Top Tech Jobs & Startup Jobs in London

Own the strategy, roadmap, metrics, and adoption of JetBrains Context, a managed retrieval layer for AI coding agents. Partner with engineering, ML, platform, IDE, marketing, sales, and customer success teams to improve indexing, retrieval quality, integrations, reliability, cost, and latency. Define product direction, evaluate competitors, establish quality metrics, communicate with customers and developer communities, and drive enterprise adoption.

Create and maintain foundational Kotlin libraries used by hundreds of thousands of developers. Contribute to Kotlin API design, standard tools, specifications, and language features. The role requires deep computer science expertise, careful technical reasoning, design documentation, performance engineering, and API design, with potential work involving JVM internals, Unicode, time zones, and language interoperability.

Lead hands-on development of AI-powered workflows, coding-agent integrations, and C++ development capabilities for CLion. Design agent architectures, tool integrations, benchmarks, and evaluation frameworks while prototyping debugging, code understanding, refactoring, and navigation workflows. Collaborate with JetBrains AI, ML, product, QA, and C++ teams to shape technical direction, roadmap priorities, and AI strategy for developer tools.

Own the full software development lifecycle for JetBrains identity and access services. Develop and operate reliable Kotlin-based services, with some Go, supporting authentication, authorization, organizations, memberships, groups, and workspaces. Design new platform features, improve existing production systems, review code, manage integrations, and address JVM and PostgreSQL performance in distributed, multi-tenant SaaS environments.

Lead design and implementation of ML/LLM systems for ingestion, knowledge extraction, retrieval, and reasoning. Build datasets, metrics, pipelines, agentic retrieval and RAG solutions. Establish MLOps, orchestration, observability, and experiment tracking. Collaborate on research agenda, shape ML architecture, and hire and grow the ML team.

Design, build, and lead development of AppGlass features; develop AI-first tooling and automation; implement enterprise integrations and deployments across Kubernetes and cloud; and improve reliability, testing, performance, and security for production use.

Develop and maintain SFT and RL post-training pipelines for multi-step coding agents, train and adapt LLMs for agent workflows, build evaluation and simulation environments, design metrics and evaluation frameworks, analyze results to improve models and datasets, and collaborate with research, product, and infra teams to ship models into JetBrains IDEs.

Design, train, and deploy large language models from scratch; convert business requirements into technical specs; collect and process pretraining/fine-tuning datasets; run distributed training on large GPU clusters; support and improve production ML subsystems and tooling.

Define and lead the VCS product strategy to evolve version control and code-change workflows into a standalone ecosystem across JetBrains IDEs. Identify workflow gaps, set roadmaps, explore cross-IDE and AI-native review opportunities, align stakeholders, and work with engineering and QA to deliver scalable, platform-friendly solutions while representing the product externally.

Lead and grow a ~50-person ML organization, prioritize high-impact AI projects, set ML technical direction and standards, ensure delivery and measurable business value, and collaborate across products to translate AI research into product innovations.