Senior AI Research Scientist

2 days ago

Shimoga, Karnataka, India beBeeInnovation Full time US$ 1,80,000 - US$ 2,50,000
Job Title: Senior AI Research Scientist

This role combines cutting-edge research with hands-on software development to create innovative AI capabilities.

You will be a bridge between science and software, driving innovation in LLMs, prompt systems, evaluation frameworks, and algorithm pipelines. Your goal is to bring these ideas to production-grade systems that power real users.


Responsibilities:

Research & Development

  • Develop and experiment with new LLM training methods, agent architectures, fine-tuning, reinforcement learning, and evaluation methodologies.
  • Build prototypes to test hypotheses and iterate rapidly—refactor them into production systems when experiments succeed.

Model & Architecture Innovation

  • Design and benchmark improvements in LLM architecture, fine-tuning workflows, and generation control.
  • Optimize for performance, resource efficiency, and interpretability.

Production Engineering

  • 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.

Scientific Experiment Tooling

  • Create frameworks to automate scientific cycles: data generation, metric computation, model comparison dashboards.
  • Enable internal teams to run reproducible experiments at scale.

Knowledge Sharing & Publication

  • Document 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 field (or equivalent experience)

  • Deep familiarity with LLMs, fine-tuning, reinforcement learning.
  • Expertise 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).
  • Bonus: familiarity with Kubernetes, JAX compiler stacks (e.g., Flax, Alpa), Hugging Face Accelerate, or MLOps toolchains.

How You'll Measure Success:

Research Innovation

Prototypes demonstrating clear scientific advances (e.g., better evaluation metrics or prompt robustness). Papers submitted or draft-ready internal research documents.

Engineering Quality & Velocity

Experimental pipelines and prototypes are production-ready within sprint cycles; clean codebases, smooth deployments, few bugs.

System Impact & Use

R&D code drives real product features—high user adoption, improved reliability or performance.

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