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