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Salary
$100k – $178k per year
Location
In office (Bochum)
Seniority
Junior · 2+ years exp
Employment
Full-Time

Confirmed on the employer's own hiring board on Oct 28, 2026. First seen by Alion on Sep 8, 2025. SonarSource scores A on the Alion truth index.

Overview
Company
Impact
Profile match
SonarSource is a leading provider of continuous code quality and security solutions designed to help developers write clean, reliable software. Through its core product portfolio - including SonarQube, SonarCloud, and SonarLint - the company automatically detects bugs, security vulnerabilities, and code maintainability issues across more than 30 programming languages. By integrating directly into IDEs and CI/CD development pipelines, its platform provides real-time feedback that prevents technical debt and strengthens software security before code reaches production.

Position description

At Sonar, we are seeking an innovative Research Engineer to join our Data & Agentic team and pioneer the next generation of our code analysis and verification engines. You will be at the forefront of applying cutting-edge AI, advanced agentic workflow and static analysis techniques to complex source code. Your work will directly shape our core products-ensuring both AI-generated and developer-written code meets rigorous enterprise quality and security standards. If you are driven to turn state-of-the-art research into practical, high-impact solutions, this is the role for you.

What you will do

  • Create & Innovate: Actively participate in the conceptualization phase. Brainstorm novel ways to improve our products and provide immediate, practical feedback to steer exploratory research toward paths that are both impactful and engineering-feasible.
  • Develop Agentic Workflows: Design, prototype, and validate intelligent agentic systems and automated workflows that facilitate writing clean code, identifying root causes of system issues, and remediating complex bugs and vulnerabilities.
  • Develop Pragmatic Prototypes: Build hands-on prototypes that prove the viability and scalability of research concepts. Proactively identify engineering bottlenecks or scalability limits and propose effective mitigations early in the research phase.
  • Evaluate & Benchmark: Drive the rigorous evaluation of new product features. Construct high-quality datasets, define relevant success metrics, and implement comprehensive benchmarks leveraging Sonar’s existing IP alongside third-party and open-source AI tools.
  • Engineer Data Pipelines: Build and manage robust data pipelines to gather, process, and version massive code-centric datasets required for training and evaluating specialized models at scale.
  • Translate Prototypes to Products: Collaborate closely with engineering and product teams to integrate successful prototypes into Sonar's cutting-edge products, ensuring they meet the needs of our global user base.
  • Communicate and Evangelize: Clearly articulate and document complex technical concepts and research findings to both technical and non-technical stakeholders

Experience and qualifications

    Must have:

  • Advanced academic background in Computer Science, Machine Learning, or a related quantitative field.

  • Strong software engineering experience (minimum 2+ years in industry) with proficiency in Python and major modern programming languages.

  • Practical experience with modern agentic frameworks, software automation tools, and LLM architectures.

  • Experience of driving research projects from initial ideation to a demonstrable prototype with a high degree of autonomy.

  • Hands-on experience integrating third-party tools, program analysis frameworks, or open-source libraries into functional prototypes.

  • Proven experience in data handling: constructing high-quality datasets, defining objective success metrics, and building robust benchmarks for new solutions.

  • The ability to fast context-switch and maintain momentum across multiple concurrent, fast-moving research topics.

  • Excellent communication skills in English, with a talent for translating complex research findings into clear, actionable artifacts for product engineering squads.

  • Plus:

  • Background in static analysis: abstract syntax trees (ASTs), code security, bug detection, etc..

  • Familiarity with LLM techniques such as model fine-tuning, semantic search engines, etc.

  • Experience with large-scale data processing frameworks and cloud infrastructure (e.g. AWS).

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