AI-Research Scientist-Medvolt

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