Machine Learning Engineer – Scaling

About the role

Helical is looking for a Machine Learning Engineer focused on scaling bio foundation models for drug discovery. You will work at the intersection of AI research and production, turning cutting‑edge models into reliable, high‑performance services used by pharma and biotech teams.

Key responsibilities

• Design, build, and maintain scalable training and inference pipelines for large models such as Transformers, SSMs, and diffusion models.
• Optimize model latency, throughput, and resource utilization across cloud and on‑premise environments.
• Create modular, reusable ML components for internal use and open‑source contributions.
• Collaborate with researchers to transform notebooks into production‑grade systems.
• Own core ML infrastructure components including data loading, distributed compute, and experiment tracking.

Required profile

• Master’s or PhD in Machine Learning, Computer Science, Applied Mathematics or a related field.
• Strong Python programming skills with deep expertise in PyTorch, JAX, or TensorFlow.
• Hands‑on experience building and scaling ML pipelines in production settings.
• Familiarity with MLOps tools such as Weights & Biases, Ray, Docker, and related practices.
• Experience with modern model architectures (Transformers, Diffusion Models, SSMs) and a high‑agency, fast‑iteration mindset.

Required skills

• Python
• PyTorch
• JAX
• TensorFlow
• Transformers
• Diffusion Models
• State‑Space Models (SSMs)
• Weights & Biases
• Ray
• Docker
• Distributed training
• Model compression
• Serving at scale