Machine Learning Scientist
Computing Biotechnology Cloud Machine Learning
年収
Location
Benefits
13ヶ月目の給与
フル社会保険
その他の福利厚生
年間給与の見直し
旅行/会社の旅行
仕事用のノートパソコン/デスクトップ
業績ボーナス
追加の健康保険
Job Overview And Responsibility
- Design and maintain end‑to‑end data pipelines to ingest, clean, augment, and version large‑scale molecular and phenotypic datasets. - Automate high‑throughput docking simulations; extract, featurize, and curate docking poses and affinity scores for downstream modeling. - Research, prototype, and productionize graph neural network and transformer‑based models for molecular property prediction, lead optimization, and virtual screening. - Develop and integrate semi‑supervised learning and active‑learning workflows to maximize insight from limited experimental labels. - Establish evaluation frameworks and benchmarks; analyze performance metrics (e.g., ROC‑AUC, enrichment factors) and iterate on model architectures to improve accuracy and robustness. - Collaborate closely with chemists, biologists, and software engineers to integrate ML solutions into NYB’s discovery platform, ensuring reproducibility, scalability (distributed/GPU training), and maintainable codebases. - Communicate results and methodologies in team meetings, internal reports, and through external publications or conference presentations.
Required Skills and Experience
- Education & Research: • PhD or equivalent postdoctoral experience in Computer Science, Computational Biology, Bioinformatics, or a related discipline, with a strong publication record. - Technical Expertise: • At least 5+ Years of Experience in AI/ML • Proficient in Python and ML frameworks (PyTorch, TensorFlow, or JAX); experience with graph libraries (DGL or PyG) and transformer toolkits (Hugging Face). • Hands‑on with RDKit (cheminformatics) and molecular docking software (e.g., AutoDock Vina, Glide). • Familiarity with semi‑supervised techniques (consistency regularization, pseudo‑labeling) and self‑attention architectures. - Infrastructure & Scale: Experience with cloud platforms (AWS/GCP), containerization (Docker/Kubernetes), and distributed training across multi‑GPU clusters. - Analytical Foundations: Strong background in statistics, optimization, and experimental design; ability to translate biological questions into ML problems. - Collaboration & Communication: Excellent written and verbal skills; proven ability to work cross‑functionally in fast‑paced environments.
Why Candidate should apply this position
- Competitive salary - Workplace: This role is based in our Hanoi office, located in the vibrant Old Quarter. - Build a professional network through collaborations with pharmaceutical companies, industry leaders, and academic experts. - Work on impactful projects that address critical challenges in drug discovery and healthcare. - We provide a dynamic, fast-paced, and collaborative environment where problem-solving and agility are at the heart of what we do. Along with a competitive salary, we foster a culture that values ambition, confidence, and humility, consistently pushing the boundaries of innovation. If you're excited about working in a young, talented tech company and want to explore the world of AI and pharmaceuticals, we encourage you to apply.
Preferred skills and experiences
- Contributions to open source ML tools; experience in generative molecular modeling; leadership or mentoring of junior researchers. - Experience working with genomics or omics projects/data (e.g., RNA-seq, WGS, single-cell), including familiarity with common genomics tools and libraries (Bioconductor, scikit-allel, or similar).
Report to
CTO (in VietNam) and Professors, Experts (in Singapore)
Interview process
3 round: Initial Round, Technical Round, Final Round
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