Build, optimize, and scale machine learning training, inference, and deployment systems for biological foundation models. The intern will develop scalable pipelines, optimize performance, apply post-training methods, create reusable ML components, productionize research code, manage infrastructure, and run experiments involving fine-tuning, transfer learning, and evaluation.
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, Equity and Benefits
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