At a Glance
- Tasks: Build and scale cutting-edge ML applications for drug discovery.
- Company: Join a pioneering tech company transforming biology with AI.
- Benefits: Competitive salary, flexible work options, and opportunities for growth.
- Other info: Dynamic startup environment with high ownership and rapid career advancement.
- Why this job: Make a real impact in the biotech field while working with innovative technologies.
- Qualifications: MSc or PhD in relevant fields and strong Python skills required.
The predicted salary is between 80000 - 100000 £ per year.
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.
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.).
Essentials
- MSc or Ph D in Machine Learning, Computer Science, Applied Math, or similar.
- Strong Python programming skills, with deep knowledge of Py Torch, JAX, or Tensor Flow.
- 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!)
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ML Engineer - Scaling employer: Helical
Helical is an exceptional employer for those looking to make a meaningful impact in the field of drug discovery through innovative AI solutions. With a founder-led, talent-dense team based in London, we foster a fast-paced work culture that values ownership and creativity, offering interns the chance to work directly with senior leadership and shape the future of our operations. Our commitment to employee growth is evident as we provide opportunities for full-time positions and encourage the use of cutting-edge tools to transform workflows, making every day at Helical a rewarding experience.
