While technology is the heart of our business, a global and diverse culture is the heart of our success. We love our people and we take pride in catering them to a culture built on transparency, diversity, integrity, learning and growth.
If working in an environment that encourages you to innovate and excel, not just in professional but personal life, interests you- you would enjoy your career with Quantiphi!
We are seeking a highly innovative Machine Learning Engineer to join our R&D team. In this role, you will have the opportunity to work with the State-of-the-art generative models for drug design and build new models/components that improve the overall promise of generative models in the drug discovery space.. You will operate at the intersection of advanced deep learning and research, with a mandate to push the efficacy of the in silico hypotheses of drug design to yield better results in the lab. You will work on grounding the deep learning models with domain knowledge and build custom solutions. This position requires a researcher’s curiosity combined with an engineer’s rigor, as you will be responsible for the full lifecycle of model development—from conceptualizing and building generative models that develop drug like candidates, building rigorous evaluation frameworks and optimize the best candidates that can have the best chance of success in the lab.
ResponsibilitiesDesign, 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.
M.S. or equivalent experience in Computer Science, Artificial Intelligence, Biotechnology, BioEngineering, or a closely related field.
(MTech + 2 - 4) 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.
Reinforcement Learning (RL)
RL Algorithms: 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).
RLHF/RLAIF: Familiarity with Reinforcement Learning from Human/AI Feedback pipelines to align LLMs or agentic behaviors.
Environment Design: Ability to design custom simulation environments and reward functions that accurately map to complex real-world objectives.
Graph Neural Networks (GNNs)
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.
Equivariant & Invariant Networks
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.
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 and can innovate upon them.
Engineering & "Other Technologies"
Distributed Computing: Experience scaling training across multiple GPUs/nodes using tools like accelerate, DeepSpeed, FSDP, or Ray.
MLOps & Serving: Proficiency in containerization (Docker, Kubernetes) and model serving frameworks (e.g., vLLM, Triton Inference Server, or TorchServe).
Code Quality: 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.
Familiarity with LLMs, agentic AI and vision models is a plus.
Hands-on experience applying RL in life sciences based use cases.
Contributions to open-source Deep Learning libraries.
Experience with alternative frameworks like JAX or specialized hardware (TPUs).
If you like wild growth and working with happy, enthusiastic over-achievers, you'll enjoy your career with us!
