AI Researcher - Post-Training

Position description

At Sonar, we are seeking an ambitious senior researcher to join our cross-disciplinary team, innovating and developing the next generation of solutions to build enterprise-grade coding agents and models. You will harness Sonar’s deep experience in static analysis, and combine it with your experience and leading techniques in large language model post-training. If you are interested in being hands-on with state-of-the-art research, building practical solutions that deliver high-impact for customers, and working within a team of innovative researchers and engineers, this role is for you.

Outcome Driven Development

Work in a team developing and implementing advanced products that enable customers to post-train models to power their agentic coding practices. These agents need to generate high-quality code that meets their enterprise standards and software development best practices.

Translate Prototypes to Products

Collaborate closely with researchers, research engineers, MLOps and engineers within the team to design hypotheses and experiments, iterate proofs-of-concept quickly and develop successful prototypes into cutting-edge products.

Subject Matter Expert

You will contribute and discuss ideas within our cross-disciplinary team, driving towards the next generation of coding model post-training for enterprises.

Spearhead Research & Innovation

Stay up-to-date with the latest LLM and agentic developments; you are driven by learning and teaching others. You will need to explain complex technical details and concepts to both technical and non-technical audiences.

The ideal candidate will have

• An advanced academic background (Master’s or PhD) in Computer Science, Machine Learning, or a related quantitative field.
• 4+ years industry experience in machine learning, with a solid understanding of modern software engineering practices and tools.
• Fluency with Python including core ML frameworks, experience with Rust or any of SonarQube’s flagship languages (C#, C++, JS/TS, Java) is a plus.
• Expertise in post-training of large language models, including:
  • Policy optimization algorithms (e.g. GRPO, PPO)
  • Verifier-based RL frameworks (RLVR)
  • Supervised fine-tuning (SFT)
  • Data-centric AI methods, including synthetic data and curation
  • Parameter-efficient fine-tuning (PEFT)
  • Preference and Safety Alignment
• Experience of driving research projects, delivering valuable findings and prototypes, and then converting them into products.
• Excellent communication skills in English and a talent for explaining complex scientific topics clearly and concisely.