Research Engineer II (Full Stack Engineer)

Location: NTU Main Campus, Singapore

Time Type: Full time

Job Description

Implicant Pte. Ltd. is a Singapore deep-tech spin-off from NTU SPMS. We build machine learning models that are interpretable by design and run on encrypted data using fully homomorphic encryption (FHE). We are building a managed cloud platform for FHE-protected interpretable model training and inference, targeting financial services, defense, healthcare, and clinical analytics.

Key Responsibilities:

• Build and ship features across the Implicant platform end-to-end, from database migration through API to frontend, with no handoffs.

• Extend the training and inference platform (data ingestion, training jobs, Pareto frontier analysis, rule extraction, distillation pipelines).

• Maintain and grow the customer-facing dashboard (inference page, model catalogue, billing).

• Co-build the external API and Python SDK alongside the current full-stack lead.

• Implement the FHE bootstrap key onboarding flow for clients running encrypted inference.

• Wire Stripe billing and metering into the platform.

• Own backend workflows for training jobs and asynchronous compute (FastAPI with Temporal-style orchestration).

• Contribute to platform security and reliability (row-level security, authentication, audit logging).

Job Requirements:

• Bachelor's degree in Computer Science, Software Engineering, or a related field.

• 4 years of relevant experience

• Proven experience as full-stack engineer

• Effective communication and interpersonal skills to collaborate with other engineers.

• At least 3 years of full-stack experience shipping customer-facing software end-to-end.

• Strong working knowledge of Python with FastAPI (or similar) and TypeScript with a modern frontend framework (Svelte preferred; React or Vue acceptable).

• Production experience with PostgreSQL, including row-level security.

• Good understanding of Machine Learning (architecture principles and ML librairies)

• ML-literate: comfortable reading scikit-learn and PyTorch code and working with tabular machine learning workflows.

• Comfortable in a small team with direct ownership and minimal process overhead.

• Based in Singapore or willing to relocate.

We regret that only shortlisted candidates will be notified.

Hiring Institution: NTU