Machine Learning Intern

Helical; City Of London; On-site; GBP 13,000 - 20,000; Full time

Job summary

Helical is seeking a Machine Learning Engineer intern in City of London. You will build, optimize, and scale real-world applications of bio foundation models, working with researchers and product engineers to productionize training, inference, and deployment workflows.

This deeply technical role offers high ownership, encouraging prototyping new methods, contributing to core ML infrastructure, and translating research into fast, iterative code.

Responsibilities

• Build and maintain scalable training/inference pipelines for biological foundation models architectures (e.g. Transformers, SSMs).
• Optimize model performance, latency, and throughput across environments.
• Apply 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.

Job 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.