About the job
Join Helical in Revolutionizing Drug Discovery
At Helical, we are pioneering in-silico labs designed to transform the landscape of biology. Traditional drug discovery methods depend on slow and costly wet labs, constrained by the limitations of physical experimentation. We are here to change the paradigm.
Our team develops the application layer that harnesses Bio Foundation Models, empowering pharmaceutical and biotech organizations to conduct millions of virtual experiments in mere days, instead of years. Currently, leading global pharma companies leverage our innovative solutions, and we are poised for an ambitious growth trajectory.
As a founder-led, high-caliber team, we are committed to excellence, rapid execution, and a culture of ownership. If you thrive in complex environments, seek authentic responsibility, and want to influence operational strategies as we scale, you will find your place with us.
Explore our work on GitHub and learn more about us at our website.
Your Role
As a Machine Learning Engineer - Scaling, you will be instrumental in building, optimizing, and scaling applications based on bio foundation models. Collaborating closely with researchers and product engineers, you will contribute to the productionization of model training, inference, and deployment workflows. Your work will involve pushing the boundaries of foundation models through prototyping innovative methods, enhancing our core ML infrastructure, and translating research into efficient, iterative code.
This role is highly technical and demands significant ownership , perfect for engineers eager to work at the forefront of AI infrastructure, model development, and system design.
What You’ll Do
- Create and maintain efficient training/inference pipelines for foundation models (e.g., Transformers, SSMs).
- Enhance model performance, latency, and throughput across various environments.
- Design modular, reusable ML components for both internal and open-source purposes.
- Work alongside researchers to transition notebooks into production-grade systems.
- Take ownership of essential ML infrastructure components (data loading, distributed computing, experiment tracking, etc.).
