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.