Machine Learning Engineer

Company Description

MedMosaic collaborates with biopharmaceutical companies, life sciences organizations, and healthcare startups to deliver cutting-edge clinical development and medical affairs services across Asia. Combining regional expertise with innovative technology and AI-driven solutions, MedMosaic aims to enhance regulatory compliance, operational efficiency, and patient access to treatments. Committed to empowering patients and supporting healthcare professionals, the company strives to provide clear medical information and expand access to clinical trials for new therapies. Headquartered in Singapore with offices in Malaysia, MedMosaic fosters clarity, compliance, and compassion in healthcare delivery.

Key Responsibilities include (but will not be limited to)

• End-to-End Product Ownership You'll work directly with stakeholders to translate regulatory and business requirements — jurisdiction-specific rules, standards, thresholds — into production-ready features and deployable models. You own the problem from whiteboard to deployment.
• NLP & Model Orchestration Design, fine-tune, and maintain advanced text classification pipelines (e.g., ELECTRA, RoBERTa). You'll continuously evaluate model performance and drive improvements.
• RAG & Knowledge Integration Build and enhance Retrieval-Augmented Generation systems backed by vector databases.
• Generative AI Pipelines Develop and scale generative models.
• Agentic Systems Engineer agentic workflows and Q&A systems that allow users to interact with reports.
• Visual & Data Integrity Auditing Apply Computer Vision and OCR techniques to audit graphs, tables, and visual data — verifying axis consistency, scale uniformity, and alignment with sources.
• Automated Reporting Architect systems that generate structured reports that's actionable for both technical and non-technical stakeholders.
• MLOps & Model Governance Own the full model lifecycle: manage retraining cycles, version control, and performance monitoring. Maintain a clean, auditable Git history that scales as the team grows.

Requirements & Qualifications

• Education Bachelor's or Master's in Data Science, Computer Science, Statistics, or a related field. Equivalent experience with a strong portfolio will be considered.
• Experience 5 years of professional experience in applied ML or data science. Prior experience in a startup or small high-impact team is a strong plus — we're looking for people who are comfortable with ambiguity and ownership in equal measure.
• Core Technical Stack Hands-on production experience with PyTorch or TensorFlow, Hugging Face Transformers, and SQL/NoSQL databases.
• LLM & RAG Expertise Demonstrated experience building with Large Language Models for semantic search, feature engineering, or RAG architectures. You understand not just how to use LLMs, but when not to.
• Autonomous & Product-Minded You can take a vague requirement, define the problem, build the solution, and ship it — without waiting to be told what to do next. You think about tradeoffs: speed vs. interpretability, precision vs. recall, simplicity vs. capability.
• Engineering Discipline You write code that other people can read, extend, and trust. Strong Git hygiene, documentation habits, and a mindset toward long-term maintainability over short-term shortcuts.