How your CV stacks up
Upload your CV to see how well it fits this job role
?%
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
Reasons to use Rodeo
Start with a chat, not a search bar
Grad scheme, placement, apprenticeship? Not sure what you want yet — that's fine. Your agent talks it through with you and turns "I have no idea" into a shortlist.
It searches the market for you
Every day your agent scans the market matching roles against what actually matters to you, not just keywords on a CV.
Only hits
No noise. No "maybe this fits." Just roles with a clear explanation of why they're right — and where to focus when applying.
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.)
Get help with your application
Your very own career expert that helps elevate your application to the next level.
Requirements
Essentials
- MSc or PhD in Machine Learning, Computer Science, Applied Math, or similar
- Strong Python programming skills, with deep knowledge of PyTorch, JAX, or TensorFlow
- Hands-on experience building and scaling ML pipelines in real-world settings
- Comfort with MLOps tools and practices (e.g. Weights & Biases, Ray, Docker, etc.)
- Experience with modern ML architectures — Transformers, Diffusion Models, SSMs, etc
- High agency, fast iteration speed, and comfort with ambiguity in early-stage environments
Bonus Points
- Contributions to open-source ML libraries or tooling
- Experience with distributed training, model compression, or serving at scale
- Scaling AI Systems For Large Post-Training Runs
- Knowledge of how to integrate ML systems into user-facing applications or APIs
- Interest in the biology/pharma space (not required, but you'll pick it up fast here!)
“It took my CV and asked me questions relevant to understanding what kind of jobs to suggest for me. Suggestions were almost perfect. Jobs were exactly what I’ve been looking for.”
Jessica, London
Skills
Location