Bio AI Foundation ML Intern

Helical; Greater London; On-site; GBP 25,000 - 38,000; Full time

Benefits offered by this job

Competitive Salary

Equity

Benefits

Job summary

Helical is seeking a Machine Learning Engineer intern to build and scale real-world applications of bio foundation models. You will productionize model training, inference, and deployment workflows while prototyping new methods and contributing to core ML infrastructure.

This deeply technical role offers high ownership, with opportunities to work at the bleeding edge of AI infrastructure, model development, and system design, alongside researchers and product engineers.

Qualifications

• Requires MSc/PhD in ML, CS, 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 (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.

Responsibilities

• Build and maintain scalable training/inference pipelines for biological foundation models (e.g. Transformers, SSMs).
• Optimize model performance, latency and throughput across environments.
• Apply 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).
• Drive experimentation around fine-tuning, transfer learning, and evaluation at scale.

Skills

Python

PyTorch

JAX

TensorFlow

Education

MSc or PhD in Machine Learning, CS, Applied Math

Tools

Weights & Biases

Ray

Docker

Job description

Helical is seeking a Machine Learning Engineer intern to build and scale real-world applications of bio foundation models. You will productionize model training, inference, and deployment workflows while prototyping new methods and contributing to core ML infrastructure.

This deeply technical role offers high ownership, with opportunities to work at the bleeding edge of AI infrastructure, model development, and system design, alongside researchers and product engineers.