AI Research Scientist/Engineer

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 Advanced AI Models : Design, prototype, and validate novel ML models that identify and resolve complex bugs, vulnerabilities, and code smells, going beyond the capabilities of traditional static analysis.

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 AI-powered 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 AI 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

Strong industry experience in AI and a solid understanding of modern software engineering practices and tools, with minimum 2 years experience in industry.

Exceptional programming skills in Python (other programming languages such as Java is a strong plus), and the ability to write clean, well-documented code for product integration.

Practical experience working with modern AI/ML frameworks, LLMs, and integrating third-party or open-source AI tools into functional prototypes.

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

Experience with modern LLM architectures and techniques, such as:

Fine-tuning strategies (e.g., LoRA, QLoRA) and advanced prompt engineering.

Building and optimizing Retrieval-Augmented Generation (RAG) pipelines.

Working with vector databases and semantic search.

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

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

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.