ML Research Engineer, Foundation Models (Senior / Staff / Principal)

About The Team

Join a world-class team at the forefront of AI and biochemistry. At Genesis Molecular AI, we’re a tight-knit team of proven deep learning researchers, software engineers, and drug discovery pioneers. Our shared mission is nothing short of revolutionary: to forge the next generation of AI foundation models that unlock new therapies for patients with severe diseases. We conduct fundamental research at the intersection of machine learning, physics, and computational chemistry, pushing the boundaries of each field.

About The Role

This role is for a highly skilled ML Research Engineer who thrives at the intersection of fundamental research and production-grade engineering. As a core member of the Genesis AI team, you will serve as the engineering pillar for inventing, scaling, and shipping our next generation of foundation models for molecular science. You will partner closely with ML researchers, computational chemists, and drug discovery scientists to translate cutting-edge model ideas into systems that power real drug discovery programs.

• Scaling model pretraining pipelines
• Advancing reinforcement learning or post-training systems
• Optimizing performance of large molecular models
• Bringing structure prediction models like Pearl into production environments used by chemists and drug programs

Who You Are

• 2+ years industry experience of building complex ML systems
• A research engineer with deep ML rigor
• You have deep expertise in building scalable, high-performance foundation models, pretraining, and posttraining methods, and systems around them
• A builder who ships
• You write clean, high-performance code and are comfortable working across the ML stack (Python, PyTorch, distributed training systems)
• You have demonstrated experience translating research into working systems quickly
• An expert in modern ML engineering
• You understand the mathematics and systems behind modern ML methods
• You can design, optimize, and implement novel modeling approaches
• Experienced in training models at scale
• You understand distributed training, large-scale datasets, and performance optimization across GPU clusters
• You thrive in environments where models move rapidly from prototype to production
• Experience with GPU systems programming
• Hands-on experience writing CUDA kernels or optimizing GPU workloads beyond standard frameworks
• Hands-on experience with our core libraries: PyTorch, PyTorch Lightning, and Ray Distributed Training, PyTorch Geometric, etc.
• Comfortable in research ambiguity
• You can iterate on novel architectures, training pipelines, and experimental ideas while maintaining rigorous engineering discipline
• A first-principles thinker
• You approach problems from fundamentals and take pride in building robust systems from conceptual design to state-of-the-art implementation
• A curious mind, excited to dive into the emerging field at the intersection of AI, physics, chemistry, and biology and make foundational contributions and discoveries
• Insired by our culture of intellectual curiosity and the shared belief that breakthroughs happen when diverse perspectives and minds unite
• A strong cross-functional collaborator
• You communicate effectively with scientists across disciplines including computational chemistry, structural biology, and medicinal chemistry