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AI Research Engineer - Post-Training

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Ghost-risk verdict

Some ghost-posting signals

  • open for 65 days (60–89 days is elevated risk)
  • 3475 open roles at this company in 30 days (mass-hiring blitz)
  • no salary disclosed (correlates with ghost postings)

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About the role

What you will do

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.

Experience and qualifications

The ideal candidate will have

An advanced academic background (Master’s or PhD) in Computer Science, Machine Learning, or a related quantitative field.

Strong 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 AI models, with techniques such as:

Reinforcement learning from verifiable rewards

GRPO and related techniques

Offline or semi-online reinforcement learning

Parameter efficient fine-tuning

Supervised fine-tuning

Safety Alignment

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

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.

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