Research Engineer II (Full Stack Engineer)
Research Engineer II (Full Stack Engineer)
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
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