Role Overview

We are looking for an AI Research Scientist to lead the development of advanced AI/ML

This role focuses on:

● training and fine-tuning large-scale AI models

● developing domain-specific AI/ML modules

● bridging research and real-world applications

● building scalable, production-ready AI systems

You will work at the intersection of machine learning, scientific data, and real-world

deployment, contributing to the development of next-generation AI systems for drug

discovery.

What You’ll Work On

● Designing and training machine learning and deep learning models for complex

scientific problems

● Fine-tuning large-scale models for domain-specific applications

● Developing custom AI/ML modules tailored to biomedical and drug discovery

workflows

● Building scalable training pipelines and experimentation frameworks

● Working on LLM-based and generative AI systems for knowledge discovery and

reasoning

● Designing data pipelines for large-scale model training and evaluation

● Collaborating with engineering teams to deploy models into production systems

● Continuously improving model performance, robustness, and scalability

Key Responsibilities

● Design, train, and fine-tune advanced ML/DL models

● Develop domain-specific AI models for structured and unstructured scientific data

● Build and maintain scalable training and evaluation pipelines

● Conduct experiments and iterate on model architectures and approaches

● Work on generative AI, LLMs, and advanced modeling techniques

● Collaborate with ML engineers and backend teams for production deployment

● Ensure reproducibility, performance, and reliability of AI systems

● Stay up-to-date with latest research and translate it into applied solutions

Tech Stack

● Core ML/DL: PyTorch, TensorFlow, JAX (preferred)

● Data: NumPy, Pandas, large-scale data pipelines

● AI Systems: LLMs, generative models, domain-specific architectures

● Infrastructure: Distributed training, GPUs, cloud platforms

● Backend Integration: FastAPI / Django (for model serving)

● Cloud: AWS (primary), Azure, GCP

● Other: Experiment tracking, model versioning, Docker

Core Skills

● Strong foundation in machine learning, deep learning, and statistical modeling

● Proven experience in training and fine-tuning large-scale models

● Experience developing domain-specific AI/ML systems

Research & Applied AI (Critical)

● Ability to translate cutting-edge research into real-world systems

● Strong understanding of generative AI, LLMs, or advanced ML techniques

● Experience designing novel approaches or improving existing architectures

Systems & Engineering Mindset

● Experience building scalable training pipelines and ML systems

● Understanding of model deployment and productionization

● Ability to work with large datasets and compute-intensive workloads

Nice to Have

● Experience in life sciences, drug discovery, or scientific datasets

● Exposure to graph-based models, multimodal learning, or simulation-integrated AI

● Publications in relevant AI/ML or computational science domains

● Experience with distributed training and optimization

Eligibility:

● PhD in Computer Science, AI, Machine Learning, Computational Biology, or related

field

● 4–5 years of relevant experience in AI/ML research and applied systems

● Strong track record of model development, research, or applied AI work