Job Openings ML Research Engineer - Legal Reasoning

About the job ML Research Engineer - Legal Reasoning

Role Overview

The Justice Lab & Tax-Litigation Co-Pilot

Youll join The Justice Lab—our skunk-works unit focused on ideas six-to-twelve months ahead of production.

Flagship project: a Tax-Litigation Co-Pilot that ingests full judgments, statutes, and filings, then produces defensible predictions and transparent explanations.

  • Project Coordinator: Arghya Bhattacharya (CTO, Adalat AI)
  • Project Oversight: Prof. Daron Acemoglu (Nobel Laureate, MIT) & Prof. Daniel Kang (UIUC)

Role in a Nutshell

As a Research Engineer—Legal Reasoning you will turn cutting-edge ideas into artifacts that ship:

  • Frame the problem: formalize legal reasoning for outcome prediction.
  • Design experiments: benchmark LLMs on labeled tax-law and civil-procedure tasks.
  • Prototype systems: retrieval-augmented generation, evidence tracing, causal inference—pipelines that think like lawyers.
  • Build eval suites: factual consistency, citation faithfulness, policy impact (e.g., case-load reduction).
  • Ship hand-offables: lightweight services or notebooks that engineers can harden.
  • Publish: co-author internal memos and external papers with academic partners.

Key Responsibilities

  1. Data & Evaluation

    • Curate, label, and version corpora spanning four court tiers.
    • Create task sets for prediction, entailment, and explanation.
  2. Modeling & Experimentation

    • Fine-tune / distill LLMs with RL-, DPO-, or SFT-style feedback.
    • Explore long-context and retrieval strategies (LoRA, RAG, chunking).
  3. Legal-Reasoning Research

    • Model precedential hierarchies, detect conflicts, and generate citation-grounded chains of thought.
  4. Collaboration

    • Sync daily on design and code quality.
    • Present findings to Professors Acemoglu, Kang, and policy advisors.
  5. Documentation & Dissemination

    • Maintain reproducible logs, polished reports, and publish-ready code.

Qualifications

Must-Have

Nice-to-Have

2 + years NLP/ML research (industry or grad school)

Prior work on legal or policy datasets

Fluency in PyTorch/JAX & modern LLM fine-tuning stacks

Publications at ACL, ICML, NeurIPS, etc.

Skill in large-corpus wrangling & eval pipeline building

Causal-inference or decision-theoretic ML

Clear, concise technical writing & comms

Familiarity with Indian tax or civil-procedure law

No one ticks every box—if the mission resonates, lets talk.



What You Will Achieve in a Year

A prototype that classifies appeal merit with 75 % F1 on held-out High-Court cases.

  • An evaluation methodology poised to become the standard for legal-AI outcome prediction in the Global South.
  • A first-author or co-author paper submission (e.g., NeurIPS L4DC, ICML LawML).
  • Pilot deployment inside real-world Tax Offices.