Website:
huntingcube.ai
Job details:
Job Description
Key Responsibilities
- Language understanding and reasoning
- Build and fine-tune models for legal QnA and search — systems that can answer questions over case history, statutes, and filings accurately and with citations.
- Develop legal reasoning and agent capabilities: models that can navigate multi-step document workflows, identify relevant precedents, and surface what a judge needs.
- Own multilingual summarisation and translation for legal proceedings across 10+ Indian languages.
- Document intelligence and multimodal understanding
- Develop OCR and document parsing capabilities for scanned legal filings, orders, and exhibits — the messy, handwritten, low-quality documents that existing tools fail on.
- Extend language understanding into vision: document layout understanding, table extraction, and multimodal reasoning over legal exhibits and evidence.
- Build structured information extraction pipelines from unstructured legal text and documents.
- Evaluation methodology and benchmarking
- Design task sets and benchmarks for Indian legal NLP where none exist.
- Define metrics that capture what matters in a legal context — not just F1, but what a judge actually needs from a summary, a QnA answer, or an extracted clause.
- Conduct rigorous ablations and failure analyses that produce insight, not just numbers.
- Data curation and research dissemination
- Build training corpora from courtroom documents, judgements, and proceedings across languages and court tiers.
- Design annotation schemes for legal NLP and vision tasks that produce consistent, high-quality supervision at scale.
- Maintain reproducible, publication-ready experiment logs and actively submit to venues like ACL, EMNLP, or ICLR. This is original research — treat it as such.
Required Skills
['ML Research', 'Fine-Tuning', 'PyTorch']
Additional Information
- Fluency in PyTorch and HuggingFace Transformers.
- Hands-on experience fine-tuning large language models (SFT, LoRA, RLHF, or DPO).
- Experience designing evaluation methodology — not just running benchmarks, but deciding what to measure and why.
- Clear technical writing; comfortable documenting and communicating research.
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