Senior AI Research

4 weeks ago

India IdeaCode Full time
TL;DR: This role blends deep, publishable AI research with hands-on system development, ensuring you can conduct research, build, validate, and ship groundbreaking AI capabilities end‑to‑end.

You're a bridge between science and software. You'll innovate on LLMs, prompt systems, evaluation frameworks, and algorithm pipelines—and bring 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.

What You'll Own

Research Prototyping

- 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.

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.

Who You Are (Qualifications)

Master's or Ph.D. in CS, ML, or related (or equivalent experience)

- Deep familiarity with LLMs, fine‑tuning, reinforcment 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)
- 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 docs.

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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