Задачи:
— Разработка и проектирование Backend с использованием Python, FastAPI и PostgreSQL.
— Интеграция больших языковых моделей (LLM) таких как Kimi, OpenAI, Claude, Llama.
— Работа с AI-агентами, NLP, embeddings и семантическим поиском.
— Разработка алгоритмов.
— Обработка документов в форматах Excel, PDF, OCR и автоматизация работы с ними.
— Создание и вывод AI SaaS-продуктов в production.
— Архитектура, тестирование, оптимизация и отладка сложных систем.
Требования:
— Сильный практический опыт в указанных технологиях.
— Способность разобраться в существующем проекте, усилить архитектуру и довести продукт до стабильной рабочей версии.
Условия:
— Удалённая работа.
— Долгосрочное сотрудничество.
— Условия обсуждаются.
УдалённоseniorГеография найма не уточнена, офис: Минск
Задачи:
— Интеграция новых блокчейн сетей и токенов
— Сопровождение текущего кода
— Оптимизация синхронизации блоков и доработка процессинга криптовалюты под задачи бизнеса
Требования:
— Опыт разработки на Python не менее 5 лет (уровень middle+)
— Опыт работы с FastAPI или асинхронным фреймворком
— Опыт работы с rpc и abi
— Базовые знания криптографии, включая хеширование и ассиметричное шифрование
— Понимание принципов работы блокчейна
— Умение подписывать транзакции
— Понимание принципа работы UTXO
— Желание и умение решать сложные задачи
Будет плюсом:
— Опыт разработки микросервисной архитектуры
— Оптимизация кода под высокие нагрузки
— Знание английского языка на уровне чтения документации
Условия:
— Полная занятость, график 5/2
— Удалённый формат работы
Контакт для отклика: contact@bitbanker.ru
Задачи:
— Разработка и развитие собственной платёжной платформы.
— Интеграция международных PSP, реализация payment routing, cascading и retry-механик.
— Развитие подписок, рекуррентных платежей и токенизации.
— Обработка webhooks, возвратов, chargebacks и сверка платёжных данных.
— Разработка платёжных интерфейсов на React и TypeScript.
— Обеспечение безопасности, отказоустойчивости и мониторинга платёжной инфраструктуры.
— Участие в PCI DSS-аудитах и подготовке технической документации.
— Самостоятельное управление релизами, анализ production-инцидентов и постепенная модернизация legacy-кода.
Требования:
— Более 4 лет коммерческой разработки на Python.
— Уверенное владение FastAPI, Pydantic v2, SQLAlchemy 2.0, PostgreSQL.
— Практический опыт интеграции нескольких PSP и разработки платёжной логики: routing, cascading, recurring payments.
— Опыт работы с PSP-, network- и rebill-токенами.
— Понимание транзакционности, идемпотентности и безопасности платёжных данных.
— Личное участие в прохождении PCI DSS.
— Коммерческий опыт работы с React и TypeScript.
— Опыт самостоятельного деплоя, работы с Docker и обеспечения zero-downtime.
— Умение работать с legacy-кодом и постепенно улучшать архитектуру без остановки сервисов.
— Опыт анализа логов, метрик, трейсинга и решения production-инцидентов.
— Высокая самостоятельность и ответственность за корректность финансовых операций.
Условия:
— Полностью удалённая работа.
— Гибкий график, рабочий часовой пояс GMT+3.
— 28 календарных дней отпуска.
— Возможность профессионального роста до Tech Lead.
УдалённоseniorГибкий график, рабочий часовой пояс GMT+3
Не хотите искать каждый день?
Настройте подбор один раз, и подходящие вакансии будут сами приходить в Telegram.
Настройте подбор, и подходящие вакансии будут приходить в Telegram.
Location: Москва
Мы создаем с нуля экосистему интеллектуальных агентов для автоматизации бизнес-процессов. Нам нужен универсальный специалист, способный вести фичи от проектирования базы данных до реализации сложного интерактивного интерфейса.
Наша цель - разработка готовых решений «под ключ». Вы будете работать на стыке бэкенда и машинного обучения, напрямую влияя на то, как тысячи сотрудников банка взаимодействуют с новыми инструментами оптимизации.
Обязанности
* разработка backend для AI-агентов
* интеграция с API существующих АС
* реализация RAG-пайплайнов
* оптимизация производительности
* Code review
Требования
* 3+ года в backend-разработке
* Python (FastAPI, Django) / Java / Go
* опыт работы с LLM-фреймворками (LangChain, LlamaIndex)
* PostgreSQL, Redis, MongoDB
* Микросервисы, event-driven архитектура)
Будет плюсом:
* навыки работы с генеративными AI-моделями; опыт создания AI-агентов и использования их в работе будет преимуществом
* опыт использования GigaChat, Kandinsky и аналогов в продуктах, навыки создания и использования AI-агентов
* инструментальное владение AI для анализа, генерации и автоматизации.
* навыки работы с генеративными AI-моделями; опыт создания AI-агентов и использования их в работе будет преимуществом
Условия
* комфортный современный офис рядом с м. Кутузовская
* ежегодный пересмотр зарплаты, годовая премия
* корпоративный спортзал и зоны отдыха
* более 400 образовательных программ СберУниверситета для профессионального и карьерного развития
* программа адаптации и помощь руководителя на старте (для Junior позиций)
* 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.
– Опыт коммерческой разработки на Python от 3 лет. – Уверенное владение asyncio и понимание конкурентного выполнения, таймаутов, повторных попыток и контроля нагрузки.
Location: Montevideo, Uruguay; Remote.AR, Remote.LATAM-country; Remote.CO, Colombia; Rosario, Remote.LATAM-country
We are looking for a hands on Middle MLE Infrastructure Engineer to strengthen a shared Data and AI platform. You will focus on execution across CI/CD standards, cloud resource management, identity and access security, observability, and reliable deployments that support other teams.
The Shared Services Platform organization builds and operates the core environment for Data and AI products. You will work on a platform that standardizes CI/CD, security, observability, and cloud resource management so product squads can deliver on reliable infrastructure.
Define and maintain homogeneous CI CD standards and pipelines for data and AI across teams.
Manage cloud resources, capacity, and cost through provisioning, autoscaling, and optimization.
Own identity and access management and centralized credential governance.
Automate security policies through infrastructure as code and SecOps practices.
Run vulnerability scanning and incident response playbooks.
Build observability through metrics, dashboards, and proactive alerting for platform and products.
Ensure reliable versioning and deployment standards across the platform.
Act as a platform enabler for product squads on environment and product infrastructure.
Proven experience as a Platform Engineer, DevOps Engineer, or MLOps Infrastructure Engineer.
Experience with infrastructure as code using Terraform and configuration automation.
Experience with Docker, Kubernetes, and autoscaling.
Experience with CI CD tooling such as GitHub Actions and GitLab CI.
Experience with Git workflows including GitFlow.
Experience with Google Cloud Platform resource management, GCP IAM, billing, and cost control.
Experience with observability tools such as Prometheus, Grafana, Datadog, or similar monitoring solutions.
Experience with IAM, vulnerability scanning, data masking, and policy management.
Ability to communicate effectively with technical and non technical stakeholders.
Experience working in Agile environments.
Fluency in Spanish.
Exposure to MLOps platforms, model registry, and model serving infrastructure.
Experience with FastAPI or Flask.
Familiarity with BigQuery, pipelines, and lakehouse or warehouse environments.
Familiarity with GenAI tooling such as LangChain and Agents.
Location: United States
About Turing
Turing’s mission is to accelerate superintelligence to drive real economic progress. Headquartered in San Francisco, Turing works with frontier AI labs to generate high-quality datasets, reinforcement learning environments, and frontier research benchmarks that improve model capabilities in software engineering, enterprise knowledge work, and advanced STEM reasoning. In software engineering, Turing is the largest and longest-running data provider in the category. Turing also works with Fortune 500 enterprises across financial services, life sciences, healthcare, retail, automotive, and CPG to build and deploy end-to-end agentic AI systems inside mission-critical workflows. By operating on both sides, Turing closes the loop between frontier research and enterprise deployment, turning real-world deployment signals into better data, evaluations, and more capable models. Learn more at
www.turing.com
.
Engineering Manager
Role Overview
We are seeking an Engineering Manager to lead the development of internal tooling and quality-control systems that power AI training workflows. This role is ideal for a leader who enjoys working across application development, infrastructure, and applied AI. You will be part of a rapidly growing team solving complex problems in AI systems, working in a startup-paced environment with high expectations and short timelines.
The ideal candidate is someone who takes ownership beyond what their role technically requires, has a track record of shipping production systems, and is excited by the intensity of a fast-paced, ambiguous environment rather than looking for stable, predictable work.
Key Responsibilities
Tooling and Platform Development:
Build scalable platforms supporting data creation, annotation, evaluation, review, and delivery.
Design and implement APIs, internal applications, and developer tooling that enable efficient workflows.
Develop maintainable, well-tested code that scales with growing demands.
Quality and Efficiency:
Improve dataset quality, task validation, model evaluation, and production efficiency.
Build and iterate on evaluation frameworks that ensure quality and reliability of AI training outputs.
Collaborate with research and domain experts to translate requirements into practical solutions.
Architecture and Standards:
Own architecture, technical delivery, reliability, security, and engineering standards.
Design and implement data pipelines for dataset creation, annotation, and delivery at scale.
Contribute to technical design and architecture decisions that balance speed, quality, and maintainability.
Leadership and Mentorship:
Mentor engineers and establish strong design, testing, and release practices.
Partner with Operations, Product, and AI specialists to identify bottlenecks and deliver pragmatic solutions.
Lead teams through urgent, high-pressure delivery schedules while maintaining quality and engineering excellence.
Technical Skills
Web Development -
Node.js, React, NestJS, Next.js, Express
Python Engineering and Data Science -
FastAPI, NumPy, Jupyter notebooks. Proficiency in large-scale data generation and data pipeline orchestration. Frameworks like Spark, Beam, or Dataflow preferred. Statistical analysis, data visualization, and data manipulation.
Infrastructure -
Kubernetes, Docker, Pulumi, Terraform, infrastructure-as-code
AI Engineering -
LangChain, LiteLLM, RL Gyms, LoRA and fine-tuning, AI agents, and Harbor
Quality Systems -
Dataset validation, evaluation frameworks, automated testing, and QC tooling
Qualifications
Strong software engineering fundamentals with a proven track record of shipping production systems.
Experience leading teams effectively in fast-paced, ambiguous environments.
Demonstrated ability to balance speed, quality, scalability, and maintainability in technical decisions.
Strong debugging, problem-solving, and communication skills with cross-functional teams.
Interest in the evolving challenges of training and evaluating AI systems.
What Success Looks Like
You lead a team that ships high-quality solutions enabling AI training workflows. You can identify technical bottlenecks, design pragmatic solutions, and guide your team to implement them reliably. You're energized by working across the full stack from infrastructure to application code, and you're excited about the technical challenges of building evaluation and quality-control systems for AI at scale while developing strong engineers.
Values
We are client first:
We put our clients at the center of everything we do, because their success is the ultimate measure of our value.
We work at Start-Up Speed:
We move fast, stay agile and favor action because momentum is the foundation of perfection
We are AI forward:
We help our clients build the future of Al and implement it in our own roles and workflow to amplify productivity.
Advantages of joining Turing
Work at the frontier of AI
, helping the world’s leading AI labs improve their most advanced models by building expert datasets, RL environments, and first-of-a-kind benchmarks.
Contribute to leading-edge AI research
and showcase your work at top conferences such as ICLR, ICML, and NeurIPS.
Bring frontier AI innovation to the enterprise
, applying lessons learned from leading AI labs to solve real-world business challenges.
Collaborate with and learn from exceptional colleagues
with deep AI experience from Google, Meta, Amazon, and other leading technology companies.
Move at the pace of AI innovation
, with the speed, ownership, and impact of a startup.
Turing is proud to be an equal opportunity employer. We do not discriminate on the basis of race, religion, color, national origin, gender, gender identity, sexual orientation, age, marital status, disability, protected veteran status, or any other legally protected characteristics. At Turing we are dedicated to building a diverse, inclusive and authentic workplace and celebrate authenticity, so if you’re excited about this role but your past experience doesn’t align perfectly with every qualification in the job description, we encourage you to apply anyways. You may be just the right candidate for this or other roles.
For applicants from the European Union, please review
Turing's GDPR notice here
.
Location: United States
About Turing
Turing’s mission is to accelerate superintelligence to drive real economic progress. Headquartered in San Francisco, Turing works with frontier AI labs to generate high-quality datasets, reinforcement learning environments, and frontier research benchmarks that improve model capabilities in software engineering, enterprise knowledge work, and advanced STEM reasoning. In software engineering, Turing is the largest and longest-running data provider in the category. Turing also works with Fortune 500 enterprises across financial services, life sciences, healthcare, retail, automotive, and CPG to build and deploy end-to-end agentic AI systems inside mission-critical workflows. By operating on both sides, Turing closes the loop between frontier research and enterprise deployment, turning real-world deployment signals into better data, evaluations, and more capable models. Learn more at
www.turing.com
.
The Role
Turing builds large-scale datasets and reinforcement learning (RL) environments that power post-training for the world's leading AI labs and enterprises.
The STEM Horizontal team produces the high-signal coding and STEM data frontier labs train on. We're looking for a
Research Engineering Manager
to lead the research engineering behind its next layer:
synthetic data
that measurably improves models, and
new benchmarks
that measure what today's evals miss.
You'll lead a team of research engineers, work side by side with research scientists, and stay close enough to the code to review an eval harness line by line or prototype a generator yourself when the team needs it. You'll also
build the team
: hiring research engineers is a core part of this role, not a side task. Ideally you've been on the other side, at a frontier lab, and know what makes a lab trust a dataset or a benchmark result.
Synthetic data that earns its place.
Generation, verification, and filtering pipelines where every sample is checked, and where we can show which properties of the data drive downstream gains, not just how much of it we made.
Benchmarks that stay meaningful.
New evaluations for capabilities current benchmarks miss, built with calibrated difficulty, contamination controls, and statistics strong enough that a lab can make a decision on the result.
Quality failures here are quiet. A subtle bug in a verifier doesn't crash anything; it ships thousands of wrong labels into someone's training run and costs weeks. If you want to build the team and the systems that catch that early, this is the seat.
What You'll Do
1. Build and Lead the Team
Build the team:
define the profiles you need, run hiring, and grow research engineers selected for research taste as much as engineering rigor.
Run the team's operating rhythm: project selection, planning, experiment reviews, design docs, and clear ownership.
Coach engineers on experimental design, code quality, and presenting results; give direct, regular feedback and run fair performance cycles.
Define the
standards
the team works to: eval methodology, data quality bars, and reproducibility — every dataset and result can be regenerated from a commit, a config, and a seed.
2. Partner with Research Scientists
Turn hypotheses into
experiment plans
together: what we're testing, the baseline, the ablations, and the result that would change our minds.
Set the agenda
with research scientists. You're expected to bring your own hypotheses, not only implement theirs.
Take research prototypes to production scale without losing the scientific intent.
3. Build Synthetic Data Systems at Scale
Own
synthetic data pipelines
that generate, verify, filter, deduplicate, and version data, combining programmatic generators, LLM-based generation, automated verifiers, and targeted expert review.
Judge data by its
effect on models
: ablations and fine-tuning or RL experiments that show which attributes (difficulty, diversity, correctness, coverage) drive gains.
Build
quality systems
that hold up at volume: gold sets, LLM-judge calibration against expert labels, sampling audits, and agreement metrics.
4. Create New Benchmarks and Evaluations
Find where frontier models fail and where existing benchmarks have saturated, and design
evaluations that measure what's missing
.
Own
benchmark methodology
end to end: task taxonomy, difficulty calibration, graders and verifiers, contamination controls, held-out splits, and confidence intervals.
Ship
eval harnesses
, including agentic and sandboxed-execution evals, that are deterministic, reproducible, and trusted by internal teams and partner labs.
Make the call
on whether a result holds up: partner labs and internal teams act on your sign-off.
Publish benchmarks, datasets, and findings with our research scientists at venues such as NeurIPS, ICML, and ICLR.
5. Stay Hands-On and Work with Frontier Labs
Write code every week on the hardest, highest-leverage parts: core verifier logic, eval statistics, the pipeline component everything else depends on. Expect
a third or more
of your time on direct technical work.
Be the person the team brings its hardest debugging and methodology questions to: why a judge disagrees with experts, why an eval number moved, whether a gain is real or noise.
Act as a
technical counterpart
to researchers at frontier AI labs: scope requests, set acceptance criteria, and explain results and trade-offs plainly.
Partner with Strategic Project Leads, delivery, and engineering teams so research advances land in client work and in the platforms the team builds.
Sample Projects
Identify
a capability gap,
design and ship
a benchmark for it, and see it adopted by partner labs.
Stand up a
synthetic data pipeline for long-horizon coding tasks
with execution-based verifiers, and run the ablations that show which task properties drive RL gains.
Calibrate an LLM-judge grading system
against expert labels, map where it fails, and decide where humans stay in the loop.
Design the experiment that tells a partner lab whether a
two-point gain
on their eval is real.
Decide which of three competing synthetic data approaches the team should bet on, and design the experiments that settle it.
Tech Stack
Research:
Python · PyTorch or JAX · SFT · DPO · PPO / GRPO · RL with verifiable rewards
Evals & data:
LLM-as-judge · programmatic verifiers · sandboxed execution · gold sets · contamination checks
Platform:
Python services (Django / FastAPI) · PostgreSQL · Docker · Terraform · AWS or GCP
What We're Looking For
Experience:
8+ years of hands-on ML or research engineering, at least 5 in industry, including
3+ years directly managing
research engineers, ML engineers, or researchers (hiring and performance management included).
Synthetic data at scale:
you've
owned
synthetic or curated data pipelines in production
whose output drove training decisions
, and can show how you measured their quality and downstream impact.
Evaluation rigor:
you've
designed and shipped
LLM evals or benchmarks that others used to make model decisions, and understand contamination, saturation, judge bias, inter-annotator agreement, variance, and confidence intervals.
Experimental judgment:
you plan ablations up front, choose baselines honestly, and can tell a real effect from noise.
Technical depth:
strong Python; production experience with PyTorch or JAX; working knowledge of the post-training stack (SFT, DPO, PPO/GRPO, RL with verifiable rewards).
Technical direction:
a track record of setting direction for a team, not only executing a roadmap handed to you.
Scale:
distributed data processing and large evaluation or training jobs (e.g., Ray, Spark, Kubernetes) on AWS or GCP.
Communication:
clear written communication for scientists and non-specialists alike; comfortable presenting results to researchers at partner labs.
AI-forward:
daily use of LLM coding tools (Claude Code, Cursor, Copilot) as part of how you ship, with the judgment to review their output critically. This is a must-have.
Education:
PhD or Master's degree in AI, machine learning, computer science, statistics, or a closely related field; PhD preferred. Exceptional equivalent research experience will also be considered. We weigh industry outcomes most heavily.
Strongly Preferred
Frontier AI lab experience:
post-training data, evals, or reward modeling at a frontier lab (e.g., OpenAI, Anthropic, Google DeepMind, Meta, xAI) or a top industry research group.
Built or contributed to a
public benchmark or dataset
the community uses.
Nice to Have
First-author papers at NeurIPS, ICML, ICLR, ACL, EMNLP, or similar.
RL environment and verifier design for agentic tasks: coding agents, tool use, computer use, long-horizon workflows.
Data valuation or attribution: measuring what each slice of data contributes to a model.
Depth in a STEM domain such as math, physics, chemistry, or biology (not required).
Experience managing tech leads or managers, or running several concurrent programs.
Directing large expert networks through specs, rubrics, and fast feedback loops.
A note from our CTO, Ece Kamar
Some of the most important questions in AI can't be answered from inside a single lab: how to evaluate frontier models, how data and RL truly drive capability, and what happens when AI meets the real world. At Turing, we work directly with frontier labs, the academic community, and enterprises deploying AI at scale. That creates a feedback loop where deployment shapes our research and our research shapes the field, and we publish our findings, datasets, and benchmarks openly. Join our talented team at Turing to push the frontier of AI with rigorous science that matters.
Why Turing
Work directly with leading AI labs and enterprises at the frontier of post-training, evaluation, and synthetic data.
Build the datasets and benchmarks that improve and measure the capabilities of advanced AI systems.
Shape how frontier labs measure progress on coding and STEM reasoning.
Operate with high autonomy, rapid iteration, and meaningful commercial impact.
Collaborate with exceptional colleagues from organizations including Google, Meta, Amazon, and other leading technology companies.
Contribute to research that may be shared through technical publications and leading conferences such as ICLR, ICML, and NeurIPS.
Compensation
$260,000 to $350,000 + Equity
Don't meet every single requirement?
Studies have shown that women and people of color are less likely to apply to jobs unless they meet every single qualification. Turing is proud to be an equal opportunity employer. We do not discriminate on the basis of race, religion, color, national origin, gender, gender identity, sexual orientation, age, marital status, disability, protected veteran status, or any other legally protected characteristics. At Turing we are dedicated to building a diverse, inclusive, and authentic workplace and celebrate authenticity, so if you're excited about this role but your past experience doesn't align perfectly with every qualification in the job description, we encourage you to apply anyways. You may be just the right candidate for this or other roles.
For applicants from the European Union, please review Turing's GDPR notice here.
Values
We are client first:
We put our clients at the center of everything we do, because their success is the ultimate measure of our value.
We work at Start-Up Speed:
We move fast, stay agile and favor action because momentum is the foundation of perfection
We are AI forward:
We help our clients build the future of Al and implement it in our own roles and workflow to amplify productivity.
Advantages of joining Turing
Work at the frontier of AI
, helping the world’s leading AI labs improve their most advanced models by building expert datasets, RL environments, and first-of-a-kind benchmarks.
Contribute to leading-edge AI research
and showcase your work at top conferences such as ICLR, ICML, and NeurIPS.
Bring frontier AI innovation to the enterprise
, applying lessons learned from leading AI labs to solve real-world business challenges.
Collaborate with and learn from exceptional colleagues
with deep AI experience from Google, Meta, Amazon, and other leading technology companies.
Move at the pace of AI innovation
, with the speed, ownership, and impact of a startup.
Turing is proud to be an equal opportunity employer. We do not discriminate on the basis of race, religion, color, national origin, gender, gender identity, sexual orientation, age, marital status, disability, protected veteran status, or any other legally protected characteristics. At Turing we are dedicated to building a diverse, inclusive and authentic workplace and celebrate authenticity, so if you’re excited about this role but your past experience doesn’t align perfectly with every qualification in the job description, we encourage you to apply anyways. You may be just the right candidate for this or other roles.
For applicants from the European Union, please review
Turing's GDPR notice here
.
Location: Москва
Центр практического ИИ — подразделение Сбера, которое занимается созданием AI-решений и агентных систем, а также их внедрением в бизнес-процессы банка.
Сейчас мы ищем в команду Senior python-разработчика на проекты по внедрению AI-агентов в hr процессы Сбера (подбор, оценка и развитие компетенций сотрудников и т.д.).
Первый этап отбора на эту вакансию – общение с AI-рекрутером. После отклика ждите сообщение от него в телеграмме, диалог займёт примерно 10 минут. Задача AI-рекрутера — уточнить недостающие детали и ускорить рассмотрение вашей кандидатуры. AI-рекрутер только начинает свой путь, поэтому просим относиться с пониманием. Ваш опыт и участие помогут сделать его удобным и полезным для всех!
Обязанности
### Вы будете:
* анализировать прототипы AI-агентов от Data Scientist’ов
* оптимизировать инференс моделей, LLM и RAG-пайплайнов
* доводить решения до production-ready кода (тесты, типизация, логирование)
* деплоить, настраивать CI/CD и мониторинг
* интегрировать агентов с корпоративными API и базами данных (PostgreSQL, OpenSearch), Kafka.
Требования
### Мы ожидаем, что вы имеете:
* опыт работы разработки на Python от 3 лет, умение писать чистый, тестируемый код
* практический опыт доведения прототипов моделей и AI-агентов до продакшена, знакомы с агентными фреймворками и архитектурами (LangGraph, LlamaIndex и др)
* опыт разработки RAG-систем, работы с реляционными и векторными базами данных (понимание какую БД когда выбирать)
* опыт оптимизации инференса LLM/NLP-моделей
* опыт работы с параллелизмом и асинхронностью
* готовность иметь дело с широким набором инструментов и технологий: Python, Elastic/OpenSearch, Kafka, FastAPI, Docker, OpenShift, SQL, GPU, BitBucket/GitLab).
Условия
### Мы предлагаем:
* комфортный современный офис г.Москва, рядом с метро Кутузовская
* офисный формат работы (возможно обсудить гибрид после исп.срока с посещением офиса не менее 2х дней в неделю)
* ежегодный пересмотр зарплаты и годовая премия
* корпоративный спортзал и зоны отдыха
* более 400 образовательных программ СберУниверситета для профессионального и карьерного развития
* расширенный ДМС, льготное страхование для семьи и корпоративная пенсионная программа
* гибкий дисконт по ипотечному кредиту, равный 1/3 ключевой ставки ЦБ
* подписка Прайм с возможностью совместного использования на трёх близких
* вознаграждение за рекомендацию друзей в команду Сбера.
- Разрабатывать и дорабатывать внутренние веб-сервисы: небольшие приложения на Python (FastAPI / Flask) с веб-интерфейсом; - Писать ботов и интеграции: Telegram и MAX, обмен с 1С по HTTP / OData, выборки из MS SQL;
-Разработка сервисов с использованием LLM. -Опыт работы в роли разработчика от 4 лет в проектах по разработке ПО с использованием инструментов и технологий Python.
Workplace: Удаленно
RWB — международная технологическая группа компаний, образованная в результате слияния двух лидеров рынка — IT-компании Wildberries и оператора наружной рекламы Russ.
Мы развиваем одну из крупнейших цифровых экосистем, объединяя электронную коммерцию, финтех, логистику, рекламу, путешествия, здоровье и другие направления. Наши технологии помогают людям решать повседневные задачи, а предпринимателям — находить новые возможности для роста.
Портал Продавцов — это личный кабинет продавца, где он отслеживает свои товары, изучает аналитику и руководит процессами продаж. Логистика, динамическое ценообразование, подъем-моделирование, рекомендации, временные ряды, качественные данные и NLP — мы работаем во множестве доменов.
Наша задача — не только обеспечить продавцу принятие решений по данным, но и вывести весь UX продавца на новый уровень: от распределения товаров по складам до ИИ для создания карточек товара. Сейчас мы ищем новых коллег ML Engineer'ов, чтобы вместе внедрять SOTA-модели и растить бизнес-метрики на миллиарды.
Обязанности
Строить и реализовывать модели классического ML/DL;
Проводить анализ используемых моделей и улучшать их;
Использовать и понимать современные подходы e2e ML-разработки;
Уметь запускать инференс моделей: batch и real-time;
Принимать участие в формулировке в продуктовой постановке задачи;
Принимать участие в дизайне A/B экспериментов;
Проактивно взаимодействовать с аналитиками и менеджерами по продуктам.
Требования
Опыт решения задач классического ML (временные ряды, классификация, регрессия);
Уверенный опыт разработки на Python (не менее 2-х лет);
Опыт работы с Scikit-learn, PyTorch, бустингами;
Опыт написания backend-оберток для ML-моделей на FastAPI;
Навыки контейнеризации k8s будут плюсом;
Опыт работы с Apache Airflow, ClearML или аналогичными решениями;
Опыт в Big Data стеке: HDFS, PySpark, Trino;
Умение использовать эконометрические подходы в работе с данными.
Условия
Обучение и развитие: языковые клубы, собственный корпоративный университет, программы развития управленческих навыков и многое другое;
Благополучие сотрудников: корпоративный пакет ДМС со стоматологией, корпоративный спорт, консультации психолога и дополнительные возможности аккредитованной IT-компании;
Множество сообществ: клуб спикеров, футбола, йоги, шахмат и т.д.;
Забота о семьях: создаем условия, в которых легко сочетать карьеру и заботу о близких – от гибкого подхода до масштабных проектов для детей сотрудников;
Скидки и партнерские программы: на обучение, страхование, покупки и многое другое;
Комфортная рабочая среда: бесплатное питание в офисе, современные офисы рядом с метро, корпоративная техника и портал для сотрудников.
Автоотклики