Research Engineer - Generative Drug Design

Role Overview: As a Senior Research Engineer at Quantiphi, you will be a key member of the R&D team focusing on Generative Drug Design. You will have the opportunity to work with state-of-the-art generative models for drug design, pushing the boundaries of efficiency and adaptability in the drug discovery space. Your role will involve designing, implementing, and optimizing reinforcement learning and imitation learning techniques for various use cases under Drug Discovery and Biotech. You will be responsible for building scalable and production-ready codebases using PyTorch, exploring novel learning approaches, and generating intellectual property through open research collaborations. Key Responsibilities: - Design, implement, and optimize RL and imitation learning techniques for Drug Discovery and Biotech applications. - Build scalable, transferable, and production-ready codebases using PyTorch. - Explore and prototype novel learning approaches to 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. Qualifications Required: - M.S. or equivalent experience in Computer Science, Artificial Intelligence, Biotechnology, BioEngineering, or a closely related field. - 2-4 years of research experience with a strong publication record. - Core proficiency in PyTorch, including the ability to write custom training loops, implement complex loss functions, and debug autograd issues. - Proven track record of designing custom neural network architectures rather than fine-tuning pre-trained models. - Extensive experience with reinforcement learning algorithms such as PPO, TRPO, DPO, DQN, A2C, or SAC. - Strong experience designing Graph Neural Network architectures (e.g., GCN, GAT, MPNN) for non-Euclidean data. - Deep knowledge of geometric deep learning and designing E(n) or SE(3)-equivariant networks. - Experience in distributed computing, MLOps, and serving frameworks. - Strong collaboration skills and the ability to work in interdisciplinary and fast-paced teams. If you are a self-starter with excellent problem-solving skills, adaptability to shifting research directions, and a passion for innovation, you will thrive in this Senior Research Engineer role at Quantiphi. Role Overview: As a Senior Research Engineer at Quantiphi, you will be a key member of the R&D team focusing on Generative Drug Design. You will have the opportunity to work with state-of-the-art generative models for drug design, pushing the boundaries of efficiency and adaptability in the drug discovery space. Your role will involve designing, implementing, and optimizing reinforcement learning and imitation learning techniques for various use cases under Drug Discovery and Biotech. You will be responsible for building scalable and production-ready codebases using PyTorch, exploring novel learning approaches, and generating intellectual property through open research collaborations. Key Responsibilities: - Design, implement, and optimize RL and imitation learning techniques for Drug Discovery and Biotech applications. - Build scalable, transferable, and production-ready codebases using PyTorch. - Explore and prototype novel learning approaches to 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. Qualifications Required: - M.S. or equivalent experience in Computer Science, Artificial Intelligence, Biotechnology, BioEngineering, or a closely related field. - 2-4 years of research experience with a strong publication record. - Core proficiency in PyTorch, including the ability to write custom training loops, implement complex loss functions, and debug autograd issues. - Proven track record of designing custom neural network architectures rather than fine-tuning pre-trained models. - Extensive experience with reinforcement learning algorithms such as PPO, TRPO, DPO, DQN, A2C, or SAC. - Strong experience designing Graph Neural Network architectures (e.g., GCN, GAT, MPNN) for non-Euclidean data. - Deep knowledge of geometric deep learning and designing E(n) or SE(3)-equivariant networks. - Experience in distributed computing, MLOps, and serving frameworks. - Strong collaboration skills and the ability to work in interdisciplinary and fast-paced teams. If you are a self-starter with excellent problem-solving skills, adaptability to shifting research directions, and a passion for innovation, you will thrive in this Senior Research Engineer role at Quantiphi.