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Founding ML Engineer (Spectrum)
Уникальная возможность работать в инкубаторе JetBrains над амбициозным проектом с высокой степенью автономии. Позиция сочетает в себе стабильность крупной компании и драйв стартапа, предлагая влияние на продукт мирового уровня.
Сложность вакансии
Роль Founding Engineer предполагает высочайший уровень ответственности: от проектирования архитектуры с нуля до формирования команды. Требуется глубокая экспертиза в LLM, RAG и агентных фреймворках, а также опыт работы в стартапах на ранних стадиях.
Анализ зарплаты
Зарплата в JetBrains обычно соответствует верхнему децилю рынка для опытных инженеров. Учитывая статус 'Founding Engineer' и требования к опыту (5+ лет), компенсация будет значительно выше средней по региону, дополняясь расширенным пакетом бенефитов.
Сопроводительное письмо
I am writing to express my strong interest in the Founding ML Engineer position for Spectrum at JetBrains. With over five years of experience in ML systems and a deep focus on LLMs and agentic workflows, I am excited by Spectrum's mission to move beyond simple vector search toward a unified ontology for software knowledge. My background in building RAG pipelines and deploying scalable AI solutions aligns perfectly with your goal of creating a 'living spec' for complex software systems.
In my previous roles, I have successfully led ML initiatives from zero to one, establishing MLOps practices and designing multi-agent systems that solve real-world reasoning problems. I am particularly drawn to this role because it combines the agility of a startup with the deep expertise of JetBrains. I am eager to apply my skills in prompt engineering, knowledge graphs, and inference optimization to help Spectrum become the single source of truth for architectural knowledge.
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Описание вакансии
Software engineers and AI agents alike suffer from the same problem: finding that one person or place that will answer their tough, specific question. Many solutions promise to solve this with similarity search in vector databases. Unfortunately, finding the answer is often a puzzle with pieces to be collected across a myriad of contradictory sources and cannot be solved without surgical search and careful reasoning.
Spectrum collects data from an organization’s code, docs, and issues, and organizes knowledge in a unified ontology that AI agents can efficiently search through and reason over. We aim to revolutionize the semantic layer space for software-building organizations and move beyond specs that fall out of sync with code, introducing a living spec – one that’s extracted from the whole system and used to keep it aligned. Spectrum is meant to be the single source of truth for all product and architectural knowledge.
Spectrum is a resident of JetBrains' startup incubator, with startup speed and autonomy, and backed by 25 years of developer tooling expertise. We are looking for a top-class ML Engineer who will help us shape the future of software development. You will own our AI and ML engineering stack and help define the research agenda for our team. Your technical vision and design decisions will directly shape the product and determine its success.
Your responsibilities will include:
- Designing and building the ML/LLM solution for data ingestion, knowledge extraction, retrieval, and subsequent reasoning.
- Creating the datasets, metrics, and pipelines that drive measurable improvements across the system.
- Architecting and improving agents for context retrieval, knowledge extraction, and data alignment, which includes prompt engineering, model selection, and inference optimization.
- Establishing MLOps practices, including orchestration, observability, and experiment tracking.
- Collaborating with the engineering team on system design and with JetBrains Research on the research agenda.
- Defining hiring criteria, growing the ML team, and shaping the ML team culture.
We expect you to have:
- A proven track record as an ML/AI Lead.
- At least five years of experience in ML/AI systems, with at least two years focused on LLMs and generative AI.
- A deep understanding of the LLM ecosystem, including model architectures and fine-tuning approaches.
- Hands-on experience with:
- Prompt engineering and LLM pipeline design, including evaluation.
- Agentic frameworks such as LangChain, LlamaIndex, LangSmith, smolagents, or an equivalent.
- Vector databases and retrieval-augmented generation (RAG) patterns.
- Deploying and scaling LLM-powered applications using APIs (e.g. OpenAI or Anthropic) or open-source models.
- Strong Python skills – Kotlin knowledge would be a plus.
- Excellent communication skills, with the ability to explain complex technical concepts to diverse audiences.
- Proficiency in English, both written and verbal.
Our ideal candidate would have:
- Experience with ontologies, knowledge graphs, or graph-based reasoning.
- Experience in early-stage startups – you enjoy the zero-to-one phase.
- The ability to think strategically about product-led AI, beyond just training models in isolation.
- A background in code analysis, developer tools, or software engineering research.
- Experience with multi-agent systems or complex agentic workflows.
- Actively contributed to relevant open-source projects or publications.
What we offer
- A competitive salary and JetBrains benefits.
- A generous runway and corporate resources with startup autonomy.
#LI-KP1
We are an equal opportunity employer
We know great ideas can come from anyone, anywhere. That’s why we do our best to create an open and inclusive workplace – one that welcomes everyone regardless of their background, identity, religion, age, accessibility needs, or orientation.
We process the data provided in your job application in accordance with the Recruitment Privacy Policy.
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Навыки
- Python
- LLM
- Generative AI
- LangChain
- LlamaIndex
- Vector Databases
- RAG
- MLOps
- Prompt Engineering
- Knowledge Graphs
- Kotlin
- Ontologies
Возможные вопросы на собеседовании
Проверка понимания ограничений стандартного RAG и умения работать со сложными структурами данных.
Как бы вы спроектировали систему извлечения знаний из противоречивых источников (код vs документация), чтобы обеспечить консистентность онтологии Spectrum?
Оценка практического опыта работы с современными инструментами разработки ИИ-агентов.
Какие критерии вы используете при выборе между готовыми фреймворками (например, LangChain или LlamaIndex) и написанием собственного решения для оркестрации агентов?
Важно для продукта, который должен работать быстро и эффективно в рамках экосистемы JetBrains.
Опишите ваш подход к оптимизации инференса и сокращению задержек (latency) в сложных многоагентных цепочках рассуждений.
Роль предполагает лидерство и создание процессов с нуля.
Как вы планируете выстраивать процессы MLOps и оценки качества (evaluation) для системы, где нет однозначно правильных ответов (ground truth)?
Проверка способности мыслить стратегически и развивать команду.
Какими будут ваши первые три приоритета в роли Founding ML Engineer в первые 90 дней работы?
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