Staff Machine Learning Engineer

About the Company

Adalat AI is a legal-tech nonprofit revolutionizing the Indian judicial system through cutting-edge AI. We are building the country's first end-to-end justice tech stack — from speech-to-text transcription in courtrooms to intelligent legal assistants — to eliminate judicial delays and make justice more accessible.

We currently operate across 10 Indian states and are backed by some of the world's largest foundations. Our AI systems, including state-of-the-art ASR models for Indian languages, are deployed in multiple high courts — most recently at the Delhi High Court.

Founded by technologists and legal experts from Harvard, Oxford, MIT, and IIIT-Hyderabad, we're earning recognition alongside India's most important digital public infrastructure (like UPI and Aadhaar) and are featured in major outlets like Fast Company, Indian Express, and Times of India.

Role Overview

As a Staff Machine Learning Engineer, you will play a central role in driving the core ML research and engineering at Adalat AI. You will work across the ML lifecycle — from data design to training and deployment — and serve as a technical mentor to a growing team of ML engineers and researchers.

This role is ideal for someone with deep experience in training large models, especially in low-resource settings, and who thrives on ownership, autonomy, and real-world impact. You will help build systems that touch millions of lives by improving the functioning of the world's largest court system.

Key Responsibilities

Research & Systems Building

Design, train, and deploy models for speech recognition, summarisation, legal Q\&A, retrieval, and translation.
Build scalable ML systems using LLMs, transformers, and custom architectures.
Train large models from scratch (or from base checkpoints) when needed, including curating and managing data pipelines.
Contribute to original research; submit to top-tier conferences (A\*STAR/CORE-ranked such as ACL, NeurIPS, ICML, EMNLP, or similar).

Technical Leadership

Mentor junior engineers and researchers on ML design, experimentation, and deployment practices.
Lead technical design discussions and decisions on modeling strategies, data pipelines, and infrastructure.
Set up best practices for reproducibility, evaluation, and documentation across ML projects.

Cross-functional Collaboration

Translate product and legal requirements into technical architecture and model specs.
Work with linguists, annotation teams, and legal domain experts to define data needs and ensure model reliability.
Collaborate with backend engineers to ensure seamless integration of models into production systems.

About You

Research Expertise: Strong background in AI research with a passion for applying advanced techniques to solve real-world problems. Experience handling the annotation team is a bonus.
Leadership Ambition: Ready to step into a leadership role while maintaining hands-on involvement in research and development.
Problem Solver: Ability to tackle complex technical challenges and develop innovative solutions.
Collaborative Mindset: Excellent communication skills, humble attitude and ability to work cross-functionally with product and engineering teams.
Startup Experience: Thrives in dynamic, fast-paced environments, preferably with experience in early-stage startups.
LLM Expertise: Proven track record of building and shipping successful applications powered by Large Language Models.
Customer-Centric Approach: Strong commitment to understanding and addressing customer needs through AI-driven solutions.

Qualifications

Ideal Profile

PhD in ML, NLP, Speech, or a related field OR equivalent experience working on cutting-edge ML projects at scale.
Experience publishing in top-tier A\*STAR-ranked AI/ML conferences (e.g., NeurIPS, ACL, EMNLP, ICML, CVPR, ICLR).
Strong track record of building and deploying production-grade ML systems, ideally in low-resource or domain-specific environments.
Proven experience training LLMs or ASR models from scratch, including building custom datasets and pipelines.
Familiarity with ML system optimisation, including inference serving, model quantisation, and latency reduction.

Bonus: experience working in civic tech, public infrastructure, or legal-tech is highly appreciated.

You Might Thrive Here If You Are…

A hands-on builder and researcher, not afraid of messy data, ambiguous specs, or field deployments.
A natural mentor, who enjoys helping others level up while maintaining high technical standards.
Excited about justice tech and the chance to build systems that improve governance at population scale.
Comfortable moving between experimentation and shipping, and between deep work and scrappy MVPs.

Nice to Have

Experience with annotation team workflows and building training datasets in-house.
Experience with retrieval-augmented generation (RAG), fine-tuning strategies, or few-shot learning.
Familiarity with tools like Hugging Face Transformers, Weights & Biases, Ray, Triton, or ONNX.
Background in legal, civic, or public policy work.