AI Research Innovator

1 day ago

Gandhinagar, Gujarat, India beBeeResearch Full time US$ 1,60,000 - US$ 2,00,000
Unlock Innovative AI Solutions">

About the Role

This is a unique opportunity to combine in-depth Artificial Intelligence (AI) research with hands-on system development.

You will be responsible for conducting experiments, building, validating, and deploying groundbreaking AI capabilities from start to finish.

The ideal candidate will bridge the gap 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.

  • Research Prototyping
  • Tackle complex experiments in LLM training, agent architectures, fine-tuning, reinforcement learning, and new evaluation methodologies.
  • Build code-first prototypes to test hypotheses and iterate rapidly—refactor them into production systems when experiments succeed.
  • Model & Architecture Innovation
  • Develop 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
  • Write up interesting findings—internal memos, tech blogs, or scholarship-worthy results.
  • Participate in code and science critique sessions with peers.

Requirements

Masters or PhD in CS, ML, or related field (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.
  • Bonus: familiarity with Kubernetes, JAX compiler stacks, Hugging Face Accelerate, or MLOps toolchains.

Success Metrics

Research innovation:

  • Prototypes demonstrating clear scientific advances.
  • Papers submitted or draft-ready internal research docs.

Engineering quality and velocity:

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

System impact and use:

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

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