Machine Learning Scientist
6 days ago
Job Description Job overview and responsibility - Design and maintain endtoend data pipelines to ingest, clean, augment, and version largescale molecular and phenotypic datasets. - Automate highthroughput docking simulations; extract, featurize, and curate docking poses and affinity scores for downstream modeling. - Research, prototype, and productionize graph neural network and transformerbased models for molecular property prediction, lead optimization, and virtual screening. - Develop and integrate semisupervised learning and activelearning workflows to maximize insight from limited experimental labels. - Establish evaluation frameworks and benchmarks; analyze performance metrics (e.g., ROCAUC, 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 experiences - 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 (HuggingFace). Handson with RDKit (cheminformatics) and molecular docking software (e.g., AutoDock Vina, Glide). Familiarity with semisupervised techniques (consistency regularization, pseudolabeling) and selfattention architectures. - Infrastructure & Scale: Experience with cloud platforms (AWS/GCP), containerization (Docker/Kubernetes), and distributed training across multiGPU 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 crossfunctionally in fastpaced environments. 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). 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. Report to CTO (in VietNam) and Professors, Experts (in Singapore)
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