Helical

AI-driven in-silico lab for drug discovery

Machine Learning Intern

Applied Machine LearningUpdated on Nov 10

InternshipLondon, UKNo salary listedIn-person

About the Job

Requirements

A Master of Science or PhD in Machine Learning, Computer Science, Applied Mathematics, or a similar field is required.
Strong Python programming skills and deep knowledge of PyTorch, JAX, or TensorFlow are required.
Hands-on experience building and scaling machine-learning pipelines in real-world settings is required.
Comfort with MLOps tools and practices such as Weights & Biases, Ray, and Docker is required.
Experience with modern machine-learning architectures such as Transformers, Diffusion Models, and SSMs is required.
The candidate must be comfortable with ambiguity in an early-stage environment.

Responsibilities

Build and maintain scalable training and inference pipelines for biological foundation-model architectures such as Transformers and SSMs.
Optimize model performance, latency, and throughput across environments.
Apply current post-training research to existing architectures and data setups.
Design modular, reusable machine-learning components for internal and open-source use.
Collaborate with researchers to turn notebooks into production-grade systems.
Own machine-learning infrastructure components, including data loading, distributed computing, and experiment tracking.
Drive experimentation in fine-tuning, transfer learning, and evaluation at scale.

Desired Qualifications

Contributions to open-source machine-learning libraries or tooling are a bonus.
Experience with distributed training, model compression, or serving at scale is a bonus.
Familiarity with foundation-model evaluation and benchmarking frameworks is a bonus.
Knowledge of integrating machine-learning systems into user-facing applications or APIs is a bonus.
Interest in the biology or pharmaceutical space is a bonus.

Machine Learning Intern

Helical · London, United Kingdom · Full-time · Posted 2026-11-06

Workplace: on_site

Department: Platform & AI

Description

As a Machine Learning Engineer intern 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 various biological foundation models architectures (e.g. Transformers, SSMs).
Optimize model performance, latency, and throughput across environments.
Apply the latest post-training research to current architectures and data setups.
Design modular, reusable ML components for internal and open-source use.
Collaborate with researchers to turn notebooks into production-grade systems.
Own ML infrastructure components (data loading, distributed compute, experiment tracking, etc.).
Drive experimentation around fine-tuning, transfer learning, and evaluation at scale.

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.
Familiarity with foundation model evaluation and benchmarking frameworks.
Knowledge of how to integrate ML systems into user-facing applications or APIs.
Interest in the biology/pharma space.

Benefits

What we offer:

Proven product already in use with top pharma
Opportunity to shape a category-defining company at an inflection point
High ownership, direct impact, and close collaboration with founders
Competitive Salary and Benefits

Apply

Apply at Helical

About the Company

Helical

AI-driven in-silico lab for drug discovery

Helical builds a virtual AI lab that turns biological foundation models into reproducible, decision-ready in-silico workflows for pharmaceutical R&D. The platform has a Virtual Lab for biologists and translational scientists and a Model Factory for ML engineers, and both share data, models, and results to bridge computational predictions with biological validation. It enables target identification, biomarker discovery, and therapeutic design by running computational workflows that generate actionable insights which are validated experimentally. The company differentiates itself by integrating biology and machine learning in a collaborative platform that creates an evidence layer linking predictions to validation, and by working with top pharma customers to shorten discovery timelines from months to days.

Company size: 11-50
Company stage: Seed
Total funding: $12.4M
Headquarters: Luxembourg, Luxembourg
Founded: 2023

Recently Posted Jobs

Explore more opportunities at Helical

See more jobs