ML Engineer - Scaling

Helical is building the in-silico labs for biology

Drug discovery still relies on wet labs: slow, expensive, and constrained by physical trial-and-error. Helical is changing that.

We build the application layer that makes Bio Foundation Models usable in real-world drug discovery, enabling pharma and biotech teams to run millions of virtual experiments in days, not years. Today, leading global pharma companies already use Helical, and we’re at the start of a highly ambitious growth journey.

We’re a founder-led, talent-dense team building a category-defining company from Europe. We care deeply about the quality of our work, move fast, and expect ownership. If you’re excited by complexity, real responsibility, and shaping how a company actually operates as it scales, you’ll feel at home here.

Our github: https://github.com/helicalAI/helical/

Our Website: https://www.helical-ai.com/

Your Role

As a Machine Learning Engineer - Scaling at Helical, you’ll build, optimize, and scale real-world applications of bio foundation models

You’ll work closely with researchers and product engineers to productionize model training, inference, and deployment workflows. You’ll also help push the limits of foundation models by prototyping new methods, contributing to our core ML infrastructure, and translating research into fast, iterative code.

This is a deeply technical role with high ownership — ideal for engineers who want to operate at the bleeding edge of AI infrastructure, model development, and system design.

What You’ll Do

• Build and maintain scalable training/inference pipelines for foundation models (e.g. Transformers, SSMs).
• Optimize model performance, latency, and throughput across environments.
• Design modular, reusable ML components for internal and open-source use.
• Collaborate with researchers to scale notebooks into production-grade systems.
• Own ML infrastructure components (data loading, distributed compute, experiment tracking, etc.).