Вакансии

Location: Москва, гибрид, RU; Россия О команде Команда IMV (Instant Market Value) разрабатывает модели оценки товаров и услуг на Авито — от автомобилей и недвижимости до электроники и услуг. Мы также создаём алгоритмы, которые прогнозируют срок продажи в зависимости от выставленной цены. Наши решения помогают продавцам выбирать стратегию: продать быстрее или подождать подходящего покупателя ради большей выгоды. Покупателям они позволяют лучше ориентироваться в рыночных ценах, замечать подозрительно дешёвые предложения и принимать более взвешенные решения. На основе моделей IMV Авито создаёт транзакционные продукты, развивает инструменты модерации и помогает профессиональным продавцам повышать прибыльность бизнеса. Результаты работы наших алгоритмов ежедневно видят миллионы пользователей. Команда развивает систему оценки товаров и услуг, которая помогает пользователям выбирать оптимальную цену и управлять сроками продажи. Для этого мы используем изображения, тексты и табличные данные. Наши модели должны не только точно предсказывать стоимость и сроки продажи, но и быстро адаптироваться к изменениям рынка, устойчиво работать в продакшене и объяснять свои предсказания. Мы ищем тимлида команды, который возглавит направление, определит техническую стратегию и поможет повысить качество моделей. Какие задачи вас ждут: — формировать техническую стратегию IMV и вместе с продуктовой командой определять приоритеты развития; — руководить командой DS- и ML-инженеров: ставить цели, помогать принимать технические решения и обеспечивать достижение результата; — развивать сотрудников, проводить регулярные встречи, давать обратную связь и участвовать в найме; — отвечать за качество моделей и надёжность ML-систем на всём жизненном цикле — от исследований и экспериментов до внедрения и эксплуатации; — выстраивать процессы разработки, экспериментирования и проверки гипотез; — находить возможности для развития продукта и превращать сложные исследовательские задачи в понятный план реализации; — взаимодействовать с продуктовыми, аналитическими, инженерными и инфраструктурными командами; — сохранять глубокое погружение в техническую часть, участвовать в проектировании архитектуры и разборе наиболее сложных задач. Мы ждём, что вы: — имеете опыт управления командой DS- и ML-инженеров от 1 года; — глубоко понимаете ML и нейросетевые архитектуры, умеете системно улучшать качество моделей; — проектировали масштабируемые ML-системы и запускали модели в продакшен; — уверенно владеете Python, PyTorch и основным стеком для работы с данными, хорошо знаете SQL; — умеете понятно объяснять технические решения, аргументировать свою позицию и договариваться с разными командами; — умеете формировать техническую стратегию, декомпозировать задачи и доводить инициативы до результата; — находите баланс между качеством, скоростью разработки и потребностями бизнеса. Работа у нас — это: — возможность влиять на опыт миллионов пользователей и развивать ML-продукт Авито; — значимая роль с большой зоной ответственности и возможностью определять развитие направления IMV; — исследовательские и инженерные задачи на большом масштабе; — сильное комьюнити: команда экспертов, с которой можно обсуждать идеи и подходы; — расширенная забота о здоровье: ДМС, страхование жизни, онкострахование*; — сервис психологической поддержки, а также финансовые, юридические и ЗОЖ-консультации для вас и ваших близких; — корпоративный спорт: групповые тренировки, велбиинг-приложение; — спецпредложения от партнёров и скидки на сервисы Авито: бронирование жилья, услуги продвижения, ипотечное страхование; — компенсация питания.

Location: Warszawa, Poland; Almaty, Kazakhstan; Prague, Czechia; Tallinn, Estonia; Tbilisi, Georgia; Budapest, Hungary; Rīga, Latvia; Vilnius, Lithuania; Lisboa, Portugal; Bucharest, Romania; Madrid, Spain Workplace: fully Customer Description: The customer is a global mobility and urban services platform providing transportation and other on-demand services through a digital marketplace. Project Description: The work spans deep learning model development, large-scale geospatial data pipelines, and low-latency production serving. The candidate will measure the impact through offline evaluation metrics and the results of online experiments Project Phase: ongoing Soft Skills: • Ability to influence teammates and cross-functional stakeholders effectively. • Curious and improvement-oriented mindset with a willingness to challenge existing approaches. • Excellent ability to communicate complex technical findings in a clear and concise manner. Hard Skills / Must Have: • 5+ years of machine learning engineering experience building and deploying deep learning models in production. • Experience building regression, forecasting, or other supervised machine learning systems for production prediction tasks. • Expert-level proficiency in Python and its core data science libraries (e.g., PySpark, Pandas, NumPy, Scikit-learn, PyTorch; gradient-boosting libraries such as CatBoost/XGBoost/LightGBM). • SQL. • Ability to design an ML system from scratch, including data analysis and processing. • Experience translating business goals into ML problems with appropriate metrics and non-functional requirements. • Experience designing and evaluating ML experiments. • Experience with MLOps tools. • Experience working with large-scale geospatial and behavioral datasets. • Experience deploying models to production on ML serving infrastructure and optimizing for latency, and awareness of concept drift and how to detect and manage it. • Comfort working with large-scale geospatial and behavioral data (e.g., GPS traces, H3 spatial indexing) Hard Skills / Nice to Have (Optional): • Academic background in Computer Science, Mathematics, or a related discipline. • Experience with travel time prediction, traffic estimation, or routing quality. • Experience with open-source routing engines. • Knowledge of map matching, speed profiles, road graph tiles, and historical traffic. • Experience with mapping, location, or geospatial products. • Experience building products for developing markets. • Experience with cloud data and machine learning platforms. Responsibilities and Tasks: • Design and build machine learning models to improve routing and travel time prediction. • Develop traffic estimation models using large-scale GPS data. • Implement map-matching solutions for noisy GPS data. • Improve travel time calculation, smoothing, and rerouting logic. • Translate routing objectives into machine learning objectives and evaluation metrics. • Lead offline and online model evaluation activities. • Collaborate with backend engineers to deploy low-latency production models. • Partner with product and operations teams to define new features and requirements. • Own the production ML lifecycle, including serving, monitoring, drift detection, and retraining pipelines. 🧪 Technology Stack: }Python, SQL, PySpark, Pandas, NumPy, Scikit-learn, PyTorch, XGBoost, LightGBM, CatBoost, MLOps, ML Lifecycle Management, Production ML Systems, ML Infrastructure, Geospatial Analytics, GPS Data, H3 Spatial Indexing 📝 Additional notes: The work will be carried out either on client's equipment or via a virtual workstation. Requires fluent Russian , as part of the team communication is conducted in Russian

Location: San Francisco; New York Workplace: Hybrid ABOUT BASETEN Baseten powers mission-critical inference for the world's most dynamic AI companies, like Cursor, Notion, OpenEvidence, Abridge, Clay, Gamma, and Writer. By uniting applied AI research, flexible infrastructure, and seamless developer tooling, we enable companies operating at the frontier of AI to bring cutting-edge models into production. We're growing quickly and recently raised our $1.5B Series F https://www.baseten.co/blog/announcing-our-series-f/, led by Altimeter Capital, Conviction Partners, and Spark Capital. Join us and help build the platform engineers turn to ship AI products. THE ROLE Forward Deployed Engineers work directly with the largest and fastest-growing AI companies in the world, owning their technical outcomes on Baseten and taking on the hardest problems in serving and improving models at scale. The work spans the model lifecycle: inference, post-training, and the systems that tighten the loop between them. - Act as each account's de facto CTO on Baseten, with final accountability for how their workloads are designed, run, and scaled. - Take customer objectives from vague to shipped: frame the problem, define the spec and success criteria, build the PoC, and carry it through to production quickly, using the right tools for the problem. - Design the evals and benchmarks that isolate where quality or performance falls short, then close the gap yourself, whether that means optimizing inference, improving the model through post-training, or reworking the eval itself. - Be the first responder to mission-critical failures including triage, owning the fix directly or route to the owning team and stay accountable until it ships. - Build internal systems so that each engagement is faster than the last. This includes tooling and automation for eval and deployment infrastructure, and the recipes and reference implementations that make the product more self-serve. - Shape the product itself, channeling what your accounts need into the roadmap and shipping fixes and features into Baseten's codebase yourself. - Do all of this across multiple accounts at once, sequencing the work, pulling in the right people at the right time, and keeping customers and internal stakeholders aligned on status and risk. REQUIREMENTS - Minimum 1-2 years of software engineering experience, shipping and maintaining code in large production systems, ideally with breadth across the stack - Experience debugging complex production issues - working through logs, metrics, and traces to root-cause problems in unfamiliar systems - Confidence owning ambiguous technical problems. This includes triaging, making decisions under uncertainty, and knowing when to pull in other engineers who own the underlying systems - Motivation beyond pure engineering. An interest in wanting to work directly with customers, understand their problems firsthand, and influence the product - Clear communication on complex technical topics, whether you're talking to a customer's engineers or their leadership - Genuine curiosity about AI inference and training, and a drive to become an expert in the infrastructure powering it - Willingness to respond to customers outside regular working hours and participate in an on-call rotation - Excitement about solving problems for some of the largest and fastest-growing companies in the world running mission-critical AI workloads WHAT YOU'LL BRING We don't expect any one person to cover all of this - the strongest candidates may spike in one or two of the following: - Depth in a core infrastructure domain such as storage systems or networking (anywhere from the cloud layer to cluster interconnects like InfiniBand and RoCE) - Experience operating distributed compute platforms like Kubernetes, Slurm, or Ray, especially for GPU workloads - A detailed understanding of LLM architectures and modern inference engines like vLLM, TensorRT-LLM, or SGLang - The ability to profile and optimize GPU workloads, in training or serving - Hands-on experience with post-training techniques like SFT and RL, or a broader deep learning background plus fluency in a tensor computation library like PyTorch or JAX - Operational depth - running on-call, leading incident response, debugging distributed systems under pressure BENEFITS - Competitive compensation, including meaningful equity - (U.S. only) 100% coverage of medical, dental, and vision insurance for employee and dependents - Flexible PTO policy including company wide Winter Break (our offices are closed from Christmas Eve to New Year's Day!) - Paid parental leave - Fertility and family-building stipend through Carrot - (U.S. only) Company-facilitated 401(k) - Exposure to a variety of ML startups, offering unparalleled learning and networking opportunities. Apply now to embark on a rewarding journey in shaping the future of AI! If you are a motivated individual with a passion for machine learning and a desire to be part of a collaborative and forward-thinking team, we would love to hear from you. At Baseten, we are committed to fostering a diverse and inclusive workplace. We provide equal employment opportunities to all employees and applicants without regard to race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, or veteran status. We are an Equal Opportunity Employer and will consider qualified applicants with criminal histories in a manner consistent with applicable law (by example, the requirements of the San Francisco Fair Chance Ordinance, where applicable).

Location: Helsinki; Paris Workplace: Hybrid ABOUT VOODOO Founded in 2013, Voodoo is a tech company that creates mobile games and apps with a mission to entertain the world. Gathering 800 employees, 7 billion downloads, and over 200 million active users, Voodoo is the #3 mobile publisher worldwide in terms of downloads after Google and Meta. Our portfolio includes chart-topping games like Mob Control and Block Jam, alongside popular apps such as BeReal and Wizz. TEAM The Engineering & Data team builds innovative tech products and platforms to support the impressive growth of their gaming and consumer apps which allow Voodoo to stay at the forefront of the mobile gaming industry. The Voodoo Ad-Network is an autonomous product group of around 60 highly driven professionals with an ambitious mission: building top-tier ad network services. Our primary goal is to leverage Voodoo's massive first-party data ecosystem to optimize and scale monetization. We are in a rapid growth phase, expanding into new ventures such as opening to external inventory, penetrating the external advertiser market, and driving social network monetization following our recent acquisition of BeReal. To support this incredible trajectory and promising early results, we are scaling our team. The Models team is a core element of the targeting performance. It leverages machine learning and a strong business understanding to directly impact the product's financial performance. It's composed of mostly senior Data Analysts, Analytics Engineers, Data Engineers and Data Scientists/ML Engineers that iterate together on finding and building the next performing iteration. This role can be either Paris or Helsinki based and done in a hybrid setup. ROLE We're looking for a Senior ML Engineer to join our Models team. You will be joining a dedicated squad of Data Engineers, Data Scientists, and ML Engineers focused on building and maintaining the ML training infrastructure that powers our ad-targeting models from data preprocessing through model deployment to production monitoring. In this role, you will be leading the following topics: - Architectural Ownership: Take end-to-end ownership of highly visible projects from ideation to production release. This includes feature scoping, timeline estimation, architecture design, and benchmarking new technologies. - Pipeline Engineering: Build and maintain quality data and ML pipelines to align with ever-evolving business and machine learning needs. Optimize training pipelines for performance, memory efficiency, and cost (e.g. spot instance strategies, efficient data loading, preprocessed artifact reuse). - Data Scientists Enablement: Enable Data Scientists to iterate faster by providing reusable, well-tested pipeline components (transformers, dataloaders, training utilities) and reviewing their contributions to shared code. Extend dataset capabilities: integrating new data sources, scaling feature windows, and increasing training data volumes without breaking pipeline constraints. - Deep Learning Development: Contribute to deep learning development: GPU workload orchestration, custom PyTorch training loops, and model architecture support. - ML Lifecycle & Reproducibility: Maintain reproducibility and consistency across the ML lifecycle: versioned configs, experiment tracking, and online-offline consistency tooling. - Scalability & Reliability: Collaborate with infrastructure teams on scalability — node pools, resource monitoring, CI/CD migrations. Participate in weekly rotation to triage and resolve alerts from Airflow, dbt, and related systems. - Agile Collaboration: Thrive in a fast-paced agile environment with rapid decision-making processes. You will collaborate daily with back-end developers, data scientists, infrastructure engineers, and product managers. - Mentorship & Team Culture: You will actively contribute to our engineering culture, share knowledge, and ensure every team member feels comfortable, supported, and empowered to grow in their role. PROFILE We are looking for a Senior ML Engineer who deeply understands both the ML training lifecycle and the specific challenges of putting machine learning models into production at scale. - 5+ years minimum of experience as an ML Engineer or a similar role - End-to-End ML Ownership: Problem framing, baselines, experimentation, deployment, and iteration - Python Proficiency: Extensive knowledge for ML pipeline code: preprocessing, training/evaluation workflows, experiment utilities, and reproducible configs - Training Mechanics: Comfortable implementing training mechanics where needed (custom steps/metrics, dataloading patterns, performance-conscious preprocessing), not only notebook-level prototyping - ML Frameworks: Practical experience training and evaluating models with scikit-learn, LightGBM, PyTorch for deep learning - Performance Optimization: Experience optimizing memory usage during data preprocessing and model training - Experiment Management: Hyperparameter tuning, experiment tracking, and reproducible training (configs, seeds, versioning) - Cloud & Infrastructure: Experience with Amazon Web Services. Familiarity with scalability, reliability, and security topics - ML Production Awareness: Understanding of the challenges involved in running ML models in production (familiar with topics such as feature store, training-serving skew, etc.) - Model Serving: Experience with model serving infrastructure (real-time or batch inference, latency/throughput optimization) is a plus - Excellent communication skills in English OUR STACK Python · Scikit-learn · LightGBM · PyTorch · DBT · Spark · MLflow · Prometheus · Terraform · Airflow · Kubernetes · Amazon Web Services BENEFITS - Excellent benefits that will depend on the country you're based in

About Us & The Team Til-Qazyna is a national scientific and practical center dedicated to building the core digital and AI infrastructure for the Kazakh language. We develop foundational language resources, linguistic tools, and open-source models, including custom text corpora, morphological analyzers, RAG pipelines, and fine-tuned Large Language Models. We are looking for an AI Engineer to focus exclusively on natural language processing, corpus engineering, and LLM fine-tuning. Key Responsibilities • LLM Adaptation & Fine-Tuning: Fine-tune, evaluate, and benchmark open-source LLMs (Llama, Mistral, Qwen, Gemma) for Kazakh language understanding and generation using parameter-efficient methods (LoRA, QLoRA, PEFT). • NLP & Linguistic Tooling: Develop, maintain, and optimize rule-based and neural NLP components, including morphological analyzers, lemmatizers, tokenizers, and terminology parsers. • Corpus & Data Pipelines: Design automated pipelines for large-scale text crawling, data cleaning, deduplication, synthetic data generation, and dataset curation. • Inference & Deployment: Package NLP models and LLMs into containerized microservices (Docker, FastAPI) and optimize inference using modern serving frameworks (vLLM, Ollama, TensorRT-LLM, ONNX). Requirements • 1+ years of hands-on experience as NLP / AI / Machine Learning engineer. • Solid understanding of the Transformer architecture, attention mechanisms, embeddings, and tokenization. • Experience with core ML/NLP libraries: PyTorch, Hugging Face (transformers, datasets, accelerate, peft), Scikit-learn. • Experience building data preprocessing pipelines and working with structured/unstructured text data. • Practical knowledge of Docker, Linux, and Git. • Degree in Computer Science, Data Science, Applied Mathematics. Nice to Have • Hands-on experience with modern LLM serving and optimization engines (vLLM, TGI, llama.cpp, quantization techniques like AWQ/GPTQ). • Experience with RAG stacks (LangChain, LlamaIndex, ChromaDB, Qdrant, FAISS). • Understanding of the morphological, agglutinative, or grammatical characteristics of the Kazakh language. • Familiarity with alignment techniques (DPO, RLHF) or synthetic data generation. What We Offer • Direct impact on sovereign AI development and language technology used at scale. • Official employment in full compliance with the Labor Code of the Republic of Kazakhstan. • Access to high-performance GPU compute clusters for model training and experimentation.

Обязанности: • разработка и совершенствование End-to-End ML-пайплайнов • разработка продакшен-пайплайнов обработки данных • работа с огромными объёмами данных Сбера (петабайты) на PySpark, исследование подходов применения их в моделях • писать эффективный и масштабируемый код для тренировки и инференса моделей на PyTorch, проводить эксперименты на GPU-кластере • performance оптимизации кода по обработке больших массивов данных или онлайн сервисов рекомендаций с высокой нагрузкой • менторинг младших членов команды, обмен знаниями и экспертизой. Требования: • математический бэкграунд • хорошее знание Python и ключевых фреймворков для работы с данными (PySpark, PyArrow, Pandas) • опыт написания качественного production кода • опыт написания промышленных пайплайнов обработки данных, содержащих множество шагов, зависимостей и сложную логику • опыт использования Airflow (или другого industry-standard оркестраторов пайплайнов, т.к. Luigi, Dagster и т.д.) • хорошее понимание баз данных SQL / NoSQL • навыки работы с генеративными AI-моделями; опыт создания AI-агентов и использования их в работе будет преимуществом • опыт использования GigaChat, Kandinsky и аналогов в продуктах, навыки. Условия: • комфортный современный офис рядом с м. Кутузовская (опционально) • возможность выбрать удобный график – офис/гибрид/удаленка (опционально) • ежегодный пересмотр зарплаты, квартальная и годовая премия (премия указывается в зависимости от должности и системы премирования) • корпоративный спортзал и зоны отдыха • более 400 образовательных программ СберУниверситета для профессионального и карьерного развития • программа адаптации и помощь руководителя на старте (для вакансий уровня Junior) • 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: — Решать сложности бизнес-заказчиков и оценивать полученный результат. — Планировать работу команды (Data Scientists, ML-engineers). — Проектировать развитие системы прогнозирования спроса. — Проектировать с руководителями смежных технических команд совместные интеграции. — Программировать и проводить code review. Требования: — Опыт работы в ML и DS проектах от 5 лет. — Опыт решения задач прогнозирования временных рядов. — Управленческий опыт работы. — Умение писать на Python. — Умение работать с данными на PySpark, SQL или Pandas. — Знание и понимание классических ML алгоритмов (линейная регрессия, деревья решений и градиентный бустинг). — Знание Deep Learning фреймворков (Pytorch, Transformers).

Location: Bellevue Workplace: OnSite LIFE AT UIPATH The people at UiPath believe in the transformative power of automation to change how the world works. We’re committed to creating category-leading enterprise software that unleashes that power. To make that happen, we need people who are curious, self-propelled, generous, and genuine. People who love being part of a fast-moving, fast-thinking growth company. And people who care—about each other, about UiPath, and about our larger purpose. Could that be you? Your Mission At UiPath, we believe the next generation of software won't simply automate tasks—it will reason, plan, adapt, and act autonomously. As a Principal Applied Scientist, you'll help invent that future. You will lead the research and development of frontier AI systems that combine foundation models, multimodal reasoning, agentic workflows, and post-training techniques to build autonomous software capable of operating enterprise applications like a human. This is an individual contributor leadership role for scientists who enjoy building. You'll work at the intersection of applied research and production engineering—designing new algorithms, training and evaluating large models, developing novel post-training methods, and shipping systems that reach enterprise customers. If you're passionate about pushing the limits of LLMs, multimodal models, reasoning, planning, tool use, and AI agents—and you enjoy transforming research into products—this is the role for you. About the Team Our Advanced Machine Learning organization is building large multimodal foundation models capable of understanding enterprise software, reasoning over complex workflows, planning long-horizon tasks, and autonomously interacting with digital systems. Our work spans: - Foundation models - Agentic AI - Multimodal reasoning - Reinforcement learning - Post-training - Model alignment - Evaluation systems - Enterprise AI agents Our mission is to advance practical general intelligence for enterprise automation. What You'll Do - Lead research and development of large-scale foundation models and agentic AI systems for enterprise automation. - Design novel approaches for post-training, model alignment, reinforcement learning, preference optimization, and synthetic data generation. - Develop scalable evaluation frameworks that measure reasoning, tool use, planning, and autonomous task completion. - Build production-quality AI systems that combine LLMs, multimodal models, retrieval, planning, memory, and external tool use. - Drive innovations in long-context reasoning, workflow generation, autonomous software interaction, and multi-agent orchestration. - Design large-scale experiments, analyze model behavior, and iterate using data-driven evaluation. - Collaborate closely with engineering and product teams to transition research into production systems. - Mentor scientists and engineers while raising the technical bar across the organization. - Influence UiPath's long-term AI strategy through technical leadership and scientific innovation. What We're Looking For We're looking for scientists who have successfully taken frontier AI research from idea to production. The ideal candidate has deep expertise in several of the following: - Foundation model training - LLM post-training - Reinforcement Learning from Human Feedback (RLHF) - Preference optimization (DPO, PPO, GRPO or similar methods) - Reward modeling - Synthetic data generation - Model evaluation - Agentic reasoning - Tool use - Function calling - Long-horizon planning - Multimodal learning - Retrieval-Augmented Generation (RAG) - Distributed training and inference - Large-scale PyTorch systems - Model alignment and safety Preferred Experience - PhD or equivalent research experience in Computer Science, Machine Learning, Artificial Intelligence, Statistics, Robotics, or a related discipline. - Significant experience building and deploying production AI systems based on large language models or multimodal foundation models. - Demonstrated ownership of end-to-end ML systems—from research and experimentation through production deployment. - Strong programming skills in Python and deep experience with modern ML frameworks such as PyTorch. - Experience training or fine-tuning large models using distributed compute infrastructure. - Strong publication record or demonstrated technical leadership in applied AI is highly valued. - Excellent communication skills and the ability to influence technical direction across organizations. Technologies We Work With While experience with every technology is not expected, many members of our team work with: - PyTorch - JAX - CUDA - vLLM - Ray - DeepSpeed - FSDP - Triton - LangGraph - Semantic Kernel - MCP - Vector databases - Kubernetes - Azure AI - Hugging Face - Distributed GPU training infrastructure You'll Thrive Here If You... - Love building AI systems—not just publishing papers. - Enjoy tackling ambiguous, first-principles problems. - Are excited by rapidly evolving research. - Think deeply about evaluation as much as modeling. - Care about elegant engineering as much as novel algorithms. - Want your research to power products used by millions of people. #LI-MH1 Maybe you don’t tick all the boxes above—but still think you’d be great for the job? Go ahead, apply anyway. Please. Because we know that experience comes in all shapes and sizes—and passion can’t be learned. Many of our roles allow for flexibility in when and where work gets done. Depending on the needs of the business and the role, the number of hybrid, office-based, and remote workers will vary from team to team. Applications are assessed on a rolling basis and there is no fixed deadline for this requisition. The application window may change depending on the volume of applications received or may close immediately if a qualified candidate is selected. We value a range of diverse backgrounds, experiences and ideas. We pride ourselves on our diversity and inclusive workplace that provides equal opportunities to all persons regardless of age, race, color, religion, sex, sexual orientation, gender identity, and expression, national origin, disability, neurodiversity, military and/or veteran status, or any other protected classes. Additionally, UiPath provides reasonable accommodations for candidates on request and respects applicants' privacy rights. To review these and other legal disclosures, visit our privacy policy https://www.uipath.com/legal/trust-and-security/privacy-policy.

Location: New York City, New York What you’ll be part of Garner is on a mission to transform the U.S. healthcare system — and we’re the only proven player doing exactly that. We partner with employers to redesign how healthcare works: applying 550+ proprietary clinical metrics across 80+ specialties to a dataset of 320M+ patients to identify the best-performing doctors, then using compelling incentives to steer members to the care that helps them get healthier, faster. The result is a rare “win win” — better care and lower costs for both members and employers. In just five years, our work has helped over 2.5 million people access higher-quality care and saved $1B in healthcare costs. We recently raised our Series E and have doubled five years running. If you've ever wanted your work to solve a problem that touches every person in this country, this is the opportunity to do exactly that. You'd be joining a team fundamentally reimagining healthcare in the U.S. — and using AI to scale that impact further and faster than anyone else can. About the Role: We are seeking an exceptional Applied Scientist to join our Applied Science team. In this role, you will design, develop, and deploy the algorithmic systems that power Garner's products and drive meaningful impact for our members. Our members rely on us to answer hard questions — Which doctor should I see? What will it cost? When should we reach out, and how? — and the quality of those answers is determined by the algorithms behind them. This is not a dashboards or descriptive-analytics role. You will own production systems end-to-end: framing the problem, defining the objective function, choosing the right approach (ML, optimization, heuristics, expert systems, or a hybrid), shipping it, and improving it against real-world outcomes. The closest analog outside healthcare is a quantitative researcher at a top hedge fund. What you will do: Own the most ambiguous, high-stakes problems on the team end-to-end, and serve as a technical resource others rely on Frame messy, real-world healthcare and business constraints into clear objectives, tradeoffs, and decision frameworks Define the set of metrics needed to judge whether a solution is working, and validate solutions before they ship Choose the right approach for each problem, from machine learning to optimization to heuristics to simple rules, based on what the problem actually calls for Find novel ways to frame and solve the team's hardest problems, proving out approaches that others build on Set the bar for quality by reviewing others' work with rigor, and build the standards and evaluation tooling the team relies on Build a deep understanding of the healthcare economy and Garner's place in it To make the role concrete, here are three problems on our near-term roadmap: Provider tiering optimization. Build a tiering algorithm that jointly optimizes geographic access and total-cost-of-care savings across our doctor network. The objective function, constraints, and tradeoff surface are all open design questions. AI primary care doctor. Fine-tune and productionize an LLM-based primary care experience on our website, including the evaluation harness, guardrails, and ongoing quality monitoring needed to ship a medical-adjacent product safely. Member engagement model. Build an ML system that ingests claims data and in-app behavior to choose the right channel and moment for each touchpoint — SMS, push, phone, or email — to influence member behavior toward better-quality, lower-cost care. What we're looking for: 4+ years of industry experience as an Applied Scientist, Machine Learning Engineer, Research Scientist, or equivalent; or 2+ years of industry experience with a relevant advanced degree, PhDs preferred A bias toward action, quickly translating ideas into working prototypes to test approaches Strong applied problem-solving skills, with the ability to define good metrics and then deliver solutions that improve them Deep technical range, with fluency across Garner's data and a habit of staying current with advances in the field Strong judgment in choosing between statistical models, heuristics, optimization approaches, and simpler algorithmic methods depending on the problem Strong communication skills and the ability to synthesize complex algorithmic ideas for senior and external stakeholders, and to secure buy-in for cross-team work A desire to be a part of a high-performing, mission-driven team that operates with urgency, a strong sense of individual accountability, and a commitment to authentic feedback What you'll get here You’ll work on problems that matter, at a company working to change healthcare at scale. You’ll work at the intersection of AI and systemic healthcare reform, where the problems we solve are as interesting and compelling as the mission. At Garner, you’ll take on real, ambitious problems with real ownership and autonomy, alongside exceptional, principles-based people who genuinely want you to win. It’s demanding by design. You’ll be challenged to stretch beyond what you thought possible and receive consistent coaching to help you grow and do the best work of your career. This isn't the right fit for everyone, and that's intentional. The people here are driven by what's at stake for real people, and that's what gives our intensity its purpose. Technologies we use: Python, SQL, AWS, Snowflake, pandas, XGBoost, PyTorch, HuggingFace, modern LLM tooling and eval frameworks. We pick tools based on the problem, not the resume — bring your judgment. This is a unique opportunity to join a fast-growing company in a transformative role, helping shape the future of healthcare. Compensation Transparency: The target base comp range for this position is $200,000 – $230,000 . Individual compensation for this role will depend on various factors, including qualifications, skills, and applicable laws. In addition to base compensation, this role is eligible to participate in our equity incentive and competitive benefits plans, including but not limited to: flexible PTO, Medical/Dental/Vision plan options, 401(k) with company match, flexible spending accounts, Teladoc Health and more. Fraud and Security Notice: Please be aware of recent job scam attempts. Our recruiters use getgarner.com and garnerhealth.com email domains exclusively. If you have been contacted by someone claiming to be a Garner recruiter or a hiring manager from a different domain about a potential job, please report it to law enforcement here and to candidateprotection@garnerhealth.com . Equal Employment Opportunity: Garner Health is proud to be an Equal Employment Opportunity employer and values diversity in the workplace. We do not discriminate based upon race, religion, color, national origin, sex (including pregnancy, childbirth, reproductive health decisions, or related medical conditions), sexual orientation, gender identity, gender expression, age, status as a protected veteran, status as an individual with a disability, genetic information, political views or activity, or other applicable legally protected characteristics. Garner Health is committed to providing accommodations for qualified individuals with disabilities in our recruiting process. If you need assistance or an accommodation due to a disability, you may contact us at talent@garnerhealth.com .

Location: New York City, New York What you’ll be part of Garner is on a mission to transform the U.S. healthcare system — and we’re the only proven player doing exactly that. We partner with employers to redesign how healthcare works: applying 550+ proprietary clinical metrics across 80+ specialties to a dataset of 320M+ patients to identify the best-performing doctors, then using compelling incentives to steer members to the care that helps them get healthier, faster. The result is a rare “win win” — better care and lower costs for both members and employers. In just five years, our work has helped over 2.5 million people access higher-quality care and saved $1B in healthcare costs. We recently raised our Series E and have doubled five years running. If you've ever wanted your work to solve a problem that touches every person in this country, this is the opportunity to do exactly that. You'd be joining a team fundamentally reimagining healthcare in the U.S. — and using AI to scale that impact further and faster than anyone else can. About the role: We are seeking an exceptional Applied Scientist III to join our Applied Science team. Garner is hiring Applied Scientists to design and ship the algorithmic systems at the core of our product. Our members rely on us to answer hard questions — Which doctor should I see? What will it cost? When should we reach out, and how? — and the quality of those answers is determined by the algorithms behind them. This is not a dashboards or descriptive-analytics role. You will own production systems end-to-end: framing the problem, defining the objective function, choosing the right approach (ML, optimization, heuristics, expert systems, or a hybrid), shipping it, and improving it against real-world outcomes. The closest analog outside healthcare is a quantitative researcher at a top hedge fund. Where you will work: This role will be based in our New York City office (in the Financial District). You must be willing to work in the office 3 days per week on Tuesday, Wednesday and Thursday. What you will do: Ship production algorithmic systems end-to-end, from problem framing to launch to iteration Frame messy, real-world healthcare and business constraints into clear objectives, tradeoffs, and decision frameworks Define the set of metrics needed to judge whether a solution is working, and validate solutions before they ship Choose the right approach for each problem, from machine learning to optimization to heuristics to simple rules, based on what the problem actually calls for Bring new ideas to your work, experimenting with approaches beyond the obvious to get better results Produce rigorous, well-validated work others can rely on, and review your peers' code and analyses Build a deep understanding of the healthcare economy and Garner's place in it To make the role concrete, here are three problems on our near-term roadmap: Provider tiering optimization. Build a tiering algorithm that jointly optimizes geographic access and total-cost-of-care savings across our doctor network. The objective function, constraints, and tradeoff surface are all open design questions. AI primary care doctor. Fine-tune and productionize an LLM-based primary care experience on our website, including the evaluation harness, guardrails, and ongoing quality monitoring needed to ship a medical-adjacent product safely. Member engagement model. Build an ML system that ingests claims data and in-app behavior to choose the right channel and moment for each touchpoint — SMS, push, phone, or email — to influence member behavior toward better-quality, lower-cost care. What we're looking for: 2+ years of industry experience as an Applied Scientist, Machine Learning Engineer, Research Scientist, or equivalent advanced degree A bias toward action, quickly translating ideas into working prototypes to test approaches Strong applied problem-solving skills, with the ability to define good metrics and then deliver solutions that improve them A solid command of the methods your work calls for and the data your models depend on Strong judgment in choosing between statistical models, heuristics, optimization approaches, and simpler algorithmic methods depending on the problem Strong communication skills, with the ability to present your work clearly to senior stakeholders A desire to be a part of a high-performing, mission-driven team that operates with urgency, a strong sense of individual accountability, and a commitment to authentic feedback What you'll get here You’ll work on problems that matter, at a company working to change healthcare at scale. You’ll work at the intersection of AI and systemic healthcare reform, where the problems we solve are as interesting and compelling as the mission. At Garner, you’ll take on real, ambitious problems with real ownership and autonomy, alongside exceptional, principles-based people who genuinely want you to win. It’s demanding by design. You’ll be challenged to stretch beyond what you thought possible and receive consistent coaching to help you grow and do the best work of your career. This isn't the right fit for everyone, and that's intentional. The people here are driven by what's at stake for real people, and that's what gives our intensity its purpose. Technologies we use: Python, SQL, AWS, Snowflake, pandas, XGBoost, PyTorch, HuggingFace, modern LLM tooling and eval frameworks. We pick tools based on the problem, not the resume — bring your judgment. This is a unique opportunity to work on high-impact search problems in healthcare, helping shape how members find better care through algorithmic systems that directly influence healthcare outcomes. Compensation Transparency: The target base comp range for this position is $158,000 – $190,000 . Individual compensation for this role will depend on various factors, including qualifications, skills, and applicable laws. In addition to base compensation, this role is eligible to participate in our equity incentive and competitive benefits plans, including but not limited to: flexible PTO, Medical/Dental/Vision plan options, 401(k) with company match, flexible spending accounts, Teladoc Health and more. Fraud and Security Notice: Please be aware of recent job scam attempts. Our recruiters use getgarner.com and garnerhealth.com email domains exclusively. If you have been contacted by someone claiming to be a Garner recruiter or a hiring manager from a different domain about a potential job, please report it to law enforcement here and to candidateprotection@garnerhealth.com . Equal Employment Opportunity: Garner Health is proud to be an Equal Employment Opportunity employer and values diversity in the workplace. We do not discriminate based upon race, religion, color, national origin, sex (including pregnancy, childbirth, reproductive health decisions, or related medical conditions), sexual orientation, gender identity, gender expression, age, status as a protected veteran, status as an individual with a disability, genetic information, political views or activity, or other applicable legally protected characteristics. Garner Health is committed to providing accommodations for qualified individuals with disabilities in our recruiting process. If you need assistance or an accommodation due to a disability, you may contact us at talent@garnerhealth.com .

Location: Europe; Canada; New York Workplace: Remote WHO ARE WE? Cohere is the leading security-first enterprise AI company. We build cutting-edge foundation AI models and end-to-end products that are designed to solve real-world business problems. We’re training and deploying frontier models for enterprises who are building AI systems. We believe that our work is instrumental to the widespread adoption of AI and we are looking for folks that want to be part of that. We obsess over what we build. Each one of us is responsible for contributing to increasing the capabilities of our models and the value they drive for our customers. Cohere is a team of researchers, engineers, designers, and more, who are all passionate about their craft. We are a global technology company headquartered in Toronto with key offices in London, New York City, San Francisco, Montreal, Paris, Berlin and Seoul. Join us! Role Overview: At Cohere, we're pushing the boundaries of what these technologies can achieve for production environments, and we're seeking a senior engineer who understands the practical challenges of making AI systems work at scale. This role demands deep technical expertise in production environments and the ability to bridge research concepts with real-world implementations. This position is hands-on and engineering-driven. You'll focus on production systems, experiment with frontier models, and collaborate with talented engineers to build scalable, production ready solutions. We strive for excellence in both engineering and science by meticulously designing experiments and design docs. We blend engineering and research seamlessly. Everyone contributes to both, depending on organizational needs. We provide access to world class compute resources, data, and talent to ensure you can do your best work. This role is perfect for you if you: - Have a deep passion for quality work. - Enjoy tuning and optimising large LLM models. - Comfortable working with people with different levels of software engineering skills, from beginner to more advanced. - Comfortable diving into complex ML codebases to identify and resolve issues, ensuring the smooth operation of our systems. - Thrive in a fast-paced, technically challenging environment, where you can contribute your innovative ideas and solutions. Key responsibilities: - Design and write high-performing and scalable software for training models. - Develop new tools to support and accelerate research and LLM training. - Coordinate with other engineering teams (Infrastructure, Efficiency, Serving) and the scientific teams (Agent, Multimodal, Multilingual, etc.). - Research, implement, and experiment with ideas on our cluster and data infrastructure. - Collaborate, Collaborate, and Collaborate with other scientists, engineers, and teams! Qualifications: - Extremely strong software engineering skills. - Value test-driven development methods, clean code, and strive to reduce technical debts at all levels. - Proficiency in Python and related ML frameworks such as JAX, Pytorch and/or XLA/MLIR. - Experience using and debugging large-scale distributed training strategies (memory/speed profiling). - Experience with distributed training infrastructures (Kubernetes) and associated frameworks (Ray). - Experience in ML and LLM academic research. Working Location: This role can be based remotely or from one of our office locations listed on the job description - there is no minimum in-office qualification requirement. We care most about hiring exceptional people regardless of locations, though please check the location listed on the posting for guidance around the core time zone or working hours alignment expected for the role. FULL-TIME EMPLOYEES AT COHERE ENJOY THESE PERKS: - A weekly lunch stipend of $75/£75 or equivalent in your local currency for lunch. - Full health and dental benefits, including a separate budget for mental health. - RRSP matching, 401K, Pension Scheme. - 100% Parental Leave top-up for up to 6 months, for either parent. - Annual enrichment benefits: Arts & culture, fitness/wellness, quality time, and a workspace improvement credit. Education & learning stipend for conferences, courses, and coaching. - 6 weeks of paid vacation (30 working days!) - Budget for traveling to other offices if you are remote, plus an annual company offsite. HOW AND WHERE WE WORK: - Cohere is remote-friendly, but we also have offices in Toronto, London, New York City, San Francisco, Montreal, Paris, Berlin and Seoul with more opening soon. - For those in the office: a daily lunch program, plenty of snacks, and regular community and social events. - For those not near an office: a co-working benefit so you can work alongside others in your city. - Everyone receives a $500 home office stipend to set up your workspace properly. If any of the above doesn’t line up exactly with your experience, we still encourage you to apply. We strive to create an inclusive work environment for all; we welcome applicants from all backgrounds and are committed to providing equal opportunities. Should you require any accommodations during the recruitment process, please submit an Accommodations Request Form https://docs.google.com/forms/d/12a6IrLdF3kI2nonKSr4tiFuz18rLQbaeYV-JM9L4o9Q/edit, and we will work together to meet your needs. We may use AI-enabled tools to screen and assess applicants against the criteria for this position. This helps our recruiters identify potentially qualified candidates, but it doesn't limit the applications our recruiters may review or consider. Beware of Scams: Cohere will never ask for payment or third-party services (e.g., CV writing) as part of our hiring process. All legitimate roles are listed on the Cohere careers page and LinkedIn only, with all communications from Cohere employees coming from an @cohere.com or @cw.cohere email alias. If jobs are viewed on other sites then please verify these through our official careers https://cohere.com/careers page.

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 . Location: US remote-friendly Reddit has a flexible first workforce. At Reddit we continue to grow our teams with the best talent. We're completely remote friendly and will continue to be after the pandemic. Advertising is Reddit’s primary revenue driver and we have an ambitious goal to turn it into a massive business. Although several large digital ad platforms already exist, advertisers are being increasingly drawn to Reddit because of our passionate communities. We believe our community-centric platform has created the opportunity for Reddit to build a highly differentiated ads business. About the Ads Data Science Team: The Ads Data Science team at Reddit leverages data to maximize advertiser value on Reddit through robust data foundations, metrics, and strategic insights generated through experimentation and cutting-edge DS methods. We work on a wide range of challenging problems in the areas of ads and platform measurement, campaign and creative management, advertiser growth and retention, monetization, and the intersection of brand and community engagement. We are a highly collaborative team of passionate data scientists and engineers who are constantly pushing the boundaries of what's possible with machine learning and statistical modeling. About the Role: We are looking for a highly motivated and experienced Senior Data Scientist to join our growing Ads Data Science team. As a Senior Data Scientist, you will play a key role in developing as well as applying cutting-edge DS models/methods to improve the adoption and performance of our advertising platform through data-driven insights. You will work closely with product managers, engineers, and other data scientists to identify opportunities, define metrics, and build solutions that drive significant impact for Reddit. Responsibilities: Design, develop, and apply DS solutions to inform improvements in advertiser experience and Reddit's ad platform Analyze large-scale datasets to identify trends, patterns, and insights that can be used to improve the effectiveness of our advertising platform Collaborate with product managers and engineers to define product requirements and translate them into data science solutions Develop ML models & DS methods to improve anomaly detection, prediction, & pattern recognition Communicate findings and recommendations to stakeholders across the organization Stay up-to-date on the latest advancements in machine learning and data science Mentor and guide junior data scientists on the team Qualifications: Advanced degree (Masters or Ph.D.) in a quantitative field such as: Statistics, Mathematics, Physics, Economics, or Operations Research For M.S. holders: 5+ years of industry experience in applied science or data science roles For Ph.D. holders: 4+ years of industry experience in applied science or data science roles Platform experience and a deep understanding of the ads ecosystem Strong understanding of statistical modeling, machine learning algorithms, causal inference and experimental design Experience with large-scale data processing and analysis using tools such as Spark, Hadoop, or Hive; knowledge of BigQuery a plus Proficiency in Python or R and experience with machine learning libraries such as scikit-learn, TensorFlow, or PyTorch Experience with SQL and relational databases Excellent communication and presentation skills Passion for Reddit and the online advertising industry Bonus Points: Experience with online advertising and ad tech Experience with causal inference and A/B testing Contributions to open-source projects or publications in relevant conferences or journals Benefits: Comprehensive Healthcare Benefits and Income Replacement Programs 401k with Employer Match Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support Family Planning Support Gender-Affirming Care Mental Health & Coaching Benefits Flexible Vacation & Paid Volunteer Time Off Generous Paid Parental Leave #LI-Remote 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: $190,800 — $267,100 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: Singapore About Nebius: Nebius is leading a new era in cloud infrastructure for the global AI economy. We are building a full-stack AI cloud platform that supports developers and enterprises from data and model training through to production deployment, without the cost and complexity of building large in-house AI/ML infrastructure. Built by engineers, for engineers. From large-scale GPU orchestration to inference optimization, we own the hard problems across compute, storage, networking and applied AI. Listed on Nasdaq (NBIS) and headquartered in Amsterdam, we have a global footprint with R&D hubs across Europe, the UK, North America and Israel. Our team of 1,500+ includes hundreds of engineers with deep expertise across hardware, software and AI R&D. The role We are seeking a highly skilled and customer-focused professional to join our team as a Senior Solutions Architect specializing in Cloud infrastructure and MLOps . As a Cloud Solutions Architect, you will play a pivotal role in designing and implementing cutting-edge solutions for our clients, leveraging cloud technologies for ML/AI teams and becoming a trusted technical advisor for building their pipelines. You’re welcome to work remotely from Singapore. Your responsibilities will include: Act as a trusted advisor to our clients, providing technical expertise and guidance throughout the engagement. Conduct PoC, workshops, presentations, and training sessions to educate clients on GPU cloud technologies and best practices Collaborate with clients to understand their business requirements and develop solution architecture that align with their needs: design and document Infrastructure as code solutions, documentation and technical how-tos in collaboration with support engineers and technical writers Help customers to optimize pipeline performance and scalability to ensure efficient utilization of cloud resources and services powered by Nebius AI Act as a single point of expertise of customer scenarios for product, technical support, marketing teams Assist to Marketing department efforts during events (Hackathons, conferences, workshops, webinars, etc.) We expect you to have: 5+ years of experience as a cloud solutions architect, system/network engineer, developer or a similar technical role with a focus on cloud computing Strong hands-on experience with IaC and configuration management tools (preferably Terraform/Ansible), Kubernetes, skills of writing code in Python Solid understanding of GPU computing practices for ML training and inference workloads, GPU software stack components, including drivers, libraries (e.g. CUDA, OpenCL) Excellent communication skills Customer-centric mindset It will be an added bonus if you have: Hands-on experience with HPC/ML orchestration frameworks (e.g. Slurm, Kubeflow) Hands-on experience with deep learning frameworks (e.g. TensorFlow, PyTorch) Solid understanding of cloud ML tools landscape from industry leaders (NVIDIA, AWS, Azure, Google) #LI-DNI Benefits & Perks: Competitive compensation Career growth and learning opportunities Flexibility and ownership Collaborative and innovative culture Opportunity to work on impactful AI projects International environment and talented teams What's it like to work at Nebius: Fast moving - Bold thinking - Constant growth - Meaningful impact - Trust and real ownership - Opportunity to shape the future of AI Equal Opportunity Statement: Nebius is an equal opportunity employer. We are committed to fostering an inclusive and diverse workplace and to providing equal employment opportunities in all aspects of employment. We do not discriminate on the basis of race, color, religion, sex (including pregnancy), national origin, ancestry, age, disability, genetic information, marital status, veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by applicable law. Applicants must be authorized to work in the country in which they apply and will be required to provide proof of employment eligibility as a condition of hire. If you need accommodations during the application process, please let us know.

Location: New York City, New York What you’ll be part of Garner is on a mission to transform the U.S. healthcare system — and we’re the only proven player doing exactly that. We partner with employers to redesign how healthcare works: applying 550+ proprietary clinical metrics across 80+ specialties to a dataset of 320M+ patients to identify the best-performing doctors, then using compelling incentives to steer members to the care that helps them get healthier, faster. The result is a rare “win win” — better care and lower costs for both members and employers. In just five years, our work has helped over 2.5 million people access higher-quality care and saved $1B in healthcare costs. We recently raised our Series E and have doubled five years running. If you've ever wanted your work to solve a problem that touches every person in this country, this is the opportunity to do exactly that. You'd be joining a team fundamentally reimagining healthcare in the U.S. — and using AI to scale that impact further and faster than anyone else can. About the role: We are seeking an exceptional Manager, Product Applied Science to build and lead a team at the core of Garner's product. Our members rely on us to answer hard questions — Which doctor should I see? What will it cost? When should we reach out, and how? — and the quality of those answers is determined by the algorithms behind them. This is a hands-on management role. You are responsible for the overall technical direction of your squad, building alongside your team of Applied Scientists to solve some of the hardest problems at the company. This is not a dashboards or descriptive-analytics team. Your team owns production systems end-to-end: framing the problem, defining the objective function, choosing the right approach (ML, optimization, heuristics, expert systems, or a hybrid), shipping it, and improving it against real-world outcomes. The closest analog outside healthcare is leading a research pod at a top hedge fund. Where you will work: This role will be based in our New York City office (in the Financial District). You must be willing to work in the office 3 days per week on Tuesday, Wednesday and Thursday. What you will do: Lead a team of 4-6 — hire, coach, and develop them, hold the bar on both rigor and speed, and help raise that bar across Applied Science Own the Population Health team and their outputs as a core responsibility Own your team's roadmap and delivery: what gets built, in what order, and whether it ships Set technical direction, and get in the weeds to do it — stay close enough to the code and the data to call the approach yourself, whether that's machine learning, optimization, a heuristic, or a simple rule, and set the standard for how your team makes that call Define the set of metrics needed to judge whether a solution is working, and validate solutions before they ship Put your own hands on the problems where your judgment matters most — the highest-stakes, least-defined work — and deliver the algorithmic step-changes that move metrics like total-cost-of-care savings, steerage, and member engagement Turn ambiguous business goals into a clear problem set alongside Research, Product, Engineering, and business stakeholders, and represent your team's work to senior leadership Set the quality bar: review your team's work with rigor, and define the metrics that decide whether a solution is working Build a deep understanding of the healthcare economy and Garner's place in it To make the role concrete, here are three problems on our near-term roadmap: Provider tiering optimization. Build a tiering algorithm that jointly optimizes geographic access and total-cost-of-care savings across our doctor network. The objective function, constraints, and tradeoff surface are all open design questions. AI primary care doctor. Fine-tune and productionize an LLM-based primary care experience on our website, including the evaluation harness, guardrails, and ongoing quality monitoring needed to ship a medical-adjacent product safely. Member engagement model. Build an ML system that ingests claims data and in-app behavior to choose the right channel and moment for each touchpoint — SMS, push, phone, or email — to influence member behavior toward better-quality, lower-cost care. The ideal candidate has: 6+ years of industry experience as an Applied Scientist, Machine Learning Engineer, Research Scientist, or equivalent; or 4+ years of industry experience with a relevant advanced degree, PhDs preferred Deep technical credibility, with the range and judgment to lead scientists who are experts in their own right, and the willingness to stay hands-on Strong judgment in choosing between statistical models, heuristics, optimization approaches, and simpler algorithmic methods depending on the problem Strong applied problem-solving skills, with the ability to define good metrics and then deliver solutions that improve them A bias toward action, quickly translating ideas into working prototypes to test approaches Strong communication skills, including at the executive level, with a track record of driving alignment across teams and functions A desire to be a part of a high-performing, mission-driven team that operates with urgency, a strong sense of individual accountability, and a commitment to authentic feedback Technologies we use: Python, SQL, AWS, Snowflake, pandas, XGBoost, PyTorch, HuggingFace, modern LLM tooling and eval frameworks. We pick tools based on the problem, not the resume — bring your judgment. This is a unique opportunity to build and lead the team behind Garner's algorithmic systems — shaping how members find better care, and how applied science gets done at Garner, while staying close to the work yourself. What you'll get here: You’ll work on problems that matter, at a company working to change healthcare at scale. You’ll work at the intersection of AI and systemic healthcare reform, where the problems we solve are as interesting and compelling as the mission. At Garner, you’ll take on real, ambitious problems with real ownership and autonomy, alongside exceptional, principles-based people who genuinely want you to win. It’s demanding by design. You’ll be challenged to stretch beyond what you thought possible and receive consistent coaching to help you grow and do the best work of your career. This isn't the right fit for everyone, and that's intentional. The people here are driven by what's at stake for real people, and that's what gives our intensity its purpose. Compensation Transparency: The target base comp range for this position is $300,000-$390,000. Individual compensation for this role will depend on various factors, including qualifications, skills, and applicable laws. In addition to base compensation, this role is eligible to participate in our equity incentive and competitive benefits plans, including but not limited to: flexible PTO, Medical/Dental/Vision plan options, 401(k) with company match, flexible spending accounts, Teladoc Health and more. Fraud and Security Notice: Please be aware of recent job scam attempts. Our recruiters use getgarner.com and garnerhealth.com email domains exclusively. If you have been contacted by someone claiming to be a Garner recruiter or a hiring manager from a different domain about a potential job, please report it to law enforcement here and to candidateprotection@garnerhealth.com . Equal Employment Opportunity: Garner Health is proud to be an Equal Employment Opportunity employer and values diversity in the workplace. We do not discriminate based upon race, religion, color, national origin, sex (including pregnancy, childbirth, reproductive health decisions, or related medical conditions), sexual orientation, gender identity, gender expression, age, status as a protected veteran, status as an individual with a disability, genetic information, political views or activity, or other applicable legally protected characteristics. Garner Health is committed to providing accommodations for qualified individuals with disabilities in our recruiting process. If you need assistance or an accommodation due to a disability, you may contact us at talent@garnerhealth.com .

Location: New York City, New York What you’ll be part of Garner is on a mission to transform the U.S. healthcare system — and we’re the only proven player doing exactly that. We partner with employers to redesign how healthcare works: applying 550+ proprietary clinical metrics across 80+ specialties to a dataset of 320M+ patients to identify the best-performing doctors, then using compelling incentives to steer members to the care that helps them get healthier, faster. The result is a rare “win win” — better care and lower costs for both members and employers. In just five years, our work has helped over 2.5 million people access higher-quality care and saved $1B in healthcare costs. We recently raised our Series E and have doubled five years running. If you've ever wanted your work to solve a problem that touches every person in this country, this is the opportunity to do exactly that. You'd be joining a team fundamentally reimagining healthcare in the U.S. — and using AI to scale that impact further and faster than anyone else can. About the role: We are seeking an exceptional Staff Applied Scientist to join our Applied Science team. Garner is hiring Applied Scientists to design and ship the algorithmic systems at the core of our product. Our members rely on us to answer hard questions — Which doctor should I see? What will it cost? When should we reach out, and how? — and the quality of those answers is determined by the algorithms behind them. This is not a dashboards or descriptive-analytics role. You will own production systems end-to-end: framing the problem, defining the objective function, choosing the right approach (ML, optimization, heuristics, expert systems, or a hybrid), shipping it, and improving it against real-world outcomes. The closest analog outside healthcare is a quantitative researcher at a top hedge fund. This is a player-coach role. You will be a hands-on-keys Applied Scientist, while also leading a small team that helps you deliver on your roadmap. Your time will be split between your own technical work and working with your team to shape how they approach their problems. This role is a good fit for someone who wants to develop their management toolkit while continuing to work closely on their own technical work, and it can lead either towards management or a deeper senior IC track. Where you will work: This role will be based in our New York City office (in the Financial District). You must be willing to work in the office 3 days per week on Tuesday, Wednesday and Thursday. What you will do: Own the most ambiguous, high-stakes problems facing the company end-to-end, and set how the team frames and approaches them Frame messy, real-world healthcare and business constraints into clear objectives, tradeoffs, and decision frameworks Define the set of metrics needed to judge whether a solution is working, and validate solutions before they ship Choose the right approach for each problem, from machine learning to optimization to heuristics to simple rules, based on what the problem actually calls for, and set the standard for how the team selects and applies these approaches Deliver algorithmic breakthroughs that move the company's most important metrics, pioneering approaches that become how applied science is done at Garner Lead a small team — set their technical direction, unblock them when they're stuck, and share accountability for their growth and the quality of what they ship Review Applied Science work at the highest level across the company, ensuring the methods used across teams are sound and correctly applied Build a deep understanding of the healthcare economy and Garner's place in it To make the role concrete, here are three problems on our near-term roadmap: Provider tiering optimization. Build a tiering algorithm that jointly optimizes geographic access and total-cost-of-care savings across our doctor network. The objective function, constraints, and tradeoff surface are all open design questions. AI primary care doctor. Fine-tune and productionize an LLM-based primary care experience on our website, including the evaluation harness, guardrails, and ongoing quality monitoring needed to ship a medical-adjacent product safely. Member engagement model. Build an ML system that ingests claims data and in-app behavior to choose the right channel and moment for each touchpoint — SMS, push, phone, or email — to influence member behavior toward better-quality, lower-cost care. The ideal candidate has: 6+ years of industry experience as an Applied Scientist, Machine Learning Engineer, Research Scientist, or equivalent; or 4+ years of industry experience with a relevant advanced degree, PhDs preferred A bias toward action, quickly translating ideas into working prototypes to test approaches Strong applied problem-solving skills, with the ability to define good metrics and then deliver solutions that improve them Recognized technical authority, with the judgment to ensure the techniques used across an organization are sound Strong interest in mentoring or technically leading other Scientists — formal management experience is welcome, but not required Strong judgment in choosing between statistical models, heuristics, optimization approaches, and simpler algorithmic methods depending on the problem Strong communication skills, including at the executive level, with a track record of driving alignment across an organization A desire to be a part of a high-performing, mission-driven team that operates with urgency, a strong sense of individual accountability, and a commitment to authentic feedback What you’ll get here You’ll work on problems that matter, at a company working to change healthcare at scale. You’ll work at the intersection of AI and systemic healthcare reform, where the problems we solve are as interesting and compelling as the mission. At Garner, you’ll take on real, ambitious problems with real ownership and autonomy, alongside exceptional, principles-based people who genuinely want you to win. It’s demanding by design. You’ll be challenged to stretch beyond what you thought possible and receive consistent coaching to help you grow and do the best work of your career. This isn't the right fit for everyone, and that's intentional. The people here are driven by what's at stake for real people, and that's what gives our intensity its purpose. Technologies we use: Python, SQL, AWS, Snowflake, pandas, XGBoost, PyTorch, HuggingFace, modern LLM tooling and eval frameworks. We pick tools based on the problem, not the resume — bring your judgment. This is a unique opportunity to work on high-impact problems in healthcare — shaping how members find better care through algorithmic systems that directly influence healthcare outcomes, and helping the scientists around you do the same. Compensation Transparency: The target base comp range for this position is $300,000-$390,000. Individual compensation for this role will depend on various factors, including qualifications, skills, and applicable laws. In addition to base compensation, this role is eligible to participate in our equity incentive and competitive benefits plans, including but not limited to: flexible PTO, Medical/Dental/Vision plan options, 401(k) with company match, flexible spending accounts, Teladoc Health and more. Fraud and Security Notice: Please be aware of recent job scam attempts. Our recruiters use getgarner.com and garnerhealth.com email domains exclusively. If you have been contacted by someone claiming to be a Garner recruiter or a hiring manager from a different domain about a potential job, please report it to law enforcement here and to candidateprotection@garnerhealth.com . Equal Employment Opportunity: Garner Health is proud to be an Equal Employment Opportunity employer and values diversity in the workplace. We do not discriminate based upon race, religion, color, national origin, sex (including pregnancy, childbirth, reproductive health decisions, or related medical conditions), sexual orientation, gender identity, gender expression, age, status as a protected veteran, status as an individual with a disability, genetic information, political views or activity, or other applicable legally protected characteristics. Garner Health is committed to providing accommodations for qualified individuals with disabilities in our recruiting process. If you need assistance or an accommodation due to a disability, you may contact us at talent@garnerhealth.com .

Location: United Kindom, United Kingdom; Buenos Aires, Argentina; Bogotá, Colombia; Sao Paulo, Brazil; Montevideo, Uruguay; Bangalore, India; Chittagong, Bangladesh; Karachi, Pakistan; Tokyo, Japan; Hanoi, Vietnam; Taipei, Taiwan; Bangkok, Thailand; London, United Kingdom; Lugano, Switzerland; Barcelona, Spain; Dubai, United Arab Emirates; Sofia, Bulgaria; Prague, Czechia; Belgrade, Serbia; Copenhagen, Denmark; Tallinn, Estonia; Athens, Greece; Tbilisi, Georgia; Budapest, Hungary; Dublin, Ireland; Sliema, Malta; Oslo, Norway; Amsterdam, Netherlands; Warsaw, Poland; Lisbon, Portugal; Bucharest, Romania; Stockholm, Sweden; Brussels, Belgium; Tel Aviv, Israel; Istambul, Türkiye; Nicosia, Cyprus 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 the AI model team, you will drive innovation in architecture development for cutting-edge models of various scales, including small, large, and multi-modal systems. Your work will enhance intelligence, improve efficiency, and introduce new capabilities to advance the field. You will have a deep expertise in Large Language Model (LLM) and Multi-Modal architectures, a strong grasp of pre-training optimization, and a hands-on, research-driven approach. Your mission is to explore and implement novel techniques and algorithms that lead to groundbreaking advancements: multi-modal data curation and alignment, strengthening baselines, and identifying and resolving existing pre-training bottlenecks to push the limits of cross-modal AI performance. Responsibilities Large-Scale Pre-Training: Conduct foundational pre-training for LLMs and Multi-Modal models (integrating text, vision, audio, or other modalities) on large, distributed servers equipped with multi-nodes & thousands of NVIDIA GPUs. Architecture & Alignment Innovation: Design, prototype, and scale innovative architectures, tokenizers, and cross-modal alignment layers to enhance model intelligence and multi-modal understanding. Data Strategy: Source, filter, and curate massive-scale textual and multi-modal datasets, establishing robust data pipelines for efficient pre-training. Experimental Research: Independently and collaboratively execute experiments, analyze results, and refine training methodologies for optimal performance and token efficiency. Optimization & Debugging: Investigate, debug, and eliminate bottlenecks in model efficiency, computational performance, and multi-modal alignment stability during long training runs. System Scalability: Contribute to the advancement of distributed training systems to ensure seamless scalability and hardware efficiency on target platforms. 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). Hands-on experience contributing to large-scale LLM or Multi-Modal pre-training runs on large, distributed servers equipped with thousands of NVIDIA GPUs, ensuring scalability and impactful advancements in model performance. Familiarity and practical experience with large-scale, distributed training frameworks, libraries and tools. Deep knowledge of state-of-the-art transformer and non-transformer modifications aimed at enhancing intelligence, efficiency and scalability. Strong expertise in PyTorch and Hugging Face libraries with practical experience in model development, continual pretraining, and deployment. 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: - Разработка и обучение ML/DL-моделей для задач NLP и Computer Vision, возможен также classic ML. - Анализ и обработка данных. - Работа с PyTorch. - Тестирование и оптимизация моделей. - Подготовка модели к запуску в продакшене, участие в A/B тестах, поэтапных раскатках и мониторинге качества. - Поддержка существующих моделей. Требования: - Широкий прикладной ML-бэкграунд: опыт работы минимум с двумя направлениями из Computer Vision, NLP / LLM, classic ML (обучение моделей, интеграция их в реальные проекты). - Коммерческий опыт в Data Science / ML от 4 лет. - Уверенное знание Python и PyTorch. - Хорошее знание классического ML и современного DL (включая математику за ними). - Опыт самостоятельного ведения задач на разных этапах проекта. - Умение переводить бизнес-задачу в ML-постановку и предлагать практичные решения.

Задачи: - Разработка и обучение ML/DL-моделей для задач NLP и Computer Vision, возможен также classic ML. - Анализ и обработка данных. - Работа с PyTorch. - Тестирование и оптимизация моделей. - Подготовка модели к запуску в продакшене, участие в A/B тестах, поэтапных раскатках и мониторинге качества. - Поддержка существующих моделей. Требования: - Широкий прикладной ML-бэкграунд: опыт работы минимум с двумя направлениями из Computer Vision, NLP / LLM, classic ML (обучение моделей, интеграция их в реальные проекты). - Коммерческий опыт в Data Science / ML от 4 лет. - Уверенное знание Python и PyTorch. - Хорошее знание классического ML и современного DL (включая математику за ними). - Опыт самостоятельного ведения задач на разных этапах проекта. - Умение переводить бизнес-задачу в ML-постановку и предлагать практичные решения