Job Opportunities API

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