Gene Solutions is a leading Vietnamese biotech company (founded in 2017) developing genetic testing and AI-driven healthcare solutions. With a network of NGS labs across Asia, we are building an intelligent platform that delivers personalized insights, risk prediction, and clinical tools to transform prenatal care and precision medicine.
About the Role
As a Research Engineer, you work at the intersection of AI research and product engineering. You will build and ship AI features end-to-end — from understanding clinical problems to designing systems, implementing models, and measuring real-world impact. The role spans data pipelines, model inference, APIs, internal tools, and lightweight user interfaces.
Key Responsibilities
- Design and build AI pipelines including LLM workflows, retrieval-augmented generation (RAG), and agent-based systems for clinical applications.
- Develop APIs, internal tools, and simple user interfaces that allow clinicians to use AI outputs directly.
- Define evaluation metrics and frameworks to measure model performance and real-world impact.
- Optimize AI inference performance, reliability, and cost in production environments.
- Explore new research ideas and rapidly build proof-of-concept systems.
- Collaborate with clinicians, bioinformaticians, data scientists, and product teams to deliver features end-to-end.
- Maintain data pipelines for real-world clinical data, ensuring validation, traceability, and data quality.
- Document systems clearly for both engineering and clinical audiences.
What We’re Looking For
- Bachelor’s degree in Computer Science, AI, Data Science, Bioinformatics, or related fields, or equivalent practical experience.
- Strong Python skills with hands-on experience in AI/ML or LLM systems.
- Experience with LLM APIs, RAG pipelines, agent frameworks, or prompt engineering.
- Familiarity with SQL, REST APIs, Git, and basic infrastructure such as Docker and CI/CD.
- Self-driven mindset with strong ownership, experimentation, and problem-solving ability.
- Ability to communicate clearly with both engineering and clinical teams.
Extra credit for:
- Experience with genomics, NGS, NIPT, or bioinformatics pipelines.
- Experience working with clinical or medical datasets.
- Familiarity with vector databases or semantic search systems.
- Experience with ML training workflows or MLOps tools.
- Basic frontend development skills to build simple tools.
- Open-source projects, research publications, or technical portfolios.
Why Join Us
- Build AI systems that directly impact prenatal screening and clinical decision-making.
- Work with large-scale genomics data and applied AI in healthcare.
- High ownership culture with opportunities to build and deploy real systems.
- Collaborate with scientists, clinicians, and experienced AI engineers.
- Competitive compensation, bonuses, insurance coverage, and team activities..
