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
