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:

%CF; training and fine-tuning large-scale AI models

%CF; developing domain-specific AI/ML modules

%CF; bridging research and real-world applications

%CF; 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

%CF; Designing and training machine learning and deep learning models for complex

scientific problems

%CF; Fine-tuning large-scale models for domain-specific applications

%CF; Developing custom AI/ML modules tailored to biomedical and drug discovery

workflows

%CF; Building scalable training pipelines and experimentation frameworks

%CF; Working on LLM-based and generative AI systems for knowledge discovery and

reasoning

%CF; Designing data pipelines for large-scale model training and evaluation

%CF; Collaborating with engineering teams to deploy models into production systems

%CF; Continuously improving model performance, robustness, and scalability

Key Responsibilities

%CF; Design, train, and fine-tune advanced ML/DL models

%CF; Develop domain-specific AI models for structured and unstructured scientific data

%CF; Build and maintain scalable training and evaluation pipelines

%CF; Conduct experiments and iterate on model architectures and approaches

%CF; Work on generative AI, LLMs, and advanced modeling techniques

%CF; Collaborate with ML engineers and backend teams for production deployment

%CF; Ensure reproducibility, performance, and reliability of AI systems

%CF; Stay up-to-date with latest research and translate it into applied solutions

Tech Stack

%CF; Core ML/DL: PyTorch, TensorFlow, JAX (preferred)

%CF; Data: NumPy, Pandas, large-scale data pipelines

%CF; AI Systems: LLMs, generative models, domain-specific architectures

%CF; Infrastructure: Distributed training, GPUs, cloud platforms

%CF; Backend Integration: FastAPI / Django (for model serving)

%CF; Cloud: AWS (primary), Azure, GCP

%CF; Other: Experiment tracking, model versioning, Docker

Core Skills

%CF; Strong foundation in machine learning, deep learning, and statistical modeling

%CF; Proven experience in training and fine-tuning large-scale models

%CF; Experience developing domain-specific AI/ML systems

Research & Applied AI (Critical)

%CF; Ability to translate cutting-edge research into real-world systems

%CF; Strong understanding of generative AI, LLMs, or advanced ML techniques

%CF; Experience designing novel approaches or improving existing architectures

Systems & Engineering Mindset

%CF; Experience building scalable training pipelines and ML systems

%CF; Understanding of model deployment and productionization

%CF; Ability to work with large datasets and compute-intensive workloads

Nice to Have

%CF; Experience in life sciences, drug discovery, or scientific datasets

%CF; Exposure to graph-based models, multimodal learning, or simulation-integrated AI

%CF; Publications in relevant AI/ML or computational science domains

%CF; Experience with distributed training and optimization

Eligibility:

%CF; PhD in Computer Science, AI, Machine Learning, Computational Biology, or related

field

%CF; 4–5 years of relevant experience in AI/ML research and applied systems

%CF; Strong track record of model development, research, or applied AI work