Software Engineer - ML Infrastructure

Lead engineering efforts focused on continuous improvement of the AI platform, focused on rapid build out and iteration on scalable and robust distributed infrastructure for ML training, inference, and evaluation.

Support model training and deployment across multiple clusters and multiple clouds, optimizing for throughput and cost.

Optimizing efficiency of ML models and other workloads in terms of latency, throughput, memory consumption, etc. (e.g., via GPU performance engineering), pushing the limits of what’s possible with the current hardware.

Contribute to the long-term vision for Genesis’ infra platform.

Strong engineer who constantly strives for technical excellence. You can write clean code and have a deep understanding of the codebases you work in.

Deeply experienced with distributed training and inference of large models on GPU clusters and some of the core libraries and frameworks we use: Pytorch, Pytorch Lightning, Pytorch Geometric, and Ray.

Independent thinker with a strong sense of ownership and capability of engineering robust systems from first-principles-based conceptualization to state-of-the-art realization.

Curious, problem-oriented thinker who is excited to dive deep into the emerging field at the intersection of AI, physics, chemistry, and biology and make foundational contributions and discoveries (no previous experience in anything but ML necessary).

Experienced with building, maintaining and debugging low-level cluster infrastructure running on multiple clouds using Kubernetes and Terraform.

Experienced GPU engineer who can quickly figure out performance bottlenecks and architect highly performant code for large scale ML workloads.

Experience with XLA, Triton, CUDA, or similar accelerator programming languages and/or deep learning compiler stacks.

Experience working with some of the following: molecular systems (protein sequences and 3D structures, small molecules, etc.), ML force fields or other physics-informed models and methods, or point cloud data in other application domains, such as 3D graphics.

Genesis Molecular AI is pioneering foundation models for molecular AI to unlock a new era of drug design and development. The company’s generative and predictive AI platform, GEMS (Genesis Exploration of Molecular Space), integrates AI and physics into industry-leading models to generate and optimize drug molecules, including the breakthrough generative diffusion model Pearl for structure prediction. Genesis has raised over $300 million from leading AI, tech and life science-focused investors, signed multiple AI-focused research collaborations with major pharma partners, and is deploying GEMS to advance an internal therapeutics pipeline for a variety of high-impact targets.

Genesis is headquartered in San Mateo, CA, with a fully integrated laboratory in San Diego. We are proud to be an inclusive workplace and an Equal Opportunity Employer.