ML Research Engineer - Legal Reasoning
Remote
Development
About the Company
Adalat AI is building an end-to-end justice tech stack that automates manual and clerical pain points in courtrooms, giving judges back time to focus on what matters most: decision-making and delivering justice. Our solutions - from AI-powered transcription in Indian languages to case-flow management and document navigation - are now deployed across 9 states, covering nearly 20% of India’s judiciary. Backed by leading technology companies and funders, and incubated at MIT and Oxford, Adalat AI is working to eliminate judicial delays and expand access to timely justice. Founded by a team with backgrounds in law, technology, and economics from Harvard, Oxford, MIT, and IIIT Hyderabad, we are scaling rapidly across India and the Global South.
Role Overview
The Justice Lab & Tax-Litigation Co-Pilot
You’ll 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
Data & Evaluation
Curate, label, and version corpora spanning four court tiers.
Create task sets for prediction, entailment, and explanation.
Modeling & Experimentation
Fine-tune / distill LLMs with RL-, DPO-, or SFT-style feedback.
Explore long-context and retrieval strategies (LoRA, RAG, chunking).
Legal-Reasoning Research
Model precedential hierarchies, detect conflicts, and generate citation-grounded chains of thought.
Collaboration
Sync daily on design and code quality.
Present findings to Professors Acemoglu, Kang, and policy advisors.
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, let’s 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.
Benefits and Perks
WFH with flexible work hours.
Unlimited PTO
Autonomy and Ownership
Learning & Development resources
Smart, Humble and Friendly peers
Generous vacation
Maternity and Paternity leaves
Contacts within the Harvard / MIT/ Oxford ecosystem
Join Our Team
To apply, please send your resume and a cover letter with the subject line: "ML Research Engineer - Legal Reasoning".