AI Innovation Lead

2 days ago

Kannur, Kerala, India beBeeResearch Full time ₹ 1,50,00,000 - ₹ 2,00,00,000
AI Research Scientist Position

Job Overview:

This is a role that combines deep publishable AI research with hands-on system development. As an AI Research Scientist, you will conduct research, build, validate, and ship groundbreaking AI capabilities end-to-end.

You will be a bridge between science and software, innovating on large language models, prompt systems, evaluation frameworks, and algorithm pipelines—and bringing them to production-grade systems that power real users. Expect to author experimental prototypes rich enough for academic-quality write-ups, then productize those same ideas into robust, reliable software.

Key Responsibilities:

  • Tackle experiments in LLM training, LLM agent architectures, fine-tuning, RL, and new evaluation methodologies.
  • Build code-first prototypes to test hypotheses and iterate rapidly—refactor them into production systems when experiments succeed.
  • Develop and benchmark improvements in LLM architecture, fine-tuning workflows, and generation control.
  • Optimize for performance, resource efficiency, and interpretability.
  • Own infrastructure: from data pipelines and experiment sandbox to model serving, batch/online inference, logging, and CI/CD.
  • Treat experiments as software: automated tests, clear versioning, reproducibility, and documentation.
  • Create frameworks to automate scientific cycles: data generation, metric computation, model comparison dashboards.
  • Enable internal teams to run reproducible experiments at scale.
  • Write up interesting findings—internal memos, tech blogs, or scholarship-worthy results.
  • Participate in code and science critique sessions with peers.

Requirements:

  • Master's or Ph.D. in CS, ML, or related (or equivalent experience)
  • Deep familiarity with LLMs, fine-tuning, reinforcement learning
  • Expert in Python + frameworks like PyTorch or JAX; experience with Hugging Face or deployed LLM APIs
  • Strong software engineering intuition: modular design, CI/CD, containerization, automated testing
  • Demonstrated track record in both research and production settings (e.g., prototypes published internally or externally)

Success Metrics:

  • Prototypes demonstrating clear scientific advances (e.g., better evaluation metrics or prompt robustness). Papers submitted or draft-ready internal research docs.
  • Experimental pipelines and prototypes are production-ready within sprint cycles; clean codebases, smooth deployments, few bugs.
  • R&D code drives real product features—high user adoption, improved reliability or performance.

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