Principal Scientist AI/ML for Life Sciences
Bencos Research Solutions Pvt. Ltd. · Not specified · onsite · Posted 46d ago
About a minute. You'll make an account on the way through.
About this role
About the Role
We are a small, agile team building AI models to solve complex problems in biology and healthcare. As a Principal Scientist, you will own research problems end-to-end—from framing and prototyping to validation and deployment. You will anchor our technical efforts, transforming ambiguous biological questions into actionable ML solutions.
What you will Do
End-to-End ML: Architect, train, and deploy production ML models tailored to complex life science datasets.
Multi-Modal AI: Fuse omics, imaging, structural, and clinical data to drive biological discovery.
Robust Modeling: Build resilient architectures designed to overcome small sample sizes, batch effects, and noisy data.
Rigor & Validation: Establish rigorous benchmarks, evaluation metrics, and validation protocols.
Requirements
Background: PhD + 5 yrs experience or MS + 10 yrs experience in CS, Computational Biology, Physics, or related field. Proven history of delivering production AI systems.
Technical Expertise: Deep proficiency in multi-modal AI with hands-on experience in at least two: Transformers, GNNs, Diffusion/Flow-Matching, or SE(3)-equivariant models.
Foundational ML: Mastery of self-supervised learning, representation learning, and fine-tuning for low-data regimes.
Agentic Frameworks: Experience designing and implementing agentic workflows to automate complex research or decision-making processes.
Engineering: Fluent in Python and PyTorch/JAX; strong emphasis on clean, scalable code.
Mindset: Thrives in ambiguous environments with rapid prototyping and concise cross-domain communication.
Strongly preferred
Experience with omics data (NGS, transcriptomics, proteomics).
Expertise in MLOps, cloud infrastructure (AWS/GCP/Azure), and large-scale model training.
Background in regulated or GxP environments.
Collaboration: Partner with cross-functional domain experts to translate scientific questions into tractable ML framing.
Technical Leadership: Guide research strategy, mentor scientists, and uphold engineering best practices.
Scientific Impact: Drive IP creation, high-impact publications, and foundational strategy.
Sourced from foundit