Full-Time
Senior Legal Counsel
Regulatory and Data Privacy
JetBrains
1,001-5,000 employees
Develops IDEs and developer tools
No salary listed
Berlin, Germany + 1 more
More locations: Munich, Germany
Hybrid
Hybrid role with on-site days in Berlin, Munich, or Prague; remote work allowed up to 30 days per year from abroad.
 Legal & Compliance (2) |
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- At least 8 years of post-qualification legal experience focused primarily on data privacy, technology law, or digital regulatory compliance, including substantial experience as in-house counsel advising on privacy and data protection matters within an international technology company
- Advise cross-functional groups (including AI, security, HR, and engineering) on data privacy and regulatory compliance.
- Evaluate product features, global data governance, and cross-border transfer mechanisms (like DPAs and SCCs) for privacy-by-design compliance.
- Draft, review, and maintain internal and external data protection agreements, privacy policies, and terms of service.
- Map our data flows and technical operations to ensure they fit within a coherent global regulatory framework.
- Research international regulatory shifts and prepare clear, structured legal memos and comparative analyses.
- Build and improve legal templates, guidance, DSAR processes, and knowledge databases.
- Spot and assess legal risks related to new technology laws, technical product design, and data processing.
- Drive process improvements within the team to boost efficiency, incorporating AI and automation tools where appropriate
JetBrains builds a suite of developer tools, chiefly Integrated Development Environments (IDEs) for languages like Java, Python, JavaScript, .NET, and C/C++, plus related products for testing and project management. Their IDEs provide deep code understanding, smart editing, and refactoring to help programmers write and maintain software, and are sold via subscriptions (including an All Products Pack) with options for perpetual licenses and free Community Editions for some tools and non-commercial use. The company differentiates itself with a tightly integrated tool suite, language-agnostic support, strong refactoring capabilities, and a track record of popular products like IntelliJ IDEA and Kotlin, along with newer AI features and lightweight Fleet editor. JetBrains aims to help developers work more efficiently across multiple languages, ecosystems, and teams.
Company Size
1,001-5,000
Company Stage
N/A
Total Funding
N/A
Headquarters
Amsterdam, Netherlands
Founded
2000
Get referred to JetBrains
See people who can refer or advise you
Simplify's Take
What believers are saying
- JetBrains launched a free DeepLearning.AI hybrid-to-local AI coding course on August 13, 2026.
- RustRover's August 6, 2026 Solana plugin expands JetBrains into fast-growing blockchain tooling.
- JetBrains still shows 95 open roles in July 2026, signaling continued investment and growth.
What critics are saying
- CVE-2026-63077 on TeamCity had active exploitation reports by August 7, 2026.
- Unauthenticated TeamCity RCE threatens build artifacts, credentials, and downstream CI/CD trust.
- Repeated 2026 JetBrains vulnerabilities in IntelliJ, TeamCity, and YouTrack damage enterprise confidence.
What makes JetBrains unique
- JetBrains' IDEs pair deep code intelligence with Kotlin and language-specific workflows.
- Kotlin Benchmark on July 9, 2026 gives JetBrains credible AI-agent evaluation leadership.
- Fleet evolved into Air, extending JetBrains into agentic development environments.
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Benefits
Flexible Work Hours
Remote Work Options
Conference Attendance Budget
Professional Development Budget
Growth & Insights and Company News
6 month growth
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↑ 0%Hybrid and Local AI course at DeepLearning.AI. August 13, 2026 Open weight models are having a moment, driven by control, choice, and cost. Hybrid and local AI are now getting serious looks, so JetBrains teamed up with DeepLearning.AI on a free AI Coding Workflows: Hybrid to Local course that covers the ideas and options. The course is now available and uses PyCharm and its AI Chat. Here's a peek into the course. Claude Code: subagents and cheaper models. JetBrains start the course with, well, not-local. Instead, JetBrains use what you already know - Claude Code and its Anthropic models - to introduce some of the techniques and "levers" that help bring choice, control, and even cost reduction. (Yes, I wrote emdashes.) JetBrains did a previous course on Spec-Driven Development (SDD) so of course, JetBrains wanted to start there. Smaller models struggle with big, open-ended "vibe coding." Dividing and bounding the work keeps smaller models on track. Important note: this course's example app is really basic. You might say "that's too easy." But that's part of the takeaway: big brain models can do the upfront work, forming right-sized steps for smaller models. JetBrains then illustrate this division with a Claude Code subagent. The main chat prompt implements each roadmap phase in a fresh subagent, to better manage context. This then gives the payoff: a cheaper model for the implementer. Use a "big brain" (Opus) for main conversation thinking and a "little brain" (Haiku) for implementation. Each lesson finishes with metrics about the change in tokens, turns, cost, and estimated wall time. Which brings JetBrains to the main course goal: learning the ideas instead of the specifics, which change weekly. New agent, inference, and model. That covers the four levers: * Specs shaped for the model size * Specialist subagents to divide work * Cheaper models for the routine work * Collect metrics as evidence to guide thinking The course then introduces choice and control: * New agent: OpenCode * New inference router: OpenRouter * New model and inference host: DeepSeek (via OpenRouter) by moving to a new agent (OpenCode) using inference routing (OpenRouter) to inference hosting and models (DeepSeek) JetBrains first move to OpenCode, running in PyCharm. JetBrains wants its IDEs to be open platforms for agents and models. This makes the move from Claude Code to OpenCode straightforward: it's the same UI. JetBrains add OpenRouter (a paid step), connect it to OpenCode, and choose DeepSeek as a model. Next JetBrains repeat its sequence: all in one chat, then context isolation using a subagent. But this time, with a different agent and model. JetBrains finish by making a dedicated implementer subagent in Markdown. This gives quite a number of levers of control: in the frontmatter for mandatory controls, and in the subagent body for "persuasion" guidance. Most importantly, we have the implementer use the smaller DeepSeek v4 Flash model as the "little brain." Compared to the Claude Code version, the metrics were, unsurprisingly, a lot cheaper. Hybrid and local. Now for the main attraction: for routine development, can JetBrains do some - or even all - of the work locally? JetBrains start with a lesson on setting up local AI: LM Studio as the inference server and Gemma 4 12B as the local model, targeting a 32 GB laptop. JetBrains then configure the implementer subagent to use this local Gemma 4 model, promoting DeepSeek v4 Flash from last lesson's "little brain" up to "big brain." The results? Quite good, as it turns out. Then the big test: fully local, with Qwen 3.5 27B as the "big brain." The results: better than expected, showing that guardrails help. How did hybrid and local do? Both of these lessons finish with a review of the metrics. That's one of the big course takeaways: look at the evidence. You can see how small models struggle, and see the effect of helping them succeed. Hybrid and Local AI are heating up. Much thanks to DeepLearning.AI both for working with JetBrains again and for pushing to get this out fast. This topic is now red-hot in the news: Sovereign AI, privacy and security, and of course cost. The innovations are coming really fast and it is important to have a gentle introduction to the fundamentals. JetBrains'll do more updates here on Local AI for control, choice, and cost. Most of all, JetBrains at PyCharm believe in the human-in-the-loop. Stay tuned for more on this.
JetBrains course reveals Cloud to Local AI workflows. According to DeepLearningAI, a free course shows how to split subagents, use cheaper models, and run coding agents fully local across workflows. Analysis. The new short course AI Coding Workflows From Cloud to Local announced by DeepLearning.AI on August 12 2026 in partnership with JetBrains introduces developers to flexible AI coding agent setups that replace default model choices payment structures and data handling with deliberate decisions across cloud hybrid and fully local environments. Key takeaways. * Developers can optimize costs by routing routine tasks to cheaper models while reserving advanced models for complex coding work in hybrid setups. * Local model execution keeps proprietary code and data on the developers machine reducing risks associated with cloud data transmission. * Subagent workflows allow parallel task distribution across different model types improving efficiency in rebuilding applications from cloud to local configurations. Deep dive into AI coding agent customization. According to the DeepLearning.AI announcement the course teaches participants to rebuild the same application in three distinct setups. Cloud based agents rely on remote inference which offers high performance but incurs recurring fees and sends code outside the local environment. Hybrid approaches combine cloud resources for heavy lifting with local models for sensitive operations. Fully local setups run everything on the developers hardware using tools from JetBrains to manage inference. Subagent task splitting strategies. The curriculum covers dividing work across specialized subagents where one agent handles code generation another manages testing and a third oversees deployment. Cheaper models process straightforward refactoring while premium models address intricate algorithm design. This segmentation directly impacts development speed and budget control in professional software teams. Business impact and opportunities. Organizations adopting these workflows gain competitive advantages through reduced API costs and enhanced data privacy compliance. Implementation challenges include initial hardware requirements for local inference and model fine tuning but solutions involve JetBrains integrated development environments that streamline model switching. Monetization opportunities arise for training providers and tool vendors offering optimized local AI stacks. Key players such as JetBrains position themselves as enablers of hybrid AI development environments that appeal to enterprises concerned with intellectual property protection. Regulatory and ethical considerations. Local execution supports compliance with data residency regulations by ensuring sensitive code never leaves the machine. Ethical best practices emphasized in the course include transparent model selection to avoid bias in generated code and regular audits of subagent outputs for quality assurance. Future outlook. Industry shifts point toward widespread adoption of customizable AI coding agents as hardware improves and open source local models mature. Predictions indicate hybrid setups will dominate enterprise environments balancing performance with privacy. Competitive landscapes will favor platforms that simplify model orchestration while regulatory frameworks evolve to address AI generated code ownership. Frequently asked questions. What is the main benefit of moving from cloud to local AI coding setups? Local setups keep code and data on the developers machine while allowing cost control through selective use of cheaper models for routine tasks. How does the course address payment optimization? Participants learn to assign cheaper models to repetitive coding tasks and advanced models only when needed reducing overall expenses in cloud and hybrid configurations. Who teaches the AI Coding Workflows course? The course is taught by Paul Weveritt Developer Advocate at JetBrains in partnership with DeepLearning.AI. What setups are covered in the short course? The curriculum covers cloud hybrid and fully local environments for rebuilding applications with subagents and model customization. DeepLearning.AI. Blockchain News is an education technology company with the mission to grow and connect the global AI community.
JetBrains fixes IntelliJ IDEA bugs. Aug 11, 2026 1 min Minor update to IntelliJ IDEA 2026.2 fixes several bugs impacting Markdown shell scripts, undo actions, terminal sessions, and Java code formatting. JetBrains has released a minor update to IntelliJ IDEA 2026.2, the latest edition of its IDE for Java and Kotlin. The update fixes several bugs impacting Markdown shell scripts, undo actions, terminal sessions, Java code formatting, and more. IntelliJ IDEA 2026.2.1 was announced August 10. The most notable updates include the following: * Markdown shell scripts now execute in the correct order. * Undo now works correctly after applying Optimize imports on the fly. * Dragging a terminal tab after using the Move to Editor action no longer restarts the terminal session. * The IDE no longer throws exceptions caused by the new Resolve Conflicts mechanism for Mercurial projects. * Java code formatting now correctly respects the Smart tabs setting. Updating to IntelliJ IDEA 2026.2.1 can be done from inside the IDE, using the Toolbox App, or using snaps in the case of Ubuntu users. The update also can be downloaded from the JetBrains website. Editor at Large Paul Krill is editor at large at InfoWorld. Paul has been covering computer technology as a news and feature reporter for more than 35 years, including 30 years at InfoWorld. He has specialized in coverage of software development tools and technologies since the 1990s, and he continues to lead InfoWorld's news coverage of software development platforms including Java and .NET and programming languages including JavaScript, TypeScript, PHP, Python, Ruby, Rust, and Go. Long trusted as a reporter who prioritizes accuracy, integrity, and the best interests of readers, Paul is sought out by technology companies and industry organizations who want to reach InfoWorld's audience of software developers and other information technology professionals. Paul has won a "Best Technology News Coverage" award from IDG.
JetBrains releases RustRover plugin for Solana Anchor smart contract development. JetBrains released a Solana plugin for RustRover on August 6, giving JetBrains released the Solana (Experimental) plugin for its RustRover IDE on August 6, giving Solana SOL$75.97+3.0% Anchor smart contract developers a built-in transaction and account explorer, ending the routine of tabbing out to an external block explorer during development. Keep up to date with the Solana eco The plugin is available on JetBrains Marketplace and was developed by Alex (@alexlst1) from the JetBrains team. The official RustRover account announced the release on August 6 at 13:00 UTC. Account and transaction explorer replaces the external browser tab. Solana smart contracts are written in Rust, which makes RustRover a natural fit for Anchor development. Until now, inspecting what a contract actually did after a deploy or test meant leaving the editor to query an external explorer such as Solana Explorer or SolScan. The plugin closes that gap. Developers can track transactions and inspect the specific accounts involved, including their state and data, from inside an active RustRover session. Community responses to the announcement singled out the embedded account explorer as the main practical gain: one developer described having account state inspection built into the editor as a significant quality-of-life improvement for anyone who has spent time switching between an IDE and a block explorer to verify transaction results. Anchor deploys, toolchain setup, and local validator support. The plugin's explorer is the headline feature, but the toolchain integration goes further. Installation runs in a single step: the plugin configures all required Solana tools and dependencies inside RustRover automatically, replacing a manual CLI setup. Anchor deploys and smart contract tests run through standard IDE run configurations, keeping the full workflow inside the editor. Local development is also covered. The plugin works with local test validators, local wallets, and Surfpool environments. For developers who need to step through compiled BPF/sBPF program execution, the plugin works alongside the community-driven Solana Debugger project, which is building a graphical debugger for on-chain Solana programs. The plugin carries an "Experimental" label on JetBrains Marketplace. RustRover is JetBrains' standalone Rust IDE, separate from the Rust plugin available for IntelliJ IDEA. Solana Compass is an independent Solana analytics and staking platform, operating a validator on Solana mainnet since September 2021. Its network statistics and...
Kodee's Kotlin roundup: birthday wishes, Shipaton 2026, and the new Kotlin AI benchmark. August 4, 2026 Hi everyone! July gave me plenty to celebrate: Kotlin turned 15, got its first public benchmark for AI coding agents, became available in BlueJ, and shipped its 2.4.10 release. Developers can also demonstrate their skills at RevenueCat Shipaton 2026 by building a Kotlin Multiplatform app and competing for the Ship Kotlin Everywhere Award. Meanwhile, X has rebuilt its Android app to be 100% Kotlin, marking another milestone for the language. Here's what stood out to me most over the past month: Kodee-approved spotlight. Kotlin turned 15: Leave a birthday wish. This one is close to my heart - Kotlin recently turned 15! To mark the milestone, JetBrains is inviting the whole community to celebrate. You can create a birthday postcard, upload a photo to party with me, and share a wish or a prediction for Kotlin's next chapter. Now is the perfect moment to look back at how far JetBrains has come - and to look ahead together. Ship Kotlin Everywhere Award at RevenueCat Shipaton 2026. Already know Kotlin? RevenueCat Shipaton 2026 is the perfect opportunity to turn your Kotlin skills into a new app. From August 1 to September 30, build and ship for Android, iOS, desktop, or web and compete for the Ship Kotlin Everywhere Award. Use the KMP starter guide to get your project up and running. To earn bonus points, you can help others by sharing your development journey. Shipping is impressive, but helping someone else is even better. The Kotlin Benchmark for AI coding agents. Kotlin now has its very own public benchmark for AI coding agents. It ranks agents on 105 real engineering tasks drawn from open-source Kotlin repositories, so you can compare them by resolution rate, token cost, and latency - and dig into the methodology behind the numbers. As AI becomes a bigger part of coding in Kotlin, I love that JetBrains finally have an open, Kotlin-specific way to measure what actually works. Kotlin 2.4.10 and Kotlin 2.4.20-Beta2. July brought the Kotlin 2.4.10 bug fix release, alongside Kotlin Kotlin 2.4.20-Beta2 with coroutine stack trace recovery, faster klib compilation, expanded Swift export, and an experimental compiler native image. Try the Beta version and share your feedback while the release is still taking shape. Kotlin comes to BlueJ. Kotlin support is now available in BlueJ 6.0 thanks to a collaboration between JetBrains and the BlueJ team at King's College London. Students can create, edit, compile, and run Kotlin code, inspect class diagrams, and interact with objects through BlueJ's familiar workflow. For educators, a new onboarding guide and ready-to-use materials make it easier to include Kotlin's concise syntax and null safety in introductory object-oriented programming courses. A conversation with the Golden Kodee winners. The first Golden Kodee Community Awards recognized Matheus Leandro Ferreira, Jaewoong Eum, Nicole Terc, Eeva-Jonna Panula, and Yinlong Liu for their contributions to education, online presence, creativity, positive societal impact, and in-person community building. Read their interviews and watch the video to discover practical advice on learning in public, starting small, and helping the community grow. Showcase your JetBrains IDE experience on LinkedIn. The free LinkedIn Connected Apps plugin lets you connect a supported JetBrains IDE to your LinkedIn profile. Once connected, a profile statement highlights how you use your IDE in practice, based on usage data that stays on your machine. As your development habits evolve, the statement updates automatically to reflect your experience. It is designed to showcase practical tool usage - not to rank developers or replace formal certification. How I came to life. I didn't always look like this! My journey began with a simple robot-inspired concept. Then, with the help of research, creativity, and community feedback, I evolved into the Kodee you know and love today. Check out my origin story (including how I got my name!). KMP library spotlight: Ktor, Koin, and Kermit. Finding the right KMP library shouldn't slow down your project. klibs.io brings together more than 4,100 Kotlin Multiplatform libraries, with filters for developers and AI integrations that give coding agents access to accurate, up-to-date library data. This month, JetBrains is spotlighting Ktor, Koin, and Kermit - a practical trio for networking, dependency injection, and logging. X rebuilds its Android app in 100% Kotlin. Built from scratch, X's Android app is now written entirely in Kotlin. X Chat also uses Kotlin Multiplatform across Android, iOS, and web for end-to-end encryption, storage, sync, and business logic. It's exciting to see Kotlin and Kotlin Multiplatform used at this scale.