality and HIPAA-compliant data products on time to customers; - Identify and resolve problems using your knowledge, background, and troubleshooting skills; - Ensure accuracy, data integrity, and validity of data and analysis in all work; - Provide support for technical product team to advance development of the suite of data-related product offerings; - Summarize the complexity of abstraction methods, findings and recommendations into clear explanations and presentations for internal and external audiences that have a varying range of technical and clinical experience; You are not afraid to dig into massive, confusing, disorganized new datasets and get them under control. You are excited to learn new environments, languages, and skills. This is a small, early stage company with enormous ambitions and everyone pitches in across the team.   Qualifications - Advanced degree in a quantitative field (ex. Data Science, Biomedical Informatics, Computer Science, Biostatistics), or B.S. with at least 5 years of professional experience - At least 2 years of data science and machine learning experience, including building pipelines to extract and curate unstructured and semi-structured data by applying advanced machine learning and AI techniques. Prior experience with clinical and healthcare data is a strong bonus. - Fluency in Python and SQL, including fluency with ML/NLP libraries (PyTorch, Tensorflow, HuggingFace, etc.) - Familiarity with using modern applied LLM techniques on real-world data - Strong technical writing, editing, and communication skills, along with a collaborative mindset - Excellent organizational skills with an ability to embrace change and effectively manage multiple projects and consistently plan work to meet deadlines - Experience working in or with startups is a plus Technical Experiences and Skills   We don’t expect anyone to have all of the following skills or experiences, but we do seek candidates who are interested in growing their skill sets and working with healthcare data in all its glorious complexity. The Data Team works closely with our Engineering Team to put our work into production and meet client needs. - Git and version control - Familiarity with encryption methods - Prior experience querying EDWs or databases and creating reports or analytics for healthcare data - Familiarity with the data aspects of electronic medical records, ex. Epic, Cerner, Allscripts - Any medical ontology experience - Any experience working with DICOM or other imaging modalities - Experience with AWS - Experience with publishing work in peer-reviewed journals Nature of our work   Our work is fast paced and iterative. We are growing, and we want to support our team members to grow in their skills as well. We are building a team that approaches problems with a diversity of perspectives, values experimentation, and refining our approach based on that experimentation. We work with the full spectrum of healthcare data from tabular data, videos, images, waveforms, etc. If a health system collects it, we might work with it! If this looks like a partial fit, please reach out, we would love to share more about the work we do for you to understand if it would be a good fit for you. There is occasional travel for in-person company working days on roughly a quarterly basis. Team Benefits - Remote work and flexible hours. Availability needed for meetings, which we try to keep to a healthy minimum - Complete wellness benefits including healthcare, dental, vision, PTO, sick days and more. Ask for details - Professional development days to build your skills - Collegial work environment - Academic bent towards inquiry and problem solving but start-up speed and flexibility - Great balance of focus time to work on projects but easy to access team members to discuss issues and work collaboratively - Dandelion is a mission-driven company that is focused on improving patient care

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.

Neoai IT Center izlayapti mutaxassislarni bir nechta yo'nalishlarda: backend dasturlash (python, Django, Fastapi, Node.js, Express.js, Java, Spring Boot, PHP, Laravel, Rest API, Postgresql, Mysql, Mongodb, Git, Docker), frontend (HTML, CSS, JavaScript, TypeScript, React, Next.js, Vue.js, Angular, Tailwind CSS, Bootstrap, Git), mobil dasturlash (Dart, Flutter, Kotlin, Java, Swift, React Native, Firebase, Android Studio, Xcode, Git), UX/UI dizayn (Figma, Adobe XD, Photoshop, Illustrator, Figjam, UI Design, UX Research, Wireframing, Prototyping, Design System), kiberxavfsizlik (Linux, Python, Bash, Networking, TCP/IP, Wireshark, Nmap, Burp Suite, Metasploit, OWASP, Git, Docker), data science (Python, Numpy, Pandas, Matplotlib, Seaborn, Scikit-learn, TensorFlow, PyTorch, SQL, Jupyter Notebook, Git), data analitika (Python, Pandas, Numpy, SQL, Excel, Power BI, Tableau, Matplotlib, Seaborn, Jupyter Notebook), sun'iy intellekt (Python, PyTorch, TensorFlow, Keras, Scikit-learn, Hugging Face, OpenAI API, Langchain, Numpy, Pandas, Git, Docker), robototexnika (Python, C++, Arduino, ESP32, Raspberry Pi, ROS, Matlab, Arduino IDE, OpenCV, Sensors, Git). Ish joyi Toshkent shahrida, ish vaqti 3 soat, maosh 4-5 million so'm. Vazifalar: o'quvchilarga zamonaviy IT texnologiyalarini nazariya va amaliyot asosida o'rgatish, darslarni amaliy loyihalar va real vazifalar bilan tashkil etish, o'quvchilar bilan individual ishlash va ularning rivojlanishini kuzatib borish, dars materiallari va amaliy topshiriqlar tayyorlash. Nomzodlar mas'uliyatli, kommunikativ bo'lishi, o'z yo'nalishi bo'yicha amaliy tajribaga ega bo'lishi, yangi texnologiyalarni muntazam o'rganib borishi talab etiladi. Shuningdek, tajribali mutaxassislar bilan birga professional muhitda rivojlanish imkoniyati mavjud.

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.

Задачи: — проводить аналитику и мониторинг работы моделей — собирать и подготавливать данные, последующий EDA, feature engineering — разрабатывать новые ML-модели — оптимизировать скрипты data/model/inference pipelines — подготавливать код проекта к проду, подготавливать, дизайнить и проводить АБ-тесты. Требования: — высшее образование по техническому направлению — от года релевантного опыта в ML — прочный математический фундамент (понимание градиентного спуска, характеристик основных распределений, PCA, МНК, базовые статистические тесты проверки гипотез) — умение и желание писать код на Python, оптимизировать его, разбираться в чужом коде, понимание SQL — владение основными библиотеками для анализа данных: pandas, numpy, scipy, matplotlib, statmodels — глубокое понимание основных алгоритмов классического ML (линейная/логистическая регрессия, деревья решений, ансамбли), базовые знания по DL (основные архитектуры, тонкости обучения) — владение основными фреймворками для ML/DL: sklearn, pytorch, transformers и др. Будет плюсом: — интерес к методам ML в задачах ценообразования и финансах — изучение Reinforcement Learning, multi-arms bandits — чтение статей по современным решениям для работы с последовательностями. Условия: — комфортный современный офис рядом с м. Кутузовская — гибридный формат работы — ежегодный пересмотр зарплаты, квартальная и годовая премия — корпоративный спортзал и зоны отдыха — более 400 образовательных программ СберУниверситета — программа адаптации и помощь руководителя на старте (для Junior) — расширенный ДМС, льготное страхование для семьи, корпоративная пенсионная программа — гибкий дисконт по ипотечному кредиту, равный 1/3 ключевой ставки ЦБ — подписка Прайм с возможностью совместного использования на трёх близких — вознаграждение за рекомендацию друзей в команду Сбера.

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.

Задачи: — Проектировать, разрабатывать и обслуживать масштабируемые серверные системы для поддержки развертывания. — Создавать надежные и эффективные API в платформу GotIt. — Создавать системы хранения и поиска данных, оптимизированные для обработки больших объемов текстовых данных, необходимых для задач обучения и вывода. — Выявлять проблемные места в производительности и оптимизировать, повышая эффективность и оперативность серверных служб. — Внедрять передовые методы обеспечения безопасности для защиты конфиденциальных данных и обеспечения соответствия отраслевым стандартам и правилам. Требования: — Степень бакалавра или магистра в области компьютерных наук, инженерии или смежных областях. — Минимум 5 лет опыта в серверной разработке с упором на проектирование и внедрение масштабируемых систем. — Опыт серверной разработки на Kotlin, либо Java, но с переходом на Kotlin. — Знание английского языка. — Опыт работы с реляционными базами данных (PostgreSQL) и разработка эффективных моделей данных. — Опыт проектирования и разработки RESTful API и использования WebSocket. — Знание облачных платформ AWS, Azure или Google Cloud, опыт развертывания и управления серверными службами в облаке. — Понимание принципов DevOps и опыт работы с Docker, Kubernetes, CI/CD. — Знания в области машинного обучения, знакомство с TensorFlow, PyTorch является плюсом. — Опыт работы с технологиями и платформами обработки естественного языка (NLP). — Опыт работы в Agile. — Вклад в проекты с открытым исходным кодом или активное участие в технических сообществах. — Опыт работы с технологиями больших данных, такими как Apache Hadoop или Spark. Условия: — Частичная занятость. — Возможность присоединения к международной команде на постоянной основе на полный день. — Возможность стать руководителем направления. — Возможность путешествий/переезда в США.

Location: Москва **ИТ B2C** — самая крупная экосистема в Сбере. Нас более 8000 человек в 18 городах России. Мы занимаемся разработкой и развитием розничных решений, помогая сделать сервисы Банка доступнее, безопаснее и удобнее. Ждем именно тебя! Мы — команда экспертов, объединённых общей страстью к искусственному интеллекту и рекомендательным системам. Наша главная задача — создание современной масштабируемой рекомендательной платформы, способной предвосхищать ожидания пользователей и предлагать персонализированные рекомендации на каждом этапе их взаимодействия с экосистемой Сбер. Наши решения охватывают широкий спектр отраслей: от финансов и e-commerce до индустрии развлечений и здравоохранения. Мы следим за развитием технологий, экспериментируем с новыми ML-подходами, внедряем их в платформу и доводим до конкретного применения в бизнесе. Мы ищем **Lead ML Engineer**, который будет отвечать за технологическое развитие направления рекомендательных систем и формирование единой инженерной и операционной среды для DS- и MLE-команд. В зоне ответственности — скорость и воспроизводимость проверки DS-гипотез, качество разработки и вывода моделей в production, а также надёжная эксплуатация ML-решений с предсказуемыми качеством и стоимостью. Обязанности * формировать технологическое видение и roadmap развития ML Engineering и MLOps в направлении рекомендательных систем, определяя ключевые инженерные приоритеты и точки роста * развивать единый жизненный цикл ML-решений — от формулирования и воспроизводимой проверки DS-гипотез до запуска моделей в production, мониторинга и дальнейшего сопровождения * создавать инженерные стандарты и практики, которые помогают командам выпускать качественные и надёжные модели: требования к коду и данным, тестированию, версионированию, документированию, релизам * управлять портфелем технологических инициатив, находя баланс между развитием новых возможностей, повышением надёжности и устранением архитектурных ограничений * повышать производительность DS/MLE команд за счёт автоматизации, стандартизации, переиспользуемых компонентов и упрощения пути от эксперимента до production * развивать применение GenAI в процессе создания ML-решений: находить наиболее перспективные сценарии, запускать пилоты и оценивать их влияние на скорость и качество разработки * повышать масштабируемость, надёжность и экономическую эффективность ML-инфраструктуры * развивать ML-инженеров и технических лидеров направления. Требования * опыт технического лидерства в крупном ML-направлении не менее двух лет: умение формировать технологическое видение и roadmap, а также доводить системные изменения до результата * глубокое понимание полного жизненного цикла ML-решений — от эксперимента и обучения модели до production, мониторинга, масштабирования и дальнейшего сопровождения * опыт проектирования и развития высоконагруженных ML-систем, включая batch-, streaming- и real-time-сценарии * опыт взаимодействия с продуктовыми, платформенными и инфраструктурными командами, включая согласование приоритетов и защиту потребностей своего направления * умение принимать решения в условиях конфликтующих приоритетов, объяснять компромиссы и доводить выбранные инициативы до результата * опыт people management в DS/MLE командах: найм и адаптация сотрудников, постановка целей, регулярная обратная связь, performance review, развитие и удержание специалистов, а также формирование среды для обмена знаниями и распространения сильных инженерных практик * опыт внедрения GenAI-инструментов в процессы разработки и оценки их влияния на скорость и качество работы команд. **Будет большим плюсом:** * опыт работы с рекомендательными системами, поиском, ранжированием, персонализацией или рекламными технологиями * практический опыт работы в роли Data Scientist: формулирование и проверка ML-гипотез, оценка качества моделей и участие в A/B-тестах. * опыт эксплуатации real-time ML-систем на масштабе миллионов пользователей и при строгих требованиях к latency и availability. **Стек технологий:** Python, PySpark, PyTorch, RePlay, GigaChat, Airflow, MLflow, Kubernetes, Redis, Kafka, S3, FastAPI и другие инструменты для разработки, обучения, внедрения и мониторинга ML-моделей. Условия * комфортный современный офис рядом с м. Кутузовская * ежегодный пересмотр зарплаты и годовая премия * корпоративный спортзал и зоны отдыха * более 400 образовательных программ СберУниверситета для профессионального и карьерного развития   * расширенный ДМС, льготное страхование для семьи и корпоративная пенсионная программа * гибкий дисконт по ипотечному кредиту, равный 1/3 ключевой ставки ЦБ * подписка Прайм с возможностью совместного использования на трёх близких * вознаграждение за рекомендацию друзей в команду Сбера

Задачи: — исследовать и разрабатывать торговые стратегии для DeFi и крипторынков: от гипотезы и анализа данных до бэктестинга, валидации и оценки результата стратегии — Quant/ML research и разработка торговых стратегий для DeFi и крипторынков — применять ML/DL-подходы для маркет-мейкинга, forecasting, управления позициями и смежных задач — разрабатывать стратегии дельта-нейтрального хеджирования и управления рисками — проводить бэктестинг и использовать симуляционные среды на крипто-данных: order book, trades — анализировать рыночные данные и улучшать алгоритмы по результатам реального исполнения Требования: — senior-уровень в Quant/ML research, systematic trading, HFT/prop trading, market microstructure или смежных задачах — участие в полном цикле ресерча: гипотеза > данные > модель/логика > бэктестинг > валидация и анализ результата — понимание временных рядов, OOS-проверки, future leakage и торговой экономики — знание Supervised Learning, статистического анализа, оптимизации моделей — уверенный Python; PyTorch, Scikit-Learn, CatBoost, XGBoost, Optuna — опыт доведения стратегий до реального исполнения или PnL-оценки — опыт работы с order book и trades — автономность в постановке гипотез и доведении их до результата — владение русским языком для общения в команде Будет плюсом: — Reinforcement Learning — опыт на Rust или low-latency engineering — опыт в трейдинге, маркет-мейкинге, low-latency крипте — опыт в квант-фондах, HFT, prop trading, банковских квант-командах — опыт с DeFi, DEX/CEX, AMM/LP, perpetuals, funding, cross-venue arbitrage — активное использование AI-инструментов Условия: — фиксированная зарплата в стейблкоинах (USDT/USDC, 1:1 к доллару) — квартальный profit sharing от результатов стратегий — оплачиваемый отпуск — компания оплачивает AI-подписки

Location: San Francisco, CA; Sunnyvale, CA Workplace: Hybrid At Niantic Spatial, we’re building the future of physical AI. Powered by a proprietary database of over 30 billion posed images, our groundbreaking mapping technology unlocks a new dimension of interaction and spatial intelligence that helps both humans and machines better understand, represent, navigate, and engage with the real environment. Join us as we build the real-world foundational models for physical AI, so people and machines can work efficiently and safely together. As a Senior Computer Vision Engineer on the Applied Algorithms team, you will own significant parts of our Localization technology. As a member of this team, you will be responsible for building the Map representing a living geospatial world model, as well as the centimeter-level accuracy Visual Positioning System (VPS) that allows people and robots to explore the real world. Part of the role will be bridging the gap between R&D and production, turning complex geospatial data into a persistent sense of space and enabling the next generation of spatial AI. RESPONSIBILITIES - System Architecture: Design, develop, and maintain production-grade computer vision systems that power Niantic Spatial’s VPS (Visual Positioning System) and 3D mapping pipelines. - Algorithmic Innovation: Refine and scale algorithms for our VPS system including, SfM (Structure from Motion) and Feed Forwards models, efficient feature and descriptor extraction to have the best possible accuracy and recall. Moving algorithms from research concepts to hardened production code that can scale for our map of the world. - Performance Engineering: Optimize complex ML and CV code for maximum efficiency in cloud and mobile environments, ensuring low latency and high-performance execution on GPU/CPU. - Benchmarking & Evaluation: Create and own the tools and frameworks used to evaluate the quality of our spatial grounding and 3D maps against ground-truth data. - Technical Leadership: Lead technical design reviews, mentor junior engineers, and serve as a team anchor for resolving complex technical disagreements within the mapping stack. - Cross-Functional Delivery: Collaborate with Product, Research, and Operations teams to ensure that state-of-the-art computer vision solutions translate into delightful user experiences. REQUIREMENTS - Education: BS, MS, or PhD in Computer Science, Robotics, Computer Vision, or a related technical field (or equivalent professional experience). - Years of Experience: 5+ years of experience developing and shipping algorithms for image processing, computer vision, or 3D reconstruction. - Coding Proficiency: Expert-level proficiency in python and/or C++. - Domain Expertise: Proven track record in designing solutions for Structure from Motion (SfM), VPS or 3D mapping.. - Frameworks & Tools: Deep experience with Deep Learning frameworks (PyTorch or JAX) and version control (Git). - Required in-office days: 3 days per week Plus If: - Experience in planning and leading technical projects from inception to production. - Significant contributions to open-source CV libraries (OpenCV, COLMAP, etc.). - Experience with CUDA or shader programming for performance optimization. Candidate Privacy Policy I understand that by submitting my job application, the information I provide as part of that application will be used in accordance with Niantic Spatial’s Privacy Notice for Job Applicants and Candidates https://www.nianticspatial.com/applicant-privacy-notice. If required by law, by submitting my job application I consent to the processing of my information as described in that Notice, including processing information I voluntarily disclose to Niantic Spatial, such as health or medical information, race or ethnicity data, and sexual orientation data and, in limited circumstances sharing information with third parties such as references and other third parties that assist in the hiring process. Niantic Spatial is an equal opportunity employer. Individuals seeking employment at Niantic Spatial are considered without regard to race, color, ancestry, national origin, religion, creed, age, gender (including pregnancy, childbirth, breastfeeding or related medical conditions), marital status, physical or mental disability, medical condition, genetic information, military or veteran status, gender identity, gender expression, sexual orientation, or any other protected category under applicable laws. Niantic Spatial, will also consider qualified applicants with criminal histories in accordance with applicable laws. Please contact your recruiter if you want to request an accommodation for the job application or interview process.

Location: UK, London Workplace: OnSite Here at Humanoid, we believe in a future where robots amplify human potential. That’s why we’ve set out on a mission to build the world’s most capable, commercially-scalable, and safe humanoid robots. We’re bringing that mission to life with HMND‑01 - our rapidly developed humanoid platform being deployed in real industrial environments - and we’re growing the team to take it even further. ABOUT THE ROLE We're hiring a Senior Autonomy Engineer focused on Spatial Intelligence to join our team. In this role, you will bridge the gap between classical spatial geometry and next-generation AI. You will lead the design and development of robust state estimation, long-term mapping, and localisation loops, with a specific focus on integrating modern machine learning techniques and foundation geometry models into our core navigation stack. Your work will ensure our robots possess rock-solid spatial awareness and execute highly reliable trajectories over extended deployments in complex, real-world environments. WHAT YOU'LL DO - Architect the spatial intelligence layer that unifies 3D geometry with semantic scene understanding to ensure safe and efficient navigation and locomotion in complex environments - Develop advanced 3D spatial reasoning capabilities that provide our robots with long-term persistent spatial memory and a deep contextual awareness of their surroundings. - Own the entire perception lifecycle by training large-scale multimodal networks and deploying and integrating them for ultra-low latency performance on edge hardware - Bridge the gap between conceptual research and physical reality by implementing state-of-the-art 3D representations that track and interpret the environment state - Collaborate with cross-functional teams to bridge the gap between large-scale model training and reliable robot integration. - Stay ahead of the field, rapidly evaluate new model architectures, benchmarks, and datasets to guide our embodied AI roadmap. WHAT WE'RE LOOKING FOR - Exceptional software engineering skills with a proven track record of shipping complex ML systems into production. - Strong background in 3D scene understanding and the design of efficient spatial data structures. - Deep experience in machine learning for vision or embodied AI, ideally with large models (VLMs, transformers, or multi-modal architectures). - Proficiency in PyTorch and hands-on experience building, fine-tuning, and deploying large-scale ML systems. - High-impact individual contributor capable of taking projects from conceptual research to high-performance robot code. - Comfortable working in a fast-moving, research-driven environment with evolving models, data, and tools. WHAT WE OFFER - Competitive equity: stock options with meaningful upside as we scale. - 30+ paid days off, including 23 days of annual leave, all UK bank holidays, and additional company closure days (including Christmas–New Year shutdown). - Private healthcare, including virtual and in-person care. - Pension scheme with 8% total contribution (5% employee, 3% employer) on full earnings. - Free daily breakfast, catered lunch, and snacks in-office. - Work at the frontier - collaborate daily with world-class engineers, researchers, and product experts building the next generation of AI and humanoid robotics. - Real ownership - direct access to founding leadership, meaningful input on product direction, and the ability to drive key initiatives from day one.

Задачи: — Проектировать, разрабатывать и обслуживать масштабируемые серверные системы для поддержки развертывания. — Создавать надежные и эффективные API в платформу GotIt. — Создавать системы хранения и поиска данных, оптимизированные для обработки больших объемов текстовых данных. — Выявлять проблемные места в производительности и оптимизировать серверные службы. — Внедрять передовые методы обеспечения безопасности для защиты конфиденциальных данных и соответствия стандартам. Требования: — Степень бакалавра или магистра в области компьютерных наук, инженерии или смежных областях. — Минимум 5 лет опыта в серверной разработке с упором на проектирование и внедрение масштабируемых систем. — Опыт серверной разработки на Kotlin или Java с переходом на Kotlin. — Опыт продумывания архитектуры приложения и проработки ключевого функционала. — Знание английского языка. — Опыт работы с реляционными базами данных (PostgreSQL) и разработка эффективных моделей данных. — Опыт проектирования и разработки RESTful API и использования WebSocket. — Знание облачных платформ AWS, Azure или Google Cloud и опыт управления серверными службами в облаке. — Понимание принципов DevOps и опыт работы с Docker, Kubernetes, CI/CD. — Знания в области машинного обучения (TensorFlow, PyTorch) являются плюсом. — Опыт работы с технологиями и платформами обработки естественного языка (NLP). — Опыт работы в Agile. — Вклад в проекты с открытым исходным кодом или участие в технических сообществах. — Опыт работы с технологиями больших данных, такими как Apache Hadoop или Spark. Условия: — Удаленная работа. — Частичная занятость. — Возможность присоединиться к международной команде на полный день. — Перспектива стать руководителем направления. — Возможность путешествий и переезда в США.

Задачи: — Руководить командой Computer Vision Engineers — Проектировать архитектуру production AI-систем — Менторить инженеров и проводить code review — Декомпозировать задачи и контролировать качество реализации — Разрабатывать пайплайны обработки десятков и сотен RTSP-потоков — Оптимизировать инференс на GPU и CPU — Принимать ключевые технические решения — Участвовать в найме и развитии AI-команды Основной стек: Python, PyTorch, OpenCV, YOLO, RTSP, GStreamer, NVIDIA DeepStream, TensorRT, Triton Inference Server, ONNX, OpenVINO, CUDA, FastAPI/gRPC, Docker и Kubernetes. Что важно: ✅ Сильный коммерческий опыт в Computer Vision и Video Analytics ✅ Опыт технического лидерства или менторства ✅ Запуск production AI-систем ✅ Уверенное знание Python, PyTorch, OpenCV и YOLO ✅ Практический опыт с RTSP, GStreamer, DeepStream и TensorRT ✅ Оптимизация моделей через ONNX, TensorRT, OpenVINO, CUDA или Quantization ✅ Понимание высоконагруженной обработки видеопотоков ✅ Умение принимать и аргументировать архитектурные решения Будет плюсом: ➕ Triton Inference Server ➕ C++ и CUDA ➕ Kafka и потоковая обработка данных ➕ MLflow и MLOps ➕ ViT, DETR, Swin и Video Transformers ➕ VLM, RAG, LangGraph, vLLM и Function Calling ➕ Опыт найма и формирования AI-команд Предлагаем влияние на архитектуру и технологический стек, сильную инженерную команду и работу над высоконагруженной системой промышленного уровня.

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 . Team Description: The Ads Retrieval ML team builds the machine learning systems that identify relevant advertising candidates for Reddit users. Retrieval sits at the heart of the ads delivery funnel: before downstream ranking and auction decisions, our models determine which campaigns and ads are eligible to compete. We work on large-scale retrieval across multiple objectives, placements, and geographies. Our work combines representation learning, candidate generation, nearest-neighbor search, behavioral and contextual signals, and rigorous offline and online experimentation. Role Description: We are looking for a Staff Machine Learning Engineer to provide technical leadership for the Retrieval ML team. You will lead the design and evolution of retrieval models and modeling practices that improve relevance, advertiser outcomes, and user experience at Reddit scale. This is an applied ML role centered on retrieval modeling and end-to-end product impact. You will be expected to stay close to the technical details—from data and objective design through model development, evaluation, experimentation, and launch—while setting direction for other engineers. Responsibilities: Define the technical direction and multi-year roadmap for ads retrieval modeling in partnership with engineering, product, data science, and ads stakeholders. Design, develop, and launch candidate-generation and retrieval models for campaigns and ads across Reddit’s advertising surfaces. Apply modern approaches such as two-tower architectures, representation learning, embeddings, sequence models, graph-based methods, and other deep learning techniques when they create meaningful product value. Improve the retrieval stack across key modeling decisions, including objectives, labels, sampling strategies, hard-negative mining, feature design, embedding generation, candidate filtering, and retrieval depth. Work with approximate nearest-neighbor and vector retrieval systems, reasoning about recall, relevance, freshness, diversity, coverage, latency, and cost trade-offs. Establish strong evaluation practices that connect retrieval metrics—such as recall, precision, candidate coverage, calibration, and downstream lift—to ads and user outcomes. Lead offline analysis and online experiments, interpret ambiguous results, and translate findings into the next modeling iteration. Partner with downstream ranking, ads platform, auction, measurement, and product teams to ensure retrieval models integrate effectively into the full ads funnel. Write design documents, review code and model changes, and raise the quality bar for modeling, testing, observability, and production ownership. Mentor ML engineers and help grow the team’s expertise in retrieval, recommendation, and representation learning. Required Qualifications: 7+ years of industry experience, including substantial experience building and shipping applied ML products. Deep experience with information retrieval, candidate generation, recommender systems, ranking, or related relevance problems. Strong understanding of retrieval modeling concepts, including DNN, embeddings, two-tower or dual-encoder models, approximate nearest-neighbor search, and multi-stage retrieval. Deep experience training, evaluating, debugging, and deploying deep learning models using TensorFlow, PyTorch, or similar frameworks. Demonstrated ownership of ML projects from problem framing and data preparation through offline evaluation, online experimentation, production launch, and iteration. Strong command of experimental design and model evaluation, including how offline retrieval metrics relate to downstream business and user metrics. Experience working with large-scale behavioral, contextual, or content datasets and complex feature pipelines. Strong software engineering fundamentals and the ability to write clear, reliable, maintainable production code. Technical leadership experience: setting direction, leading complex projects, influencing partner teams, and mentoring other engineers. Excellent written and verbal communication, with the ability to explain complex modeling choices to technical and non-technical audiences. Preferred Qualifications: Experience with ads retrieval, ad serving, recommendation, search relevance, or marketplace optimization Experience modeling user, content, campaign, or ad interactions with sequential, graph, or multimodal signals Experience connecting retrieval improvements to downstream ranking, auction, conversion, revenue, or user-experience outcomes Experience in ads marketplaces at peer companies Publications, patents, or industry contributions in applied ML or ranking systems Experience with sequential modeling (e.g., RNNs, Transformers) Benefits: 100% remote opportunity (we have 4 office locations for hybrid/onsite work preference in NY, SF, LA and Chicago) 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-AJ1 Pay Transparency: This job posting may span more than one career level. In addition to base salary, this job is eligible to receive equity in the form of restricted stock units, and depending on the position offered, it may also be eligible to receive a commission. Additionally, Reddit offers a wide range of benefits to U.S.-based employees, including medical, dental, and vision insurance, 401(k) program with employer match, generous time off for vacation, and parental leave. To learn more, please visit https://www.redditinc.com/careers/ . To provide greater transparency to candidates, we share base salary ranges for all US-based job postings regardless of state. We set standard base pay ranges for all roles based on function, level, and country location, benchmarked against similar stage growth companies. Final offer amounts are determined by multiple factors including, skills, depth of work experience and relevant licenses/credentials, and may vary from the amounts listed below. The base salary range for this position is: $230,000 — $322,000 USD In select roles and locations, the interviews will be recorded, transcribed and summarized by artificial intelligence (AI). You will have the opportunity to opt out of recording, transcription and summarization prior to any scheduled interviews. During the interview, we will collect the following categories of personal information: Identifiers, Professional and Employment-Related Information, Sensory Information (audio/video recording), and any other categories of personal information you choose to share with us. We will use this information to evaluate your application for employment or an independent contractor role, as applicable.  We will not sell your personal information or disclose it to any third party for their marketing purposes.  We will delete any recording of your interview promptly after making a hiring decision.  For more information about how we will handle your personal information, including our retention of it, please refer to our Candidate Privacy Policy for Potential Employees and Contractors . Reddit is proud to be an equal opportunity employer, and is committed to building a workforce representative of the diverse communities we serve.  Reddit is committed to providing reasonable accommodations for qualified individuals with disabilities and disabled veterans in our job application procedures. If, due to a disability, you need an accommodation during the interview process, please let your recruiter know.

Location: New York WorldQuant develops and deploys systematic financial strategies across a broad range of asset classes and global markets. We seek to produce high-quality predictive signals (alphas) through our proprietary research platform to employ financial strategies focused on market inefficiencies. Our teams work collaboratively to drive the production of alphas and financial strategies – the foundation of a balanced, global investment platform. WorldQuant is built on a culture that pairs academic sensibility with accountability for results. Employees are encouraged to think openly about problems, balancing intellectualism and practicality. Excellent ideas come from anyone, anywhere. Employees are encouraged to challenge conventional thinking and possess an attitude of continuous improvement. Our goal is to hire the best and the brightest. We value intellectual horsepower first and foremost, and people who demonstrate an outstanding talent. There is no roadmap to future success, so we need people who can help us build it. Technologists at WorldQuant research, design, code, test and deploy firmwide platforms and tooling while working collaboratively with researchers and portfolio managers. Our environment is relaxed yet intellectually driven. We seek people who think in code and are motivated by being around like-minded people. The Role We are seeking an exceptional senior-level Python engineer to join a small team working on complex data pipelines, AI/ML systems, and cutting-edge software solutions. This role will be responsible for managing technical objectives, providing technical leadership, and maintaining a hands-on approach to development. The ideal candidate will work closely across teams within WorldQuant as part of our business-facing technology organization. A successful candidate will possess deep expertise in Python development, data engineering, software architecture, and design principles. They should be able to mentor junior team members, conduct code reviews, and drive architectural decisions. Experience with AI and large language models (LLMs) is highly desirable. What You'll Bring Master's degree or higher in Computer Science, Engineering, or a related technical field from a top-tier institution. 7+ years of experience as a Python developer, with a strong focus on data engineering and AI/ML systems. Expert-level knowledge of Python and its ecosystem, including experience with data processing libraries like Pandas, NumPy, and PySpark. Proficiency in designing and implementing scalable, maintainable, and efficient data pipelines. Experience with cloud platforms (AWS, GCP, or Azure) and containerization technologies (Docker, Kubernetes). Expertise in version control systems (Git), CI/CD practices, and agile methodologies. Strong communication skills with the ability to explain complex technical concepts to both technical and non-technical stakeholders. Experience in the finance industry is a plus but not required. Experience with AI/ML frameworks such as PyTorch, or scikit-learn, LLM, agents or systems of agents is a significant plus. Our Benefits: Core Benefits: Fully paid medical and dental insurance for employees and dependents, flexible spending account, 401k, fully paid parental leave, generous PTO (paid time off) that consists of: Twenty vacation days that are pro-rated based on the employee’s start date, at an accrual of 1.67 days per month, three personal days, and ten sick days. Perks: Employee discounts for gym memberships, wellness activities, healthy snacks, casual dress code Training: learning and development courses, speakers, team-building off-site Employee resource groups Pay Transparency: WorldQuant is a total compensation organization where you will be eligible for a base salary, discretionary performance bonus, and benefits. To provide greater transparency to candidates, we share base pay ranges for all US-based job postings regardless of state.  We set standard base pay ranges for all roles based on job function and level, benchmarked against similar stage organizations.  When finalizing an offer, we will take into consideration an individual’s experience level and the qualifications they bring to the role to formulate a competitive total compensation package. The Base Pay Range For This Position Is $175,000 – $250,000 USD. At WorldQuant, we are committed to providing candidates with all necessary information in compliance with pay transparency laws.  If you believe any required details are missing from this job posting, please notify us at WQHR_US@WorldQuant.com , and we will address your concerns promptly. #LI-RS1 By submitting this application, you acknowledge and consent to terms of the WorldQuant Privacy Policy. The privacy policy offers an explanation of how and why your data will be collected, how it will be used and disclosed, how it will be retained and secured, and what legal rights are associated with that data (including the rights of access, correction, and deletion). The policy also describes legal and contractual limitations on these rights. The specific rights and obligations of individuals living and working in different areas may vary by jurisdiction. Copyright © 2025 WorldQuant, LLC. All Rights Reserved. WorldQuant is an equal opportunity employer and does not discriminate in hiring on the basis of race, color, creed, religion, sex, sexual orientation or preference, age, marital status, citizenship, national origin, disability, military status, genetic predisposition or carrier status, or any other protected characteristic as established by applicable law.