Machine Learning Scientist

Machine Learning Scientist (only remote for candidate not available in Hanoi)

About Us

• Our company is a biotechnology company leveraging
  artificial intelligence
  to revolutionize
  drug discovery
  and develop
  innovative therapeutics inspired by nature
  .
• Our mission is to tackle the complex and costly challenges of identifying effective treatments for difficult diseases through
  AI-driven solutions
  in the pharmaceutical industry.
• Our global team — with experience delivering projects in the
  USA, Japan, India, and Singapore
  — combines cross-functional expertise in
  AI, computational biology, and molecular sciences
  to push the boundaries of innovation and accelerate discovery.

Who We're Looking For

• We are seeking a
  Machine Learning Scientist
  with
  PhD or postdoctoral experience
  , ideally in
  Computational Biology, Bioinformatics, or Computer Science
  .
• You are a
  research-oriented scientist
  passionate about applying
  deep learning
  and
  advanced modeling
  to complex biological data, driving the next generation of
  AI-enabled drug discovery
  .

Roles & Responsibilities

As a Machine Learning Scientist, you will:

• Design and maintain
  end-to-end data pipelines to ingest, clean, augment, and version large-scale molecular and phenotypic datasets.
• Automate high-throughput molecular docking simulations
  , curate affinity scores and docking poses for downstream modeling.
• Research, prototype, and productionize
  graph neural networks and transformer-based models for molecular property prediction, lead optimization, and virtual screening.
• Develop semi-supervised and active-learning workflows
  to extract maximum insights from limited experimental labels.
• Establish model evaluation frameworks
  (ROC-AUC, enrichment factors, etc.) and optimize architectures for performance and robustness.
• Collaborate cross-functionally
  with chemists, biologists, and engineers to integrate ML solutions into NYB's discovery platform, ensuring scalability (multi-GPU/distributed training) and reproducibility.
• Communicate findings
  through reports, publications, and conferences, contributing to NYB's growing scientific impact.

QualificationsEducation & Research

• PhD or equivalent postdoctoral experience in
  Computer Science
  ,
  Computational Biology
  ,
  Bioinformatics
  , or a related discipline.
• Strong
  research record and scientific publications
  in machine learning or computational biology.

Technical Expertise

• Proficiency in
  Python
  and major ML frameworks (
  PyTorch
  ,
  TensorFlow
  , or
  JAX
  ).
• Experience with
  graph ML libraries
  (DGL, PyG) and
  transformer frameworks
  (Hugging Face).
• Hands-on with
  RDKit
  (cheminformatics) and molecular docking tools (
  AutoDock Vina
  ,
  Glide
  ).
• Familiar with
  semi-supervised learning
  (consistency regularization, pseudo-labeling) and
  self-attention architectures
  .
• Knowledge of
  cloud infrastructure
  (AWS, GCP),
  containerization
  (Docker, Kubernetes), and
  distributed training
  on multi-GPU clusters.
• Solid foundation in
  statistics, optimization, and experimental design
  , with the ability to translate biological problems into ML solutions.
• Excellent
  communication skills
  and experience working in
  multidisciplinary teams
  .

Bonus Points

• Contributions to
  open-source ML projects
  .
• Experience in
  generative molecular modeling
  or
  mentoring junior researchers
  .
• Exposure to
  genomics/omics data
  (RNA-seq, WGS, single-cell) and related libraries (Bioconductor, scikit-allel, etc.).

Benefits

• Competitive salary
  based on experience and performance.
• Dynamic, collaborative, and innovative work environment
  focused on solving real-world healthcare challenges.
• Opportunity to work with global pharmaceutical partners
  and academic institutions.
• Career development support
  , with access to cutting-edge AI tools and mentorship from leading scientists.
• Workplace:
  Hanoi office, located in the
  vibrant Old Quarter
  .
• Impactful projects
  that address critical problems in
  drug discovery and biotechnology
  .