Вакансии

Задачи: — участвовать в полном цикле разработки AI-агентов и универсальных ассистентов: от идеи и проектирования до запуска в продакшн — проводить эксперименты, тестировать гипотезы и исследовать новые подходы для создания агентских решений — разрабатывать и внедрять LLM-пайплайны, чат-ботов и интеллектуальные системы — обучать и дообучать NLP-модели под конкретные задачи продукта — интегрировать разработанные модели и логику в бизнес-процессы — мониторить качество работы моделей в продакшне, находить узкие места и оперативно вносить улучшения. Требования: — опыт в NLP и понимание технологий: от классических алгоритмов до трансформеров и генеративных моделей (LLM) — практический опыт коммерческой разработки AI-агентов, чат-ботов и LLM-пайплайнов — опыт обучения глубоких нейронных сетей — уверенное владение PyTorch, NumPy, Pandas, а также фреймворками для работы с агентами: LangChain, LangGraph (или их аналогами) — уверенное владение Python, Git, SQL, Bash, Docker. Умение писать чистый, поддерживаемый и тестируемый код — навык перевода бизнес-требований в конкретные технические и алгоритмические решения. Будет плюсом: — понимание принципов микросервисной архитектуры и опыт проектирования REST API — опыт разработки асинхронных и многопоточных приложений (asyncio, aiohttp, multiprocessing и др.) — опыт взаимодействия с реляционными и нереляционными базами данных — практический опыт настройки и поддержки CI/CD-пайплайнов (Jenkins, GitLab CI, ArgoCD или аналоги) — опыт развёртывания и управления приложениями в Kubernetes. 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 для профессионального и карьерного развития — программа адаптации и помощь руководителя на старте — 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.

Location: Remote - India AI Engineer - ASR: PraDigi, a Pratham Initiative About Pratham International Pratham International is a US 501(c)(3) established to support learning innovations for children and youth in contexts across the globe. We enable the adaptation and spread of proven and scalable education solutions pioneered in India by Pratham Education Foundation to contexts across the global south through programming and technical assistance tailored to local realities.  Today, Pratham reaches approximately 7.5 million children annually across 23 Indian states and union territories and an additional 1 million across 31 countries.. Through the Pratham Shah PraDigi Innovation Centre (PraDigi), Pratham International also seeks to create technology innovations for low-resource communities, and support models aimed at learning for school, work, and life for children and youth. We work on several cutting-edge projects in the domain of Generative AI, Automatic Speech Recognition, and Natural Language Processing. The team’s innovative technology products include the Anytime Testing Model (ATM), an AI-enabled question generation and grading system that allows learners to be assessed on any topic, anytime; PadhAI: Reading Assessment App, which uses advanced speech recognition to evaluate children’s reading fluency in Hindi and eight Indian languages; and a suite of LLM-powered chatbots designed for diverse learning contexts: from BaalSakhi for early childhood care, to a skilling interview bot for youth employment readiness, and OCR-based analysis tools that digitize and assess student work at scale. At Pratham International, we build for the long term. Whether it’s ensuring products are truly accessible across rural India and the global south through languages, building datasets to ensure context and accuracy for products, or gathering feedback from the field.   Backed by partners like Google.org, Anthropic, and the Gates Foundation, we are committed to building digital public goods that ensure every child is in school and learning well. Description of the Role Pratham International, through its wholly-owned subsidiary in India (PULS Pvt. Ltd.), is looking to hire an AI Engineer for the PraDigi Initiative. As an ASR Engineer, you will lead the development and deployment of speech recognition systems for automated reading assessment across multiple languages.  The engineer will work across the full ASR pipeline: from supervising data collection, annotation, data preparation and model training to decoding, alignment, error analysis, and deployment. This is a hands-on role at the intersection of speech recognition, multilingual NLP, and educational assessment. Key responsibilities include: Data Collection and Annotation Provide technical inputs to speech data collection design, including sampling across languages, dialects, age groups, reading levels, recording environments, devices, and target items to ensure the dataset is representative and useful for model training and evaluation. Define annotation guidelines and quality checks for transcripts, reading errors, disfluencies,  background speech and other phenomena relevant to reading assessment. Work with data and field teams to identify data gaps and annotation inconsistencies through model error analysis, and recommend targeted additional data collection or re-annotation where needed. Help design annotation schemas and inter-annotator quality processes so labels are consistent enough for model training, alignment, and evaluation. Model Training Train, fine-tune, and evaluate ASR models for children’s read speech across multiple languages and accents. Build pipelines for audio preprocessing, segmentation, normalization, transcription alignment, and model training. Adapt existing ASR architectures such as Whisper, wav2vec 2.0, Conformer, CTC/RNN-T, or similar models for reading assessment. Optimize models for practical deployment environments, including low-bandwidth, store-and-forward, server-based, and potentially offline/mobile inference. Adopting for Reading Assessment Develop approaches for accurately detecting substitutions, omissions, insertions, repetitions, partial-word attempts, hesitations and self-corrections. Implement forced alignment, phoneme-level alignment, or pronunciation-scoring approaches where required. Conduct systematic error analysis by language, reading level, age, accent, gender, recording conditions, and device. Collaborate with assessment experts and field teams to translate model outputs into measures such as reading accuracy, WCPM, fluency, and reading level. Benchmarking Define and track ASR metrics relevant to reading assessment, including WER/CER, word-level accuracy, error-detection precision/recall, alignment accuracy, and reading-score agreement with human evaluators. Build reproducible experimentation and model-evaluation pipelines and maintain clear technical documentation. Desired Experience and Qualification We are looking for an AI/ML Engineer with ASR expertise that can own the ASR based reading assessment pipeline across multiple languages A Degree in any Mathematics/Computer Science/Data Science or related STEM field 3-5 years of full-time experience; at least 2 working in an AI/ML role Strong experience in automatic speech recognition, speech processing, or spoken-language technologies. Strong Python skills and experience with PyTorch or similar deep-learning frameworks. Experience working with modern ASR architectures and toolkits such as Hugging Face Transformers, NeMo, Kaldi, or equivalent. Understanding of ASR concepts including: -acoustic modelling -CTC / transducer / encoder-decoder architectures -decoding and beam search -language models -forced alignment -phoneme representations -WER/CER and ASR error analysis Experience preparing and working with large-scale audio and transcription datasets. Ability to diagnose model errors systematically rather than relying only on aggregate transcription accuracy. Why join us? Impact at a Global Scale: Your work will directly support the learning journeys of millions of children. With programs active in 31 countries, you will help build Digital Public Goods that bridge the gap for the millions of children across the Global South who still struggle with foundational literacy. Unique Technical Challenges : We apply advanced machine learning where it is needed most. You will solve "last-mile" problems—such as training ASR models on children's voices and optimizing LLMs for offline, low-bandwidth environments—that are rarely addressed in the commercial sector. A Culture of Growth and Evidence : At Pratham, we value rigorous testing and continuous learning. You will have the autonomy to experiment with the latest SOTA models, supported by a 5-year strategic roadmap and funding from partners like the Gates Foundation and Anthropic. High-Calibre, Mission-Driven Team : You will work alongside a global team of experts from institutions like MIT, Harvard, and INSEAD. Our alumni have gone on to lead successful startups and international policy shifts, all while remaining dedicated to the goal of "Every Child in School and Learning Well." Inclusive and Collaborative Environment : Join a diverse team that spans Asia, Africa, and Latin America. We value innovative thinking and local expertise, ensuring that the technology we build is deeply rooted in the realities of the communities we serve. Employment Details Contract Type : Full-Time Employment Date of Joining : Immediate Work from Anywhere in India : We offer a flexible, remote-first work environment for candidates based anywhere in India. While you have the autonomy to work from where you are most productive, you will have the unique opportunity to travel to our field locations periodically. These visits are essential to see your models in the hands of learners and to gather the real-world feedback that drives our innovation. Competitive Remuneration : Salary is benchmarked against industry standards for AI roles and is commensurate with experience. As a global organization, we are committed to providing a package that recognizes the high-market value of your technical expertise alongside the deep social impact of your work. To apply Fill out the form attached Shortlisted candidates will be contacted for further rounds Please refer to this Guidance on Candidates AI Usage by Anthropic to understand how to present yourself to Pratham International effectively Pratham International is an Equal Opportunity Employer We are dedicated to creating a diverse and inclusive workplace. We welcome applications from all qualified individuals, regardless of race, gender, disability, or any other legally protected status.

Location: 🇧🇷 São Paulo / 🌎Remote Workplace: Remote A little bit about us: We are Belvo, an open finance API platform with the bold vision of democratizing access to financial services in Latin America. We enable any financial innovator to access and interpret financial data, as well as initiate payments from their end-users accounts. We’re turning the messy complexities of the Latin American financial ecosystem into a modern set of tools to access and interpret data and move money in a seamless and secure way. We’re a highly-technical, passionate, and driven team. We are more than 90 people and our team currently represents 20 nationalities. We have offices in São Paulo and Mexico City – while a large portion of us work remotely. We are tackling a very stimulating problem: connecting fintech innovators with legacy financial infrastructure. We strive to go beyond the limits of what is possible today and to do so in an elegant and developer-first way. Since starting our adventure in May 2019, we have raised $71m from the leading VC investors globally. You can read more about our company here https://belvo.com/about/ and about our team and culture here https://belvo.com/careers/. Also, head to our blog https://belvo.com/blog/ for more news about what we’re building and how we work https://belvo.com/blog/category/working-belvo/. About the team: - We work in cross-functional, autonomous teams. We follow continuous delivery best practices executed on top of a modern technology stack.  - Our products are built for developers, by developers. Technological excellence is at the heart of what we do.   - We are pragmatic and customer-focused. We strive to find the right set of trade-offs in order to validate our hypothesis as early as possible, iterating on our products based on customer feedback.  - We communicate transparently. We do weekly all-hands where we get together to discuss company performance and goals. - While we are global and remote-friendly, we also operate from our vibrant offices in CDMX and Sao Paulo. To accommodate the various time zones in which we are based, we ensure we’re always synced up for at least 3 hours per day. - Also, we are backed by some of the leading investors in Silicon Valley and Latin America, including Founders Fund, Kaszek Ventures, and YCombinator. The challenge The Intelligence Squad is Belvo's AI enabler team. One part of the squad builds the platforms and patterns that let every team at Belvo ship AI. This role sits in the other part: turning the financial data and payments we aggregate across Latin America into models that make real decisions, who looks creditworthy, which transaction is fraudulent, what category a payment belongs to, whether an income statement checks out. That data drives real financial outcomes for the people and businesses on the other end, so the bar for rigor is high. You'll be the data science reference in the squad. We're not hiring someone to execute a modeling backlog someone else wrote. We want someone who decides which problems are worth modeling, defines how we know a model is good enough, and raises the data science bar for everyone they work with. You'll be deep enough in statistics and ML to defend a modeling choice to anyone, whether this is a set of rules to simplify a problem or a complex model that you build. You'll be close enough to the business to turn a commercial problem into a modeling roadmap and a business case. And you'll be a strong enough engineer to take your models from notebook to production and own them there. Responsibilities - Model development and selection: Lead the design of statistical and ML models (classification, regression, ranking, NLP or LLM-based) for problems like risk scoring, fraud detection and transaction categorization. Choose approaches on their merits and defend those choices with data, not habit. - AI-leveraged delivery: Use agentic coding and research tooling as a core part of how you work: exploring data, generating and testing feature hypotheses, scaffolding pipelines, writing the service around the model. We care about the decisions you ship, not hours at the keyboard, and we expect you to keep compressing the gap between the two. - Feature engineering with business context: Work with Product and the business teams to figure out which signals in Belvo's financial data actually explain the outcome you're modeling, and translate an ambiguous business question into a well-scoped modeling problem. - Productionization and ownership: Take models past the notebook. Build or shape the pipelines, APIs and services that run them reliably in production, and own them once they're live: monitoring, drift, retraining, and retirement when the evidence says so. - Experimentation: Design and run experiments (A/B tests and offline evaluation) to confirm that a model change actually moves the metric it's meant to move. - Cross-functional partnership: Work closely with Engineering, Data Research, Product, and the rest of the Intelligence Squad to ship end to end, and with stakeholders across the business to keep your models grounded in what actually matters to them. - Business and customer partnership: Work directly with stakeholders and, when needed, customers. Translate ambiguous business questions into well-scoped problems, and explain trade-offs and impact clearly to non-technical audiences, executives included. - Staying current: GenAI moves weekly. We expect you to be genuinely plugged in: model releases, papers, and tooling, to hold an opinion on what's real versus hype, and to bring the useful parts back to the squad as a demo, a doc, or a session for our company-wide AI Chapter. - Framing the problem: Decide what's worth modeling. Push back when a rule, a heuristic, or a better data pipeline solves the problem without a model, and be willing to kill or retire your own model when the evidence says so. This position may be for you if - 5+ years of experience as a Data Scientist, Applied Scientist, or in a similar hands-on modeling role. - You've owned models end to end in production, from framing the problem to monitoring it live, and you can point to the measurable business impact they had. - You have a solid grounding in statistics and can explain how your models work at that level, not just how to call them. - You've built and compared more than one type of model (for example tree-based methods, regression, deep learning) for the same problem and can justify why you picked the one you shipped. - You're comfortable in Python for data science (pandas, scikit-learn, and at least one of TensorFlow or PyTorch) and in SQL for pulling and shaping your own data. - You've taken several models you built into production yourself, not just handed it off to someone else to deploy.You http://deploy.You understand engineering fundamentals: testing, version control, CI/CD, and how to write code someone other than you can maintain. - You've built with LLMs past the API-call level: tool use or agents, retrieval, structured outputs, and shipped at least one to production. You can explain how you knew it was good enough, which means you've built some form of evaluation, not just eyeballed outputs. - Agentic coding tools are part of your daily workflow, not a novelty. You can walk us through a specific task that used to take you days and now takes hours: how you set it up, what you delegated, and where you still don't trust the output. - You're proficient in written and spoken English. Amazing if - You've worked in fintech, financial risk, or another regulated data environment before. - You have a business or strategy background alongside data science (an MBA, consulting, or a product/strategy role). - You've worked in a customer-facing data science role, such as a forward-deployed engineer or a solutions/applied team at a data platform company. - You've worked with MLOps tooling for model versioning, monitoring, and automated retraining. - You've deployed models on AWS (for example SageMaker or an equivalent managed ML service). - You've written evals or an LLM-as-judge harness for a non-deterministic system, and have opinions about where that approach breaks. - You've used LLMs inside the data pipeline itself: labeling, extraction, categorization, and can say where they beat a classical model and where they quietly don't. Tech stack - Python is our primary language for data science and modeling (pandas, scikit-learn, and deep learning frameworks as needed). - SQL against a cloud data warehouse for data access and feature work.Claude deployed company-wide and Claude Code for every engineer. Agentic tooling is part of how we work daily, not a side experiment. - Cloud infrastructure on AWS, favoring managed services so you can focus on the modeling problem rather than infrastructure plumbing. - An experimentation platform for A/B testing and offline evaluation. - We use Datadog for service observability across the company. - We follow Continuous Integration and Continuous Delivery best practices. Our tech stack: - We’re building our platform using modern technologies, putting the focus on reliability and long-term maintainability - We primarily use Python on the backend. We use battle-tested technologies such as Django, and we are heavy users of Python’s asyncio for some parts of our stack - We use Javascript, Vue.js and Sass on the frontend. We are developing and leveraging our own design system and component library - We run our infrastructure on top of Amazon Web Services, leveraging managed services. We tend to favor the use of managed services wherever possible, in order to focus on our business problems - We observe and monitor our services using Datadog - We follow Continuous Integration and Continuous Delivery best practices Our process steps: At Belvo every hire is so important to us, that we share the decision to hire as a team. - People team chat - Take-home challenge - Challenge presentation - Team fit chat - Meet the founders Our perks: 🚀 Stock options (we are all owners and this is very important to us) 📈 Annual company bonus linked to company performance 🔆 Flexible working hours 🔝 Remote friendly 🐶 Pet friendly 👩🏻‍⚕️ Health Insurance 🎊 Paid time off on your birthday 💻 Renew your laptop every 3 years 🙋 Training Budget 😎 Team building events 🙀 Bank holidays swap within 30 days 🔋 Fitness/ wellness stipends 🚀 Yearly company offsite 🍒 Fresh fruit every week, all-you-can-drink tea and coffee 🏝 Extra days off accrued when completing 2, 3, 4 years and beyond at Belvo 🎳 Yearly department offsite

Задачи: — проектировать, запускать и поддерживать процессы сбора, обработки, разметки и валидации данных; — формулировать требования к датасетам совместно с командами Computer Vision, NLP и AI; — определять критерии качества данных, организовывать проверки разметки и анализировать ошибки; — оценивать скорость, стоимость и качество разметки; — находить узкие места и улучшать процесс; — проводить эксперименты с подходами к разметке, автоматизации и контролю качества, анализировать результаты и принимать решения на их основе; — использовать LLM и другие инструменты автоматизации для ускорения подготовки и проверки данных; — работать с асессорами и заказчиками: объяснять требования, собирать обратную связь, устранять проблемы в процессе; — развивать типовые пайплайны и масштабировать успешные решения на новые задачи; — поддерживать датасеты после запуска: расширять их при дрейфе данных, появлении новых сценариев и изменении требований к качеству модели; — писать понятные инструкции для асессоров, правила разметки и промпты для LLM. Требования: — работал (а) с краудсорсинговыми проектами, разметкой данных или контролем качества разметки; — умеешь декомпозировать неопределённую задачу на измеримый процесс: определить входные данные, правила, критерии приёмки и метрики качества; — умеешь оценивать и сопоставлять качество, скорость и стоимость разных подходов к разметке; — владеешь Python на уровне, достаточном для анализа данных, подготовки выборок и написания скриптов для автоматизации; — умеешь формулировать и тестировать промпты для LLM, оценивать качество результатов и учитывать ограничения такого подхода; — можешь вести несколько задач одновременно, расставлять приоритеты и прозрачно управлять ожиданиями; — умеешь вовлекать нужных участников в решение задачи и объяснять сложные вещи понятным языком. Будет плюсом, если ты: — знаком (а) с основными принципами машинного обучения и жизненным циклом ML-моделей; — работал (а) с датасетами для задач Computer Vision, NLP или генеративного AI; — знаешь HTML, CSS и JavaScript — это поможет при работе с интерфейсами и инструментами разметки; — имеешь опыт разработки или доработки внутренних инструментов для разметки, валидации либо контроля качества данных; — работал (а) с метриками качества разметки, inter-annotator agreement, аудитами ошибок или активным обучением. Условия: — полный рабочий день; — можно удалённо.

Location: Москва В команду Сервисов Мониторинга корпоративных клиентов открыта вакансия Team Lead. В команде вам предстоит работать над разработкой и внедрением AI-агентов в системы мониторинга. Основное направление команды – это ранняя диагностики проблемности корпоративных клиентов и автоматизация десятков бизнес-процессов мониторинга. Мы работаем на фронтире технологий и делаем решения без участия человека. Обязанности * менторство и развитие исследователей данных, мл-инженеров и разработчиков команды * разбор инцидентов, ответственность за бизнес-эффект продукта, расчет финансового эффекта FinAI и защита в департаменте финансов * проведение валидации моделей * построение сквозного мониторинга за работой агентов и моделей в 3 продуктах * выстраивание отношений с новыми заказчиками продуктов, системный дизайн и оценка тредозатрат при подключение новых пользователей. Требования * опыт работы в аналогичной должности от 3-х лет * понимание области NLP (SOTA архитектур моделей, LLM, фреймворки для разработки агентов) * стек: Python/PostgreSql/Spark * понимание основных принципов и алгоритмов классического ML * опыт перевода бизнес-задач в термины ML * навыки написания продакшн-кода на Python * высокая автономность в рабочих задачах. Будет плюсом: * опыт создания AI-агентов и использования их в работе будет преимуществом. Условия * работа в удобном и современном офисе недалеко от станции метро Кутузовская * ежегодный пересмотр зарплаты, 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: — проектировать и реализовывать end-to-end LLM-пайплайны, включая продвинутые RAG-системы (индексация, ретривал, ранжирование, генерация) — разрабатывать автономных AI-агентов с использованием планирования, вызова инструментов (function calling) и мультиагентной оркестрации — обучать и дообучать NLP-модели (classification, embeddings) и современные LLM под бизнес-задачи (SFT, LoRA/QLoRA) — строить системы оценки качества генерации, внедряя продуктовые и ML-метрики (RAGAS, DeepEval, кастомные LLM as a judge) — переводить бизнес-задачи в ML-постановку и реализовывать решения полного цикла от гипотезы до интеграции в продакшен. Требования: — опыт работы в Deep Learning от 3 лет, включая обучение моделей на PyTorch — продвинутое понимание архитектуры современных LLM, включая механизмы attention, MoE, принципы инференса (KV-cache, batching) и методы alignment — практический опыт построения AI-агентов и LLM-пайплайнов с использованием LangChain, LangGraph или аналогичных фреймворков — уверенная база в классическом ML и NLP, знание статистики, алгоритмов поиска и векторизации (от TF-IDF до эмбеддингов) — опыт работы с фреймворками для оценки и мониторинга LLM-решений, такими как Arize, LangSmith или Weights & Biases — владение техниками промпт-инжиниринга и опыт их применения на практике для оптимизации работы моделей — уверенное владение Git и Linux-окружением. Будет плюсом: — базовое понимание принципов проектирования архитектуры микросервисов — опыт опромышливания AI-агентов и знание принципов LLMOps, включая CI/CD, оркестрацию и управление версиями промптов — глубокое понимание архитектуры масштабируемых RAG-систем и опыт работы с векторными базами данных — опыт разработки асинхронных сервисов (asyncio) и интеграции ML-моделей в высоконагруженные системы. 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 для профессионального и карьерного развития — программа адаптации и помощь руководителя на старте — 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.

Location: California Workplace: onsite Role Overview   We are looking for a Hardware Lab Technician to own the physical side of a large-scale robot data collection operation. You’ll keep the fleet running, build what’s next, and be the person the lab calls when something stops working. This is a hands-on role across research and low-volume production where embodiments, grippers, and camera rigs change fast—and the hardware has to keep up. Day-to-day teleoperation is handled by a dedicated operator team you support; you maintain what they run on and help ensure data quality through reliable, well-calibrated hardware.   Project Details Location: 100% Onsite (Santa Clara, CA, US) Employment: Full-time W-2 Shifts: Monday–Friday, 7AM - 3:30PM or 9AM - 5:30PM  Start Date: ASAP Contract Duration: 12-month contract with possibility of extension Rate: $40 per /hour What You’ll Do Build, wire, and bring up data collection stations — robot arms, multi-camera rigs, workstations, teleoperation hardware, and cable management that survives daily operator use. Keep the fleet running: daily checks, preventive maintenance, calibration routines, and diagnosing faults on arms, hands, and sensors. Swap and integrate end-effectors and grippers, dexterous hands, force/torque and tactile sensors, and RGB-D cameras. Run calibration and firmware procedures across the fleet, and catch drift before it reaches the data. Assemble and iterate prototype hardware coming out of our mechanical and electrical engineers — printed parts, off-the-shelf components, vendor-fabricated parts, and custom harnesses. Soldering, crimping, connectorization, harness building, and PCB rework support at the bench. Own the lab's physical state: spares and consumables inventory, parts kitting, bench and tool organization, and shipping, returns, and RMAs with vendors. Support teleoperators on hardware issues during live collection — triage fast, fix or escalate, and get the station back up. Document what you do. Build sheets, maintenance logs, calibration records, and hardware SOPs the next technician can follow. Help bring up new embodiments and new stations alongside engineers and researchers. What we need to see 3+ years hands-on technician experience in a lab, prototyping, or low-volume build environment — robotics, medical devices, aerospace, semiconductor equipment, or similar. AS in a technical field, a relevant trade or technical certificate, or equivalent hands-on experience. Mechanical assembly fundamentals: fasteners and torque, bearings, belts, linkages, and the ability to read CAD and mechanical drawings (Onshape, SolidWorks, or similar). Electrical hands: soldering including basic SMD rework, crimping and connectorization, harness building, and confident use of a multimeter and oscilloscope. Comfortable in Linux at the command line — SSH into a workstation, run a calibration script, read a log, and tell us what it said. Experience with motors, encoders, and sensor integration, and an understanding of what a bad ground or a marginal cable does to a data stream. Methodical and self-documenting. An undocumented fix is a future outage. Comfortable with ambiguity and frequent change, and able to prioritize when more than one lab needs you at the same time. Able to work on-site in Santa Clara, with flexibility on shift coverage as the labs run extended hours. Ways to stand out from the crowd Humanoid or bimanual platforms (Unitree, Fourier, ALOHA/ARX-class rigs) and dexterous hands. Maintenance and calibration on collaborative or industrial arms — UR, Franka, xArm, ARX, or equivalent. Multi-camera rig build and calibration; RealSense, ZED, or Orbbec-class depth cameras. Running and maintaining FDM or SLA printers, and designing your own simple fixtures and adapters. Light scripting in Python or bash to automate a repetitive bench or calibration task. Experience in a high-throughput lab or production line where uptime or yield was a number someone tracked. Vendor-facing experience: RMAs, lead times, and spares planning. Job reference: #L1-CC1 Please note that in order to verify work authorization as is required by Federal law (I-9 process), all new employees must complete a live video verification with their selected IDs and provide photos of these selected IDs within their first 3 days of employment.   To know more details (Click here)   In compliance with federal law, all persons hired will be required to verify identity and eligibility to work in the United States and to complete the required employment eligibility verification form upon hire.  In addition, we employ anti-fraud checks to ensure all candidates meet the requirements of the program.     As a trusted global transformation partner, Welocalize accelerates the global business journey by enabling brands and companies to reach, engage, and grow international audiences. Welocalize delivers multilingual content transformation services in translation, localization, and adaptation for over 250 languages with a growing network of over 400,000 in-country linguistic resources. Driving innovation in language services, Welocalize delivers high-quality training data transformation solutions for NLP-enabled machine learning by blending technology and human intelligence to collect, annotate, and evaluate all content types. Our team works across locations in North America, Europe, and Asia serving our global clients in the markets that matter to them. www.welocalize.com   To perform this job successfully, an individual must be able to perform each essential duty satisfactorily. The requirements listed below are representative of the knowledge, skill, and/or ability required. Reasonable accommodations may be made to enable individuals with disabilities to perform essential functions.

Location: Athens, Greece; Thessaloniki, Greece About the Role We are seeking an AI/ML Engineer - python developer to join our software development team. You will work on challenging international projects, using cutting-edge technology to deliver ML and AI solutions for major international public organizations. The role offers rapid career growth and real exposure to high-impact, large-scale work. What You Will Do Implement advanced ML solutions and model deployment strategies (MLOps); Build advanced Generative AI components and pipelines, including RAG architectures, orchestration layers and evaluation frameworks; Contribute to AI governance, observability, monitoring and responsible AI practices; Contribute to the design and documentation AI solutions; Contribute to python implementation tasks and implementation of micro-services using frameworks such as FastAPI, Flask, Pydantic. etc. Requirements A university degree in IT or a related field; Very good knowledge of python, pandas, numpy, scipy, matplotlib, seaborn; Good knowledge of  FastAPI, Flask, Pydantic, SQL Alchemy; Hands-on experience in use and integration with leading LLMs and SLMs; Familiarity with AI frameworks such as LangChain, LangGraph, Haystack; Familiarity with major ML frameworks such as PyTorch and TensorFlow, and with model formats such as ONNX; Excellent command of English, written and spoken. Nice to Have Experience designing and implementing AI/NLP pipelines (AI Ops / NLP Ops); Familiarity with AWS SageMaker and / or  Azure Machine Learning platform; Familiarity with cloud platforms such as AWS or Azure; Familiarity with Big Data systems such as Spark (PySpark) and/or Databricks. Benefits We believe in rewarding talent and dedication. Here's what you can expect as part of our team: Competitive full-time salary; Private Health Coverage on the Company’s group program; Flexible Working Hours; Top-of-the-Line Tools; Professional Development: Benefit from language courses, specialized training, and continuous learning opportunities; Career Growth: Work with some of the most innovative and exciting specialists in the industry; Dynamic Work Environment: Thrive in a setting that offers challenging goals, autonomy, and mentoring, fostering both personal and company growth. If you want an exciting challenge, work with some of the coolest technologies, and enjoy your time doing it, then join us! Submit your detailed CV in English, quoting reference: ( JA/ME-PD/11/26 ). You may also consider all our other open vacancies by visiting the career section of our website ( www.eurodyn.com ) and follow us on Twitter (@EURODYN_Careers) and LinkedIn. EUROPEAN DYNAMICS (ED) ( www.eurodyn.com ) is a leading European Software, Information, and Communication Technologies company, operating internationally (Athens, Brussels, Luxembourg, Copenhagen, Berlin, Stockholm, London, Nicosia, Valetta, Vienna, Den Haag, Hong Kong, etc.) The company employs over 1100 engineers, IT experts, and consultants (around 3% PhD, 41% MSc, and 54% BSc). We design and develop software applications using integrated, state-of-the-art technology. Our current IT projects have a value exceeding 300 million EURO. EUROPEAN DYNAMICS is a renowned supplier of IT services to European Union Institutions, international organizations, European Agencies, and national government Administrations in 40 countries and 4 continents. As part of our dedication to the diversity of our workforce, we are committed to Equal Employment Opportunity without regard for race, colour, national origin, ethnicity, gender, disability, sexual orientation, gender identity, or religion. EUROPEAN DYNAMICS (ED) adheres to the General Data Protection Regulation principles by applying its Privacy Policy as published at www.eurodyn.com/privacy . By submitting an application to this position and by sharing your personal data with ED, you acknowledge and accept its Policy and authorize ED to process your personal data for the purposes of the company's recruitment opportunities, in line with the Policy.

Location: Thuwal, Saudi Arabia This position is for building, deploying and evaluating Client's Arabic AI tutor for mathematics and computer science: preparing Arabic training data, fine-tuning, aligning and evaluating LLMs on Shaheen III and Ibex, and integrating the tutor into the learning platform for a pilot in at least 10 Saudi schools. The position is part of the RDIA-funded project 5870, “An AI-Powered Arabic Tutoring Platform for STEM Education in the Kingdom.” Two engineers will be hired into this role. Key Responsibilities Prepare Arabic training datasets from educational materials and consented platform interactions. Fine-tune, align, and evaluate commercial and open-source LLMs using Shaheen III and Ibex computing platforms. Develop Arabic STEM benchmarks, preference-training methods, and student-safety guardrails. Integrate AI tutoring capabilities into the learning platform, including Arabic right-to-left interfaces. Deploy and optimize models for reliable, low-latency real-time interactions. Support school pilot implementation, evaluation, and teacher feedback collection. Ensure student data privacy and compliance with Saudi data protection requirements. Contribute to research publications, technical reports, and project deliverables. Qualifications & Experience MSc in Computer Science, Artificial Intelligence, or a related field; alternatively, a BSc with at least one year of relevant experience. Hands-on experience in LLM fine-tuning, evaluation, and AI application deployment. Professional proficiency in Arabic and English. Experience with RLHF/DPO, educational technology, or mathematics/programming education is preferred. Technical Skills Advanced Python and PyTorch. Hugging Face Transformers, PEFT, and TRL. Distributed GPU training using DeepSpeed or FSDP. LLM evaluation, reward modeling, and preference optimization. Arabic NLP, data pipelines, and OCR. Model serving and inference optimization using tools such as vLLM. APIs, Docker, Git, CI/CD, and production deployment.

Location: Sydney ABOUT MISTRAL Mistral provides full-stack AI solutions: from frontier models to developer tools, applications, and compute. We partner with enterprises tackling the hardest problems across high-stakes industries like finance, manufacturing, defense, healthcare, and the public sector, co-creating customized AI systems that they can run on their terms. We are a dynamic, collaborative team passionate about AI and its potential to transform society. Our diverse workforce thrives in competitive environments and is committed to driving innovation. Our teams are distributed between Europe, North America, Asia and the Middle East. We are creative, low-ego and team-spirited. About The Job: Technical Lead, Applied AI   Mistral AI is seeking a Technical Lead, Applied AI to drive the technical strategy, execution, and delivery of complex AI solutions for our enterprise customers. In this role, you will lead a project teams of Applied AI Engineers, ensuring the successful deployment of Mistral AI products and the development of high-impact, scalable AI use cases.   You will act as the primary technical point of contact for our most strategic customers, guiding them through the entire lifecycle—from pre-sales to post-implementation—while collaborating closely with research, product, and engineering teams to shape the future of our offerings.   As a Technical Lead, you will bridge the gap between cutting-edge AI research and real-world enterprise applications, ensuring our solutions are robust, scalable, and aligned with both customer needs and Mistral’s technological vision.   What you will do   - Deliver as an IC the critical lines of codes of our complex projects, you’ll be hands-on and de-risk the critical parts of our complex projects. You’ll stay deeply involved in coding, reviewing, and optimizing AI solutions. - Lead technical teams of Applied AI Engineers, providing mentorship, technical guidance, and best practices for deploying state-of-the-art GenAI applications across industries. - Lead technical discussions during pre-sales, translating customer requirements into actionable solutions and communicating Mistral’s technological advantages to diverse stakeholders. - Design and oversee the implementation of complex AI systems, including fine-tuning, RAG, agentic workflows, and custom LLM applications, ensuring alignment with Mistral’s product roadmap and open-source initiatives. - Drive innovation by identifying emerging trends in AI, evaluating new tools and methodologies, and championing best practices for fine-tuning, inference, and deployment. - Work closely with product managers, researchers, and engineers to ensure seamless integration of customer feedback into Mistral’s product development cycle.   About you - You hold a PhD or Master’s degree in AI, Machine Learning, Computer Science, or a related field. - You have 7/8+ years of experience in AI/ML, with at least 2+ years in a technical leadership role (e.g., Tech Lead, Engineering Manager, or Solutions Architect) focused on AI products or enterprise solutions. - You have a proven track record of leading teams to deliver complex AI projects, from prototyping to production, in industries such as tech, finance, healthcare, or industrial automation. - You possess deep expertise in fine-tuning LLMs, advanced RAG, agentic systems, and deploying NLP applications at scale. - You are proficient in Python, PyTorch, and modern AI frameworks (e.g., LangChain, Hugging Face). Experience with cloud platforms (AWS, GCP, Azure) and MLOps tools is a plus. - You have strong software engineering skills, including API design, backend/full-stack development, and system architecture. - You excel in technical communication, with the ability to articulate complex concepts to both technical and non-technical audiences, including executives and engineers. - You thrive in fast-paced, collaborative environments and are passionate about mentoring and growing technical talent.   Ideally, you have: - Contributed to open-source projects, particularly in the LLM or AI space. - Experience in customer-facing roles (e.g., Solutions Architect, Customer Engineer, or Technical Product Manager) with a focus on enterprise AI adoption. - A track record of driving technical strategy and influencing product direction based on customer needs and market opportunities.   Why join us? You’ll have the opportunity to shape the future of AI adoption in enterprises, work with a world-class team, and contribute to open-source projects that impact millions. If you’re excited about leading technical innovation and solving real-world challenges with AI, we’d love to hear from you!   WHAT WE OFFER We offer a comprehensive benefits package designed to support your well-being, growth, and work-life balance. Benefits vary by country and may include healthcare coverage, parental leave, retirement plans, relocation support, wellness programs, meal and transportation allowances, and other location-specific perks. For the most up-to-date details on benefits available in your location, please refer to our Benefits page https://app.notion.com/p/mistralai/Benefits-at-Mistral-36e6ba59a7fe836b93dd01737fcc27ef?source=copy_link. PRIVACY POLICY Your privacy matters to us. You can learn more about how we handle your personal data in our Applicant Privacy Policy https://legal.mistral.ai/terms/applicant-privacy-policy.

Требования к специалисту: — Уверенный опыт работы с LLM/SLM (Few-shot learning, prompt engineering) и BERT (классификация сценариев и интентов). — Практический опыт finetuning LLM (PEFT, LoRA/QLoRA) под узкие прикладные задачи. — Уверенный дебаг LLM: умение быстро понять, почему модель ответила не так, и исправить это промптом, гиперпараметром или файнтюном. — Опыт дообучения и оптимизации TTS-моделей. — Опыт работы и интеграции современных ASR-систем (в частности, GigaAMv2). — Опыт работы как с графовыми/сценарийными диалоговыми движками, так и с LLM-агентами. — Высокая скорость диагностики проблем в инференсе, локализации багов и принятия инженерных решений. — Уверенное владение Python (PyTorch, huggingface, фреймворки инференса) и Go. — Опыт работы с оптимизацией инференса под low-latency (vLLM, TensorRT-LLM, Triton Inference Server, ONNX Runtime). — Практика работы со потоковым аудио в реальном времени (streaming ASR/TTS, gRPC-контракты). — GoLang: базовое понимание дизайна сервисов (SOLID, DRY, KISS); — типизация (строки, числа, слайсы и мапы); — примитивы синхронизации и конкуренция (горутины, каналы); — указатели и работа с ними; — observability. — Будет плюсом: • опыт работы с gRPC. • умение настроить инфраструктуру (k8s/inventory). • знания работы TCP. Задачи: — Поэтапный перевод системы с графа сценариев и скиллов на управляемый LLM-based диалог. — Дебаг голосового пайплайна (поиск причин high latency, галлюцинаций LLM, проблем с передачей аудио, графовых ошибок и т.п.). — Оперативное принятие архитектурных решений и их самостоятельная реализация в коде. — Работа с диалоговым стеком и NLP: поддержка и быстрая отладка few-shot классификации сценариев на базе Qwen и классификаторов на BERT. — Учёт краевых случаев, исправление ошибочных срабатываний интентов. — При необходимости — finetuning локальных LLM (извлечение сущностей, удерживание инструкций). — Работа с речевым стеком: интеграция и поддержка ASR на базе GigaAMv2 (отладка качества распознавания, работы VAD и передачи текста дальше по пайплайну). — Дообучение и настройка инференса собственных и опенсорсных TTS и систем клонирования голоса для достижения нужного качества звучания и скорости работы.

Location: California; Sunnyvale, CA; San Francisco, CA; Seattle, WA; Redmond, Washington; Burlingame, CA; New York City Metropolitan Area; Los Angeles, CA; Washington, D.C.; Boston, MA Workplace: onsite Overview Welo Data is looking for detail-oriented and reliable individuals to join our team as Data Labeling Analysts, supporting speech and voice AI systems. This is a high-impact production role focused on building the datasets that power real-world AI systems. You’ll be working with audio, speech, and language data — helping ensure models are trained on accurate, well-structured, and representative inputs. While this role is more execution-focused than evaluation-heavy roles, it still requires strong judgment, attention to detail, and consistency. The work sits at the intersection of language, data, and AI systems — where precision and discipline matter at scale. We’re looking for people who are dependable, focused, and take pride in producing high-quality work, even across repetitive workflows.  Project Details Job Title: Data Labeling Analyst (Speech & Voice AI)  Hiring in: Onsite (Bay Area, Seattle, NYC, or client-dependent)  Hours: Full-time, 40 hours per week Employment Type: W2 Full-Time Employee Work Authorization: Must be authorized to work in the U.S. (no visa sponsorship) Hourly Pay Rate: $28/hour Important: This is a 100% onsite position — remote work is not available for this role. To be considered, candidates must be located in or able to commute to one of the following cities: NYC, Seattle, Bellevue, Redmond, San Francisco, Sunnyvale, Burlingame, Los Angeles, Washington DC, Boston. Please only apply if you meet this location requirement. What You’ll Do Execute high-volume data labeling and annotation tasks across speech and voice datasets Follow detailed guidelines to ensure consistency, accuracy, and data integrity at scale Work with audio and language data, including transcription, categorization, and tagging Maintain strong throughput while meeting quality expectations Escalate unclear or ambiguous cases appropriately Adapt to evolving guidelines and workflows as systems and requirements change Support baseline data production needs for AI training pipelines Contribute to team calibrations and quality alignment sessions What We’re Looking For Native-level fluency in Spanish (Mexico) Strong written communication skills and language fundamentals 1 year of work experience in data labeling, annotation, or content-focused work; or a Bachelor's degree or equivalent academic qualification in a related field. Ability to follow detailed instructions and apply guidelines consistently High attention to detail and ability to maintain accuracy in repetitive tasks Comfort working in structured, process-driven environments Ability to manage time effectively and maintain steady output Willingness to ask questions and escalate when needed Basic familiarity with AI, speech technology, or language data is a plus Benefits Paid Vacation: 6 days Paid Company Holidays: 2 days (Memorial Day and Labor Day) Paid Sick Leave: accrued per applicable state law and company policy Medical, Dental, and Vision Insurance (eligibility applies) Health Savings Account (HSA) 401(k) Retirement Plan Employee Assistance Program Additional voluntary benefits (life, accident, critical illness, etc.) Onsite Perks (where applicable): Free breakfast, lunch, and dinner Stocked micro-kitchens with snacks and beverages Commuter benefits, including shuttles and bike-to-work options Unique campus features depending on location Please note that in order to verify work authorization as is required by Federal law (I-9 process), all new employees must complete a live video verification with their selected IDs and provide photos of these selected IDs within their first 3 days of employment.   To know more details (Click here)   In compliance with federal law, all persons hired will be required to verify identity and eligibility to work in the United States and to complete the required employment eligibility verification form upon hire.  In addition, we employ anti-fraud checks to ensure all candidates meet the requirements of the program.     As a trusted global transformation partner, Welocalize accelerates the global business journey by enabling brands and companies to reach, engage, and grow international audiences. Welocalize delivers multilingual content transformation services in translation, localization, and adaptation for over 250 languages with a growing network of over 400,000 in-country linguistic resources. Driving innovation in language services, Welocalize delivers high-quality training data transformation solutions for NLP-enabled machine learning by blending technology and human intelligence to collect, annotate, and evaluate all content types. Our team works across locations in North America, Europe, and Asia serving our global clients in the markets that matter to them. www.welocalize.com   To perform this job successfully, an individual must be able to perform each essential duty satisfactorily. The requirements listed below are representative of the knowledge, skill, and/or ability required. Reasonable accommodations may be made to enable individuals with disabilities to perform essential functions. Why This Role This role is a critical part of how modern AI systems are built. The data you produce directly impacts how speech and voice models understand and interact with real users. It’s a great entry point into AI operations, offering exposure to large-scale systems and the opportunity to build foundational experience in data, language, and AI workflows.

Location: California; Sunnyvale, CA; San Francisco, CA; Seattle, WA; Redmond, Washington; Burlingame, CA; New York City Metropolitan Area; Los Angeles, CA; Washington, D.C.; Boston, MA Workplace: onsite Overview Welo Data is looking for detail-oriented and reliable individuals to join our team as Data Labeling Analysts, supporting speech and voice AI systems. This is a high-impact production role focused on building the datasets that power real-world AI systems. You’ll be working with audio, speech, and language data — helping ensure models are trained on accurate, well-structured, and representative inputs. While this role is more execution-focused than evaluation-heavy roles, it still requires strong judgment, attention to detail, and consistency. The work sits at the intersection of language, data, and AI systems — where precision and discipline matter at scale. We’re looking for people who are dependable, focused, and take pride in producing high-quality work, even across repetitive workflows.  Project Details Job Title: Data Labeling Analyst (Speech & Voice AI)  Hiring in: Onsite (Bay Area, NYC, or client-dependent)  Hours: Full-time, 40 hours per week Employment Type: W2 Full-Time Employee Work Authorization: Must be authorized to work in the U.S. (no visa sponsorship) Hourly Pay Rate: $28/hour Important: This is a 100% onsite position — remote work is not available for this role. To be considered, candidates must be located in or able to commute to one of the following cities: NYC, Bellevue, Redmond, Seattle, San Francisco, Sunnyvale, Burlingame, Los Angeles, Washington DC, Boston. Please only apply if you meet this location requirement. What You’ll Do Execute high-volume data labeling and annotation tasks across speech and voice datasets Follow detailed guidelines to ensure consistency, accuracy, and data integrity at scale Work with audio and language data, including transcription, categorization, and tagging Maintain strong throughput while meeting quality expectations Escalate unclear or ambiguous cases appropriately Adapt to evolving guidelines and workflows as systems and requirements change Support baseline data production needs for AI training pipelines Contribute to team calibrations and quality alignment sessions What We’re Looking For Native-level fluency in Spanish (Spain) Strong written communication skills and language fundamentals 1 year of work experience in data labeling, annotation, or content-focused work; or a Bachelor's degree or equivalent academic qualification in a related field. Ability to follow detailed instructions and apply guidelines consistently High attention to detail and ability to maintain accuracy in repetitive tasks Comfort working in structured, process-driven environments Ability to manage time effectively and maintain steady output Willingness to ask questions and escalate when needed Basic familiarity with AI, speech technology, or language data is a plus Benefits Paid Vacation: 6 days Paid Company Holidays: 2 days (Memorial Day and Labor Day) Paid Sick Leave: accrued per applicable state law and company policy Medical, Dental, and Vision Insurance (eligibility applies) Health Savings Account (HSA) 401(k) Retirement Plan Employee Assistance Program Additional voluntary benefits (life, accident, critical illness, etc.) Onsite Perks (where applicable): Free breakfast, lunch, and dinner Stocked micro-kitchens with snacks and beverages Commuter benefits, including shuttles and bike-to-work options Unique campus features depending on location Please note that in order to verify work authorization as is required by Federal law (I-9 process), all new employees must complete a live video verification with their selected IDs and provide photos of these selected IDs within their first 3 days of employment.   To know more details (Click here)   In compliance with federal law, all persons hired will be required to verify identity and eligibility to work in the United States and to complete the required employment eligibility verification form upon hire.  In addition, we employ anti-fraud checks to ensure all candidates meet the requirements of the program.     As a trusted global transformation partner, Welocalize accelerates the global business journey by enabling brands and companies to reach, engage, and grow international audiences. Welocalize delivers multilingual content transformation services in translation, localization, and adaptation for over 250 languages with a growing network of over 400,000 in-country linguistic resources. Driving innovation in language services, Welocalize delivers high-quality training data transformation solutions for NLP-enabled machine learning by blending technology and human intelligence to collect, annotate, and evaluate all content types. Our team works across locations in North America, Europe, and Asia serving our global clients in the markets that matter to them. www.welocalize.com   To perform this job successfully, an individual must be able to perform each essential duty satisfactorily. The requirements listed below are representative of the knowledge, skill, and/or ability required. Reasonable accommodations may be made to enable individuals with disabilities to perform essential functions. Why This Role This role is a critical part of how modern AI systems are built. The data you produce directly impacts how speech and voice models understand and interact with real users. It’s a great entry point into AI operations, offering exposure to large-scale systems and the opportunity to build foundational experience in data, language, and AI workflows.

Location: California; Sunnyvale, CA; San Francisco, CA; Seattle, WA; Redmond, Washington; Burlingame, CA; New York City Metropolitan Area; Los Angeles, CA; Washington, D.C.; Boston, MA Workplace: onsite Overview Welo Data is looking for detail-oriented and reliable individuals to join our team as Data Labeling Analysts, supporting speech and voice AI systems. This is a high-impact production role focused on building the datasets that power real-world AI systems. You’ll be working with audio, speech, and language data — helping ensure models are trained on accurate, well-structured, and representative inputs. While this role is more execution-focused than evaluation-heavy roles, it still requires strong judgment, attention to detail, and consistency. The work sits at the intersection of language, data, and AI systems — where precision and discipline matter at scale. We’re looking for people who are dependable, focused, and take pride in producing high-quality work, even across repetitive workflows.  Project Details Job Title: Data Labeling Analyst (Speech & Voice AI)  Hiring in: Onsite (Bay Area, NYC, or client-dependent)  Hours: Full-time, 40 hours per week Employment Type: W2 Full-Time Employee Work Authorization: Must be authorized to work in the U.S. (no visa sponsorship) Hourly Pay Rate: $28/hour Important: This is a 100% onsite position — remote work is not available for this role. To be considered, candidates must be located in or able to commute to one of the following cities: NYC,  Bellevue, Redmond, San Francisco, Sunnyvale, Burlingame, Los Angeles, Washington DC, Boston. Please only apply if you meet this location requirement. What You’ll Do Execute high-volume data labeling and annotation tasks across speech and voice datasets Follow detailed guidelines to ensure consistency, accuracy, and data integrity at scale Work with audio and language data, including transcription, categorization, and tagging Maintain strong throughput while meeting quality expectations Escalate unclear or ambiguous cases appropriately Adapt to evolving guidelines and workflows as systems and requirements change Support baseline data production needs for AI training pipelines Contribute to team calibrations and quality alignment sessions What We’re Looking For Native-level fluency in English (UK) Strong written communication skills and language fundamentals 1 year of work experience in data labeling, annotation, or content-focused work; or a Bachelor's degree or equivalent academic qualification in a related field. Ability to follow detailed instructions and apply guidelines consistently High attention to detail and ability to maintain accuracy in repetitive tasks Comfort working in structured, process-driven environments Ability to manage time effectively and maintain steady output Willingness to ask questions and escalate when needed Basic familiarity with AI, speech technology, or language data is a plus Benefits Paid Vacation: 6 days Paid Company Holidays: 2 days (Memorial Day and Labor Day) Paid Sick Leave: accrued per applicable state law and company policy Medical, Dental, and Vision Insurance (eligibility applies) Health Savings Account (HSA) 401(k) Retirement Plan Employee Assistance Program Additional voluntary benefits (life, accident, critical illness, etc.) Onsite Perks (where applicable): Free breakfast, lunch, and dinner Stocked micro-kitchens with snacks and beverages Commuter benefits, including shuttles and bike-to-work options Unique campus features depending on location Job Reference: #L1-CC1 Please note that in order to verify work authorization as is required by Federal law (I-9 process), all new employees must complete a live video verification with their selected IDs and provide photos of these selected IDs within their first 3 days of employment.   To know more details (Click here)   In compliance with federal law, all persons hired will be required to verify identity and eligibility to work in the United States and to complete the required employment eligibility verification form upon hire.  In addition, we employ anti-fraud checks to ensure all candidates meet the requirements of the program.     As a trusted global transformation partner, Welocalize accelerates the global business journey by enabling brands and companies to reach, engage, and grow international audiences. Welocalize delivers multilingual content transformation services in translation, localization, and adaptation for over 250 languages with a growing network of over 400,000 in-country linguistic resources. Driving innovation in language services, Welocalize delivers high-quality training data transformation solutions for NLP-enabled machine learning by blending technology and human intelligence to collect, annotate, and evaluate all content types. Our team works across locations in North America, Europe, and Asia serving our global clients in the markets that matter to them. www.welocalize.com   To perform this job successfully, an individual must be able to perform each essential duty satisfactorily. The requirements listed below are representative of the knowledge, skill, and/or ability required. Reasonable accommodations may be made to enable individuals with disabilities to perform essential functions. Why This Role This role is a critical part of how modern AI systems are built. The data you produce directly impacts how speech and voice models understand and interact with real users. It’s a great entry point into AI operations, offering exposure to large-scale systems and the opportunity to build foundational experience in data, language, and AI workflows.

Требования к специалисту: — Уверенный опыт работы с LLM/SLM (Few-shot learning, prompt engineering) и BERT (классификация сценариев и интентов). — Практический опыт finetuning LLM (PEFT, LoRA/QLoRA) под узкие прикладные задачи. — Уверенный дебаг LLM: умение быстро понять, почему модель ответила не так, и исправить это промптом, гиперпараметром или файнтюном. — Опыт дообучения и оптимизации TTS-моделей. — Опыт работы и интеграции современных ASR-систем (в частности, GigaAMv2). — Опыт работы как с графовыми/сценарийными диалоговыми движками, так и с LLM-агентами. — Высокая скорость диагностики проблем в инференсе, локализации багов и принятия инженерных решений. — Уверенное владение Python (PyTorch, huggingface, фреймворки инференса) и Go. — Опыт работы с оптимизацией инференса под low-latency (vLLM, TensorRT-LLM, Triton Inference Server, ONNX Runtime). — Практика работы со потоковым аудио в реальном времени (streaming ASR/TTS, gRPC-контракты). — GoLang: базовое понимание дизайна сервисов (SOLID, DRY, KISS); типизация (строки, числа, слайсы и мапы); примитивы синхронизации и конкуренция (горутины, каналы); указатели и работа с ними; observability. — Будет плюсом: опыт работы с gRPC; умение настроить инфраструктуру (k8s/inventory); знания работы TCP. Задачи: — Поэтапный перевод системы с графа сценариев и скиллов на управляемый LLM-based диалог. — Дебаг голосового пайплайна (поиск причин high latency, галлюцинаций LLM, проблем с передачей аудио, графовых ошибок и т.п.). — Оперативное принятие архитектурных решений и их самостоятельная реализация в коде. — Работа с диалоговым стеком и NLP: поддержка и быстрая отладка few-shot классификации сценариев на базе Qwen и классификаторов на BERT. — Учёт краевых случаев, исправление ошибочных срабатываний интентов. — При необходимости — finetuning локальных LLM (извлечение сущностей, удерживание инструкций). — Работа с речевым стеком: интеграция и поддержка ASR на базе GigaAMv2 (отладка качества распознавания, работы VAD и передачи текста дальше по пайплайну). — Дообучение и настройка инференса собственных и опенсорсных TTS и систем клонирования голоса для достижения нужного качества звучания и скорости работы.

Location: Lugano, Switzerland Workplace: remote Join Tether and Shape the Future of Digital Finance At Tether, we’re not just building products, we’re pioneering a global financial revolution. Our cutting-edge solutions empower businesses—from exchanges and wallets to payment processors and ATMs—to seamlessly integrate reserve-backed tokens across blockchains. By harnessing the power of blockchain technology, Tether enables you to store, send, and receive digital tokens instantly, securely, and globally, all at a fraction of the cost. Transparency is the bedrock of everything we do, ensuring trust in every transaction. Innovate with Tether Tether Finance: Our innovative product suite features the world’s most trusted stablecoin, USDT , relied upon by hundreds of millions worldwide, alongside pioneering digital asset tokenization services. But that’s just the beginning: Tether Power: Driving sustainable growth, our energy solutions optimize excess power for Bitcoin mining using eco-friendly practices in state-of-the-art, geo-diverse facilities. Tether Data: Fueling breakthroughs in AI and peer-to-peer technology, we reduce infrastructure costs and enhance global communications with cutting-edge solutions like KEET , our flagship app that redefines secure and private data sharing. Tether Education : Democratizing access to top-tier digital learning, we empower individuals to thrive in the digital and gig economies, driving global growth and opportunity. Tether Evolution : At the intersection of technology and human potential, we are pushing the boundaries of what is possible, crafting a future where innovation and human capabilities merge in powerful, unprecedented ways. Why Join Us? Our team is a global talent powerhouse, working remotely from every corner of the world. If you’re passionate about making a mark in the fintech space, this is your opportunity to collaborate with some of the brightest minds, pushing boundaries and setting new standards. We’ve grown fast, stayed lean, and secured our place as a leader in the industry. If you have excellent English communication skills and are ready to contribute to the most innovative platform on the planet, Tether is the place for you. Are you ready to be part of the future? About the job As a member of our AI research team, you will drive innovation in model compression and efficient deployment for advanced multimodal AI systems, including large language models (LLMs) and vision-language models (VLMs). Your work will focus on reducing model footprint and computational cost while preserving accuracy, enabling high-performance AI to run efficiently across resource-constrained edge devices. You will apply and advance compression techniques such as quantization, knowledge distillation, and pruning to streamline complex multimodal architectures that integrate text, images, and audio. We expect you to have deep expertise in model compression methods and a strong background in multimodal model architectures. You will adopt a hands-on, research-driven approach to develop, test, and implement novel compression strategies that balance model size, latency, throughput, and accuracy. Your responsibilities include building robust compression pipelines, establishing performance and fidelity metrics, and addressing bottlenecks in production inference. The ultimate goal is to deliver scalable, low-memory, low-latency AI systems on edge devices (i.e., smartphones) that maintain high fidelity and tangible real-world value. Responsibilities Apply low-bit quantization to reduce model size and inference latency for generative AI models (LLMs, VLMs, multimodal) while maintaining accuracy and output quality. Leverage knowledge distillation to transfer capabilities from larger teacher models to smaller student models, enabling efficient multimodal reasoning across text, image, and audio inputs. Implement pruning techniques to remove redundant parameters and attention heads, reducing computational overhead without sacrificing task performance. Analyze trade-offs between model efficiency (size, latency, memory) and accuracy across quantization, distillation, and pruning methods; propose improvements based on empirical findings. Research and apply mixed-precision quantization and other advanced compression strategies (e.g., adaptive pruning schedules, distillation with intermediate feature matching) to optimize the accuracy–performance balance. Stay current with the latest research in model compression, including emerging techniques for multimodal and generative architectures. Document methodologies, experiments, and results clearly to support reproducibility, internal collaboration, and stakeholder communication. Author technical papers and publish findings in top-tier conferences (e.g., NeurIPS, ICML, ICLR, CVPR, ACL, AAAI) to advance the field of model compression for multimodal AI. List the qualifications, certifications, and experience needed for the role. You can split this into mandatory and preferred sections if needed. A degree in Computer Science  or related field. Ideally PhD in NLP, Machine Learning, or a related field, complemented by a solid track record in AI R&D (with good publications in A* conferences). Experience with PyTorch deep learning frameworks or equivalent frameworks Hands-on experience with model quantization including both Quantization-Aware Training (QAT) and Post-Training Quantization (PTQ). Research and hands-on experience with knowledge distillation for compressing large models into smaller, efficient ones. Research and hands-on experience with model pruning for compressing large models into smaller, efficient ones. Solid understanding of neural network architectures and training processes – Including transformers (e.g., LLMs, VLMs), backpropagation, optimization, and fine-tuning techniques. Familiarity with C++ is a plus (especially for implementing low-level quantization kernels or inference optimizations). Important information for candidates Recruitment scams have become increasingly common. To protect yourself, please keep the following in mind when applying for roles: Apply only through our official channels. We do not use third-party platforms or agencies for recruitment unless clearly stated. All open roles are listed on our official careers page: https://tether.recruitee.com/ Verify the recruiter’s identity. All our recruiters have verified LinkedIn profiles. If you’re unsure, you can confirm their identity by checking their profile or contacting us through our website. Be cautious of unusual communication methods. We do not conduct interviews over WhatsApp, Telegram, or SMS. All communication is done through official company emails and platforms. Double-check email addresses. All communication from us will come from emails ending in @ tether.to or @ tether.io We will never request payment or financial details. If someone asks for personal financial information or payment at any point during the hiring process, it is a scam. Please report it immediately. When in doubt, feel free to reach out through our official website.

Задачи: — Разрабатывать и выводить в промышленную эксплуатацию решения на базе ИИ, в том числе AI-агентов на базе LLM — Проектировать архитектуру AI-агентов и подходы к их построению — Работать с Deep Learning в части NLP, включая архитектуру Transformer — Делать fine-tuning языковых моделей (GPT-like, BERT-like и др.) — Разрабатывать интеграции с ИТ-системами — Работать с промышленным k8s Требования: — Data Scientist / AI Engineer с коммерческим опытом от 3 лет, который в роли DS/AI Engineer реализовал и вывел в прод решения на базе ИИ, включая AI-агентов на базе LLM — Человек, который одинаково уверенно чувствует себя и в классическом ML, и в глубоком обучении, и в инфраструктуре — Коммерческий опыт работы от 3 лет — Опыт реализации и вывода в промышленную эксплуатацию решений на базе ИИ — обязательно в том числе AI-агентов на базе LLM — в роли Data Scientist / AI Engineer — Знание основ Classic Machine Learning — Хорошее знание Deep Learning (NLP) — Глубокое понимание архитектуры Transformer — Глубокое понимание подходов и методов проектирования AI-агентов — Практический опыт fine-tuning языковых моделей (GPT-like, BERT-like и др.) — Уверенная работа со стеком Python — Torch, Transformers — Опыт разработки интеграций с ИТ-системами — Опыт промышленной работы с k8s

Задачи: — Создавать и развивать AI-ассистентов для разработки и аналитики: кодовые AI ассистенты для автоматизации разработки, помощник аналитика для ускорения экспериментов с использованием LLM. — Проектировать архитектуру агентских решений и их интеграцию с платформой: определять, что остаётся доменной логикой, а что становится переиспользуемым компонентом платформы. — Совместно с командой MLOps формировать требования к платформенным примитивам (Embedding Service, Vector Store, Retrieval/Search API) и внедрять их в пайплайны ассистентов. — Проектировать и развивать систему оценки агентов: разрабатывать метрики, строить симуляции, изолированные и верифицируемые среды оценки. — Вести полный цикл разработки: от ресёрча и MVP до поддержки и развития решения – с анализом ошибок и фидбека пользователей. — Исследовать новые подходы в разработке AI-агентов: harness-среда агента, память, компактизация контекста. Требования: — Опыт промышленной ML-разработки от 5 лет, опыт с LLM от 3 лет. — Опыт разработки AI-агентов и понимание современных паттернов проектирования: память, планирование, циклы агента, tool use, structured output. — Владение Python, способность системно писать код и доводить решения до продакшена. — Ежедневное использование кодинг AI-ассистентов (Codex / Claude Code): понимание их внутреннего устройства, границ применимости, настроенный цикл разработки. — Опыт разработки RAG-систем: retrieval, чанкование, sparse / dense / hybrid поиск, reranking, agentic retrieval. — Понимание основ ML/DL и NLP: метрики качества, embeddings, устройство трансформеров, инференс LLM. — Знание алгоритмов и структур данных, опыт работы со стеком (FastAPI, Docker, Redis, Postgres, Airflow, vLLM). Условия: — ДМС со стоматологией, телемедициной и программой поддержки ментального здоровья. — Страхование жизни. — Ежемесячные промокоды на Lamoda: два по 25%, два по 15% и в день рождения — 40%. — Программа привилегий и скидок от BestBenefits. — До 15 дней больничного в год оплачиваем в размере 100% от действующего оклада. — Бесплатные юридические консультации. — Дополнительные выходные в случае важных событий. — Яркие мероприятия и много неформального общения. Развитие для каждого: — Опытный наставник для каждого новичка. — Обучение и участие во внешних конференциях. — Помогаем развивать личный бренд, выступать на конференциях и писать статьи. — Performance Review дважды в год, чтобы развиваться системно. — Проводим митапы и demo-дни для обмена знаниями. — Погружаем в fashion и каждый сезон знакомим с модными трендами.