Задачи:
— Разработка и улучшение biometric и identity verification решений
— Создание production-level Computer Vision и ML-систем
— Разработка алгоритмов Face Verification, Liveness Detection, Anti-Spoofing и Deepfake Detection
— Оптимизация моделей для высоконагруженных систем
— Работа с большими датасетами и подготовка данных
— Внедрение современных исследований SOTA в production
— Разработка и поддержка полного ML/CV pipeline
— Участие в подготовке решений к международным сертификациям и тестированиям
— Работа над AI-продуктами для банков, финтех-компаний, криптоплатформ, телеком-операторов и государственных организаций
Требования:
— Опыт работы ML Engineer / Computer Vision Engineer от 3 лет
— Опыт работы с Face Biometrics, KYC, Identity Verification или Anti-Fraud решениями
— Глубокие знания Computer Vision и Deep Learning
— Уверенное владение Python
— Опыт работы с PyTorch и/или TensorFlow
— Опыт построения production-grade ML/CV систем
— Опыт работы с large-scale datasets
— Опыт оптимизации inference, latency и производительности моделей
— Понимание архитектуры enterprise biometric systems
— Английский язык на уровне B2
— Обязательное преимущество: Опыт подготовки решений к сертификации iBeta Level 2 или Опыт прохождения / участия в сертификации iBeta Level 2
Будет большим плюсом:
— Опыт работы в biometric, KYC или identity verification компаниях
— Участие в NIST FRVT Evaluations
— Опыт разработки Anti-Spoofing и Liveness Detection систем
— Опыт работы с Deepfake Detection
— Понимание международных biometric compliance и certification standards
— Опыт внедрения SOTA research papers в production
— Опыт teamleading
Условия:
— Работа в международной AI-компании
— Участие в разработке production-grade AI-продуктов для международных рынков
— Официальное трудоустройство по трудовому договору
— График 5/2, 8-часовой рабочий день
— Работа в офисе в Алматы
— Финальный оффер зависит от уровня экспертизы
— Возможность работать над сложными задачами в области Face Biometrics, Digital Identity, KYC, AML, Fraud Prevention и Identity Verification
Задачи:
— активно участвовать в разработке микросервисов на Python;
— участвовать в проработке технических требований, архитектуры и подходов к решению;
— участвовать в управлении бэклогом команды, проводить приоритезацию и контролировать выполнение задач;
— заниматься people-менеджментом: участвовать в найме, развитии сотрудников и т.д.
Требования:
— опыт работы в роли MLOps Engineer от 4-х лет;
— опыт работы в роли Team Lead/ проведение code review – не менее 1 года;
— опыт коммерческой backend-разработки на Python от 3-х лет;
— понимание способов интеграций IT-систем (шины, очереди, API);
— практический опыт с Kubernetes, Redis;
— опыт работы с реляционными базами данных, уверенные знания SQL;
— понимание принципов CI/CD;
— понимание микросервисной архитектуры;
— опыт работы с ИИ моделями и их внедрением.
Будет плюсом:
— знания в области BigData (Hadoop, Spark), OpenSearch;
— знание планировщиков процессов (AirFlow);
— опыт работы в банковской сфере;
— понимание гибких методологий разработки (Agile);
— знание основных инструментов управления разработкой (например: Jira, Confluence);
— знания и опыт в Machine learning;
— опыт управления IT проектами, включая разработку и внедрение ML моделей в продакшн;
— опыт разработки ML моделей в роли data scientist, data engineer, ML Ops.
Условия:
— Хорошая зарплата: уровень обсуждается индивидуально, есть доплаты за менторство и профактивности.
— Развитие: долгосрочные проекты от российских заказчиков, возможность менять направления, прозрачная система Performance Review.
— Комфорт и свобода: релокейт между офисами, выбор формата работы (удалённо, офис, гибрид), адаптация для новых сотрудников.
— Обучение: доступ к корпоративному порталу, митапам, конференциям (и как гость, и как спикер).
— Социальный пакет: ДМС со стоматологией, частичная компенсация спорта, бесплатный английский, оплачиваемый отпуск и больничные.
— Корпоративная жизнь: тимбилдинги, детские праздники, внутренние мероприятия.
Обязанности:
- Запускать и развивать сервисы, которые основаны на ML-моделях и LLM
- Работать над проектами от идеи до продакшена: анализировать потребности, проектировать архитектуру, разрабатывать и внедрять проекты
- Интегрировать модели в удобные для сотрудников интерфейсы
- Вместе с командой влиять на технические решения и стандарты, внедрять лучшие практики
Требования:
- Опыт работы с Python 3
- Опыт работы с FastAPI и базами данных PostgreSQL, контейнерами в Docker и кластерами Kubernetes
- Умение писать тесты на Pytest
- Интерес к миру ML на уровне доменной области
- Самостоятельность и готовность нести ответственность за проект
Задачи:
- Проектировать и согласовывать архитектуру AI-агента, приложений и интеграций.
- Развивать и разрабатывать backend-компоненты AI-агента, общие библиотеки и интеграционные взаимодействия.
- Реализовывать решения на базе LLM, ReAct и RAG, включая поиск и подбор релевантных «шагов» из существующей библиотеки.
- Интегрировать систему с внутренними сервисами, репозиториями, базами знаний и корпоративной инфраструктурой.
- Формировать требования для построения CI/CD, мониторинга, логирования, трассировки и контроля качества для AI-агента.
- Проводить ревью кода, поддерживать код-стандарт и участвовать в командных коммуникациях.
- Взаимодействовать с специалистами по автоматизированному тестированию, QA-инженерами и разработчиками для уточнения требований и сценариев использования.
- Участвовать в разработке пользовательской поверхности решения, в формате IDE plugin.
Требования:
- Опыт работы от 3-4 лет в разработке.
- Высшее образование.
- Практический опыт построения RAG-систем и работы с векторными хранилищами, желательно PostgreSQL + pgvector.
- Опыт работы с LangChain, Python.
- Опыт разработки ReAct агентов.
- Понимание принципов ООП, SOLID и паттернов проектирования.
- Понимание базовых принципов многопоточного и асинхронного программирования, включая GIL и asyncio.
- Практические знания и навыки работы с LLM.
- Опыт работы с инструментами контейнеризации приложений, включая Docker.
- Лидерские качества: умение принимать решения и брать на себя ответственность.
Условия:
- Возможность участия в интересных проектах.
- Возможность профессионального и карьерного роста в компании.
- Опыт работы в команде профессионалов.
- Специальные тарифы для сотрудников в спортивные клубы и языковые курсы и пр.
- Удаленный и гибридные форматы работы.
Задачи:
— Разработка экосистемы сервисов на базе ИИ для автоматизированного анализа контента и аудитории в мессенджерах.
— Реализация RAG/LoRA и open source LLM для обработки TG-контента, семантического анализа, предсказаний, генерации контента и ИИ-отчетов.
— Закрытие задач по подготовке данных, дообучению моделей и их эффективному развертыванию на собственных мощностях.
Требования:
— Практический опыт работы с bare-metal серверами и видеокартами Nvidia. Умение конфигурировать CUDA, драйверы, мониторить утилизацию памяти VRAM и решать проблемы OOM.
— Способность самостоятельно «под ключ» развернуть open-source модель на собственном сервере - от настройки окружения до оптимизации инференса.
— Глубокие знания и коммерческий опыт дообучения LLM (Llama, Mistral, Qwen и др.) с использованием LoRA / QLoRA / PEFT на доменных данных.
— Экспертные знания пайплайнов Retrieval-Augmented Generation. Опыт интеграции и настройки векторных баз данных (PGVector, FAISS).
— Свободное владение Docker и Docker Compose для локальной сборки и деплоя ML-сервисов.
— Опыт работы с фреймворками высокопроизводительного инференса (vLLM, TGI, ONNX Runtime).
— Уверенный Python (FastAPI, Pydantic), асинхронное программирование (asyncio, aiohttp) и батчинг запросов для минимизации latency.
— Векторизация текста (Sentence Transformers), работа с большими массивами текстовых данных, интеграция с Postgres и Redis.
Условия:
— Стабильный доход: конкурентоспособная заработная плата. Выплаты 2 раза в месяц по 50% или 1 раз в месяц (согласовывается с сотрудником).
— Гибкий формат: график 5;2. На старте (первые 2 месяца) - работа в офисе, далее возможен переход на гибридный формат.
— Профессиональную среду: мотивированная команда, отсутствие бюрократии, возможность напрямую влиять на архитектуру ИИ-сервисов.
— Реальные возможности для профессионального роста и обучения.
Задачи:
- Интегрировать LLM в продукты и сервисы (API, backend, пайплайны обработки данных);
- Разрабатывать и оптимизировать prompt’ы, CoT, системные инструкции;
- Оценивать качество ответов (метрики + human eval);
- Настраивать guardrails: безопасность, фильтрация, соответствие требованиям;
- Строить RAG-системы: индексация данных; работа с векторными БД; оптимизация поиска и генерации;
- Оптимизировать инференс (стоимость, latency, batching, caching, квантизация);
- Участвовать в архитектурных решениях вместе с продуктовой и ML-командой;
- Исследовать и внедрять новые подходы и модели;
- При необходимости: работать с мультимодальными моделями (текст, изображения, документы).
Требования:
- Уверенный Python;
- Опыт работы с LLM в проде (не только pet-проекты);
- Понимание prompt engineering, RAG-подходов, оценки качества моделей;
- Опыт деплоя ML/LLM решений (Docker, Kubernetes, cloud);
- Работа с векторными базами данных.
Будет плюсом:
- Fine-tuning / PEFT (LoRA, adapters);
- RLHF или synthetic data generation;
- Опыт с LangChain, LlamaIndex, Hugging Face;
- Оптимизация инференса (vLLM, TensorRT, ONNX);
- Опыт с мультимодальными моделями;
- Публикации / open-source / pet-проекты;
- Участие в хакатонах;
- Понимание продуктовых метрик.
Условия:
- Полная удалёнка;
- Аккредитованная ИТ-компания;
- Интересный продукт, современный стек, микросервисная архитектура;
- Известные клиенты из ТОП-10 в своих сегментах;
- Прокачка скиллов под руководством лидов с 8+ лет опыта;
- Достойный оклад по итогам собеседования + ежеквартальный сбор ОС с премированием лучших сотрудников;
- Возможность взять на себя дополнительные задачи за соответствующее вознаграждение;
- Премии за менторство младших сотрудников;
- Ежеквартальные бонусы и подарки лучшим сотрудникам;
- Пригласите друга и получите щедрое вознаграждение за рекомендацию.
Location: Hybrid (Berlin, Germany). Must be based in or be able to relocate to Berlin, Germany (attendance at the Berlin campus 2 days a week).
Company: A pioneering local delivery platform operating in over 70 countries, focusing on efficient, affordable, and sustainable last-mile delivery.
What you will do:
— Build and scale AI agents capable of reasoning, tool calling, and interacting with internal services and data systems.
— Develop and maintain Retrieval-Augmented Generation (RAG) pipelines using vector databases and semantic retrieval systems.
— Design and implement evaluation frameworks and safety guardrails for agent behavior and model performance.
— Implement observability, monitoring, and cost visibility for LLM and agent workloads.
— Collaborate with data scientists and platform engineers to integrate AI systems with the broader ML ecosystem.
— Optimize systems for performance, reliability, and cost efficiency while operating AI workloads at scale.
Requirements:
— Proven experience shipping LLM-powered systems or AI agents into production.
— 4+ years of experience in production machine learning infrastructure, specifically involving LLMs.
— Strong programming skills in Python with experience building scalable backend services.
— Experience with Cloud platforms, ideally AWS or GCP.
— Strong communication skills and ability to work collaboratively in cross-functional teams.
Nice to have:
— Experience with Kubernetes, Terraform, Kafka, MLflow, or Kubeflow.
— Experience with LLM application frameworks such as LangChain, LangGraph, or LlamaIndex.
— Experience with ML frameworks such as Hugging Face or PyTorch.
Culture & Benefits:
— Hybrid working model with 2 days a week in the Berlin campus.
— 27 days of holiday, increasing with years of service.
— €1,000 Educational Budget, language courses, and access to Udemy Business.
— Health checkups, meditation, and gym/bicycle subsidies.
— Employee Share Purchase Plan, corporate pension plan, and life/accident insurance.
— Relocation support and dedicated relocation hub for moving to Berlin.
Задачи:
— разрабатывать RAG-помощника для пользователей «Авито Подработки»;
— автоматизировать работу диспетчеров в «Авито Подработке» с помощью LLM;
— участвовать в создании ассистента для «Авито Подработки».
Требования:
— опыт работы в Data Science / Machine Learning от 3 лет;
— опыт построения RAG;
— опыт построения AI-агентов;
— опыт работы с LLM;
— умение писать поддерживаемый, тестируемый и масштабируемый код на Python;
— опыт внедрения моделей в продакшен;
— умение работать с заказчиками внутри команды и за её пределами.
Location: Armenia; Spain; Serbia; Poland; Kyiv City; Kyiv; Odesa; North Macedonia
Workplace: remote
About Provectus
Provectus is an AWS Premier Partner and an Anthropic Strategic Partner, working at the frontier of applied AI. We help enterprises turn Claude, agentic systems, and their own data into measurable business outcomes — through bespoke applications, managed services, and advisory engagements. With offices in North America, LATAM, and EMEA, we partner with clients worldwide.
Our work centers on two verticals — Financial Services & Insurance and Healthcare & Life Sciences — where we deploy five pre-built AI Blueprints: Submission Flow, Portfolio Lens, Asset Flow, Revenue Flow, and Evidence Lens. Each Blueprint rebuilds a critical business process front to back, shipped from working code and tuned to a client's specific book, regulators, and operating posture.
Our team holds 100+ AWS certifications, is Claude Code certified, and co-delivers Anthropic's Agentic SDLC program, Cowork Activation, and AI Blueprint engagements.
Where this role sits
You will work in a small, senior pod alongside an FDE and an FDX,
Forward Deployed Executives (FDX)
own the commercial relationship and the business outcome. Works alongside the client's leadership or C-suite level to move the client's KPIs.
Forward Deployed Engineers (FDE)
embed with a client, map the client workflow, identify the business problem underneath it, design and build a working AI solution, present to the client, and transfer the knowledge to the client's team. Owns technical direction of the whole solution.
Senior AI Engineer.
When an FDE comes back from the client with the business problem, you will help turn that into an agentic system that runs in production and will be responsible for evaluation, observability, and guardrails. You'll have real ownership of components and of the technical decisions inside them.
Requirements:
Mindset
Proactive and self-directed;
you push for clarity rather than waiting for a ticket
Excellent communication and problem-solving skills
Comfort with ambiguity and ownership.
B2+ English, comfortable collaborating across distributed, multicultural teams.
Technical depth
5+ years in software or ML engineering, with production systems you were accountable for
.
Solid AI/ML foundations. You understand what the models do well enough to reason about failure modes.
Shipped to production LLM applications and agentic workflows — not demos, not POCs, not notebooks.
Agentic orchestration: multi-step workflows, graph-based orchestration, tool use, state management, and recovery from partial failure
Experience with LLM APIs (Anthropic, AWS Bedrock, or OpenAI) and agent frameworks.
Experience building and optimizing RAG systems in production.
Strong engineering fundamentals. Full-stack mindset, comfortable across AI, backend development, and cloud infrastructure. Python and/or TypeScript proficiency; depth matters more than stack.
Dropped into an unfamiliar codebase, you're productive.
Hands-on AWS in production: Bedrock, Bedrock AgentCore, Lambda, ECS, S3, SQS, ECR, or similar. GCP or Azure is a plus.
Cloud-native delivery: containers, ECS or Kubernetes, IaC, and CI/CD applied to AI pipelines.
You evaluate. You have built or owned an eval suite for a non-deterministic system, and you can explain what you measured, how you produced ground truth, and what gated a release.
Model and agent monitoring, drift detection.
Cost and latency discipline: model tiering, caching, and the ability to say what a workload costs to run before it runs.
Hands-on production experience with the Claude ecosystem — Claude Code, CLAUDE.md, hooks, skills files. Spec-driven development — writing the intent, constraints, and acceptance criteria before you let an agent build — is a strong plus.
MCP: you can say why an agent would prefer it to a REST integration. Having authored a server is a plus.
Responsibilities:
Work in a pair with an FDE and an FDX.
Build and ship production GenAI systems into the customer’s environment (cloud-native data, LLM-based, and agentic AI solutions).
Build and optimize RAG systems for production use cases
Build the evaluation harness before you build the feature.
Write production code across the stack — AI, backend services, data pipelines. We choose tools to fit the customer.
Integrate AI components into backend services and RESTful APIs
Take systems to production on AWS (GCP or Azure where the customer requires it): containerised, CI/CD. Implement LLMOps and AgentOps practices: agent tracing, prompt and version management, cost and latency monitoring, regression testing, drift detection
Start from the blueprint, contribute to enablement and handover: clear documentation, runbooks, and pairing with the client engineers who will inherit the system. Feed reusable components and lessons back into the Provectus Blueprints
Participate in technical discussions and architectural decisions
Conduct model evaluation, improve failure modes you find, optimize model performance, efficiency, and reliability
Mentor junior and mid-level AI engineers, conduct code reviews and share knowledge across the team through documentation, presentations, and workshops.
Nice to Have:
Experience in one of the industries: financial services, insurance, healthcare.
Consulting, professional services, or other embedded customer-facing delivery.
AWS and Claude Code Certifications
A2A: you can explain agent-to-agent interoperability
CI/CD pipeline experience (GitHub Actions, GitLab CI)
Practical experience with one or more use cases from the following: NLP, LLMs, and Recommendation engines.
Experience in an additional language (Go, TypeScript, or Rust).
Experience with Apache Spark, Apache Airflow, Kafkа
What We Offer:
The chance to shape how leading enterprises across LATAM, Europe, and North America adopt AI, from strategy through first deployment
A forward-deployed model working in small, senior teams alongside FDE and FDX
A growing AI delivery practice where you help build the tooling and frameworks, not just use them
Remote-friendly culture
Internal training programs with full support for Claude, AWS, and other professional certifications, conference attendance
Career growth; we actively develop our engineers
Access to the latest AI tools and premium subscriptions
Long-term B2B collaboration
Private medical insurance or a budget for your medical needs
Paid sick leave, vacation, and public holidays
Equipment and all the tech you need for comfortable, productive work
How we hire:
Intro conversation. The role, your background and aspirations, tech questions.
Technical interview with live engineering sessions. Real problems, your own editor, you may use an LLM assistant
HR Interview. Soft skills and expectations
HM interview. Tech questions; a live engine
Location: Metro Manila; Armenia; Kazakhstan; Georgia
Workplace: Hybrid
Salmon is a technology-driven financial company building a banking and lending platform across Southeast Asia, starting in the Philippines.
We combine global fintech expertise with deep local market knowledge to make financial services simple, accessible, and useful for millions of people across the region.
7M+ app downloads. 2M+ monthly active users. 7,000+ partner stores. US$310M+ raised from leading global investors.
Manila-based, globally distributed, and hybrid-first — our team spans 45+ countries.
If you want to solve complex problems at scale and impact how millions of people access and manage money, come build with us.
Southeast Asia's fintech moment starts here.
About the role:
You'll own ML models across credit scoring, collection, and antifraud, turning data into systems that directly shape how Salmon lends and collects at scale.
What you'll do:
- You'll build and ship ML models across credit scoring, collection, antifraud, and sales — high-stakes, high-visibility work from day one.
- You'll work directly with Collection, Product, and other cross-functional teams to find where a better model moves the business.
- You'll take models from idea to production, then keep monitoring and improving them as real data comes in.
What you'll own:
- Own the end-to-end lifecycle of ML models — EDA, feature generation, hypothesis testing, production deployment, and monitoring.
- Partner with Collection and Product to identify where a new or improved model would have the most leverage.
- Evaluate model performance against business impact, and iterate based on what you find.
- Explore and integrate new data sources, internal and external, to surface additional predictive signals.
- Build models that are scalable, observable, and maintainable in production systems.
What makes you a strong fit:
- 2+ years of experience as a Machine Learning Engineer or Data Scientist, ideally in fintech or fast-paced product environments.
- Solid foundation in classical ML techniques, fluent in Pandas, Scikit-learn, NumPy, SciPy, Matplotlib, Seaborn, Statsmodels, and gradient boosting libraries (XGBoost, LightGBM, CatBoost).
- Strong Python and SQL, with experience using version control.
- A strong sense of ownership and accountability.
- Experience with graph neural networks or transformer models would be a plus.
What we offer:
Ownership and flexibility
- Hybrid-first work environment
- Company-provided tools and equipment
- Office in Bonifacio Global City, Manila — a modern, walkable district at the centre of the Philippines' growing tech scene
Health and time off
- Medical insurance support for you and your family through co-funding or reimbursement, depending on your location and subject to policy limits
- Access to an internal mental health support specialist
- 22 vacation days, Philippine public holidays, and 15 sick days
- 105 days of paid maternity leave and up to 14 days of paid paternity leave for employees relocating to Manila
Relocation and family support
- Relocation support for you, your family — including flights, two weeks of accommodation, visas, paperwork, and logistics
- Reimbursement of eligible pet relocation expenses
- USD 2,500 per year for flights home
- Baby and family stipends for employees relocating to Manila: USD 1,000 per month for each child under two and USD 500 per month for each child aged two to 18
Growth and team experience
- Opportunities to learn and share your expertise through internal expert meetups, external conferences, speaking opportunities, and industry publications
- Company-sponsored trips to Manila to meet and work with your team in person
- Regular team offsites, sports activities, and team events
- High-performing teams can earn a dedicated beach house week in Southeast Asia
Задачи:
— анализ банковских и крипто-транзакций для выявления мошенничества и AML-сценариев
— разработка и улучшение ML-моделей для fraud detection
— graph analysis / Graph ML (GNN, node embeddings, link prediction)
— вывод моделей в production
Требования:
— опыт работы с transactional fraud / AML от 5 лет
— strong Python + ML stack (PyTorch / TensorFlow)
— опыт с Graph ML / Neo4j / NetworkX / GNN
— production ML и ETL pipelines
Будет плюсом:
— MLOps
— ClickHouse / Elasticsearch / Cassandra
— опыт с real-time systems и асинхронным Python
Условия:
— релокация (Узбекистан / Дубай / ЕС) или B2B outside РФ/РБ/Украины
— зарплата в валюте
— relocation package
— международная русскоговорящая команда
— сложные ML/AI задачи и сильная technical culture
Требования:
- отличное владение русским языком
- внимательность, аналитичность
- способность работать по инструкции
Обязанности:
- проверять соответствие описания видео
- вносить корректировки и уточнения
- следовать инструкциям и метрикам качества
Условия:
- оплата от 30 000 рублей
- гибкий график: 5/2, 6/1, 7/0
- работать можно в любое время
- норма — от 6 часов в день
- компания компенсирует налоги
- удалённая работа
- возможность карьерного роста
Location: London, England, United Kingdom
Graham Capital Management, L.P. (collectively with its affiliates, "Graham") is an alternative investment manager founded in 1994 by Kenneth G. Tropin. Specializing in discretionary and quantitative macro strategies, Graham is dedicated to delivering strong, uncorrelated returns across a wide range of market environments. As one of the industry’s longest-standing global macro and trend-following managers, Graham remains committed to innovation, evolving its strategies through a robust investment, technology, and operational infrastructure. Graham harnesses the synergies between its discretionary and quantitative trading businesses to offer a broad suite of complementary alpha strategies, each built on the principles of thoughtful portfolio construction, active risk management, and diversification by design. Graham invests significant proprietary capital alongside its clients – including global institutions, endowments, foundations, family offices, sovereign wealth funds, investment management advisors, and qualified individual investors – reinforcing alignment of interests across all strategies.
The foundation of Graham’s sustainability and success is the experience and contributions of its people. The firm seeks to cultivate talent, encourage the diversity of ideas, and respect the contributions of all. In turn, each employee shares in the responsibility of strengthening those around them.
Description
Graham is seeking a ML Engineer to join our Data Science team, a future-looking technical arm of Graham. We envision, design, prototype and implement the processes that feed Quantitative Research and Discretionary Trading teams as well as the broader firm. We are passionate about what we do and welcome every opportunity to prove it.
The Data Science department straddles traditional Data Science and Engineering roles as well as the application of Machine Learning & AI. We work closely with Quant Researchers, Portfolio Managers, Operations and Execution to continuously improve upon our offering. Every day we work to transform our business through data, technology, and the insights we provide our stakeholders.
At Graham, our systems feed live models around the clock, span billions of market data ticks, an ever-increasing corpus of news and other texts as well as a broad spectrum of financial and alternative data. Our objective is to support the research process by providing our stakeholders with all the right pieces to succeed in their jobs.
Responsibilities
You will be part of a growing team within Data Science. You will work alongside world-class talent to find innovative solutions to some of the most interesting problems on the buy-side. You will work closely with other areas such as Technology, Quantitative Research and Portfolio Manager groups as well as Risk and Operations to learn about problems they face with respect to data and ultimately develop cutting edge solutions. Your focus will be to dive deep into multiple data sets to understand relationships, develop time series, forecasting models, and support quant strategies, and provide new insights and leverage state-of-the-art machine learning and advanced statistical methods to produce the best data sources for the fund.
Requirements
Undergraduate or higher degree in Computer Science, Engineering, Operations Research, or other quantitative discipline
3+ years of hands-on experience with Machine Learning and Statistics on large, unstructured, data sets
Experience writing production code for multi-client systems serving model results is a great plus
Ability to clearly communicate research findings to technical and nontechnical stakeholders
Full-stack experience with Python (preferred) or C++, Spark/Scala, SQL or other distributed data processing technologies as well as experience working comfortably building and deploying services and models in containerized environments
Experience with scientific computing, statistics, optimization, time series, panel data, etc.
Comfortable handling multiple projects to solve varied problems working with multiple teams
Detail-oriented mindset
Sense of ownership of his/her work, working well both independently as well as collaboratively
This role requires commuting into our London office Mondays through Fridays.
Location: Canada; Europe; United States; United Kingdom
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!
Why this role?
Ship state of the art models to production.
Design and implement novel research ideas.
Build elegant training/deployment pipelines.
Join us at a pivotal moment, shape what we build and wear multiple hats as an intern!
Our recruitment process will begin in the upcoming weeks, and we will be carefully reviewing applications and assessing potential candidates for our internships. Should we find a suitable match with your qualifications and our requirements, we will be in touch to discuss the opportunity further and to advance your application to the next stage
Please Note: To be eligible for this position you should be a student currently enrolled in a post-secondary program, available for a full-time 3-6 month internship, co-op, or research work term.
As a Machine Learning Intern, you will:
- Design, train and improve upon cutting-edge models.
- Help us develop new techniques to train and serve models safer, better, and faster.
- Train extremely large-scale models on massive datasets.
- Explore continual and active learning strategies for streaming data.
- Learn from experienced senior machine learning technical staff.
- Work closely with product teams to develop solutions.
You may be a good fit if you have:
- Proficiency in Python and related ML frameworks such as Tensorflow, TF-Serving, JAX, and XLA/MLIR.
- Experience using large-scale distributed training strategies.
- Familiarity with autoregressive sequence models, such as Transformers.
- Strong communication and problem-solving skills.
- A demonstrated passion for applied NLP models and products.
- Bonus: experience writing kernels for GPUs using CUDA.
- Bonus: experience training on TPUs.
- Bonus: papers at top-tier venues (such as NeurIPS, ICML, ICLR, AIStats, MLSys, JMLR, AAAI, Nature, COLING, ACL, EMNLP).
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.
Основные направления:
— Продуктизация ML-моделей и пайплайнов
— Интеграция моделей в backend и бизнес-контуры
— Разработка API и сервисов для ML
— Работа со стримингом данных (Kafka)
— Участие в RnD и пилотировании новых ML-решений
Обязанности:
— Продуктизация и внедрение ML-моделей в production
— Рефакторинг кода от DS-команд под production-стандарты
— Разработка backend-сервисов и REST API для ML-решений
— Интеграция сервисов через Kafka и микросервисную архитектуру
— Участие в построении и развитии ML-пайплайнов
— Взаимодействие с DS, MLOps и аналитиками
— Участие в архитектурных решениях и RnD-задачах
Требования:
— Опыт разработки на Python от 3–4 лет
— Обязательно высшее образование (законченное)
— Опыт работы с ML-проектами (CV, NLP, RAG)
— Опыт разработки REST API (FastAPI / Flask)
— Опыт работы с Kafka
— Понимание микросервисной архитектуры и сетевого взаимодействия
— Базовое понимание Kubernetes
— Опыт работы с SQL на среднем уровне
— Умение работать с кодом Data Science и адаптировать его под production
Будет плюсом:
— Опыт работы с Airflow
— Опыт работы с Hadoop / Spark
Условия:
-ДМС со стоматологией
-Компенсация фитнеса
-Скидки на курсы английского (Skyeng) и в кино
-Индексация зарплаты
-Современная техника для работы
-IT-аккредитация
География найма не уточнена
География найма не уточнена
УдалённоseniorArmenia; Spain; Serbia; Poland; Kyiv City; Kyiv; Odesa; North Macedonia
ГибридMetro Manila; Armenia; Kazakhstan; Georgia; Relocation and family support; Relocation support for you, your family — including flights, two weeks of accommodation, visas, paperwork, and logistics; Reimbursement of eligible pet relocation expenses
London, England, United Kingdom
УдалённоСтажёрCanada; Europe; United States; United Kingdom
