Senior Research Engineer

Role Overview

• Design, implement, and optimize RL, imitation learning for a variety of use cases under Drug Discovery and Biotech.
• Build scalable, transferable, and production-ready codebases using PyTorch.
• Explore and prototype novel learning approaches that push the boundaries of efficiency and adaptability.
• Generate intellectual property, publications, and open research collaborations.
• Bring cross domain capabilities from machine learning disciplines into generative drug design.

Requirements

• M.S. or equivalent experience in Computer Science, Artificial Intelligence, Biotechnology, BioEngineering, or a closely related field
• (MTech + 2
• 1. years of research experience with a strong publication record
• Core Deep Learning & Architecture Design
• Deep Proficiency in PyTorch: You possess native fluency in PyTorch. You can write custom training loops, implement complex loss functions, and debug autograd issues without relying on high-level abstractions.
• Custom Model Development: Proven track record of designing custom neural network architectures rather than simply fine-tuning pre-trained models. You understand the mathematical intuition behind architectural choices (attention mechanisms, normalization layers, skip connections) and can innovate upon them.
• Training Dynamics: extensive experience with the nuances of neural network training, including gradient clipping, learning rate scheduling, mixed-precision training (AMP), and diagnosing convergence issues in deep networks.
• Strong theoretical and practical grasp of Model-Free and Model-Based RL.
• Experience implementing algorithms such as PPO (Proximal Policy Optimization), TRPO, DPO, DQN, A2C, or SAC (Soft Actor-Critic).
• Familiarity with Reinforcement Learning from Human/AI Feedback pipelines to align LLMs or agentic behaviors.
• Ability to design custom simulation environments and reward functions that accurately map to complex real-world objectives.
• Strong experience designing GNN architectures (e.g., GCN, GAT, MPNN) for non-Euclidean data.
• You understand message-passing paradigms, graph isomorphism challenges, and how to scale GNNs to large, heterogeneous graphs.
• Deep knowledge of geometric deep learning, specifically designing E(n) or SE(3)-equivariant networks.
• You understand how to bake physical symmetries (rotation, translation, reflection) directly into model architectures for 3D data or physical simulations.
• Experience scaling training across multiple GPUs/nodes using tools like accelerate, DeepSpeed, FSDP, or Ray.
• Proficiency in containerization (Docker, Kubernetes) and model serving frameworks (e.g., vLLM, Triton Inference Server, or TorchServe).
• Strong software engineering practices in Python, including testing, CI/CD, and writing modular, maintainable research code.
• Self-starter mindset: industrious, independent, and able to generate ideas and drive them forward without waiting for direction.
• Excellent problem-solving skills and adaptability to shifting research directions.
• Strong collaboration skills, with experience working in interdisciplinary and fast-paced teams.

Tech Stack

• Docker
• Kubernetes
• Python
• PyTorch
• Ray

Benefits

• Ample opportunities to learn, grow and interact with colleagues from varied experience and backgrounds around the globe