Machine Learning Engineer - Scaling

About Helical

Helical is revolutionizing drug discovery by creating in-silico labs for biology, moving beyond traditional wet labs which are slow, costly, and limited by physical experimentation. We develop an application layer that leverages Bio Foundation Models to enable pharmaceutical and biotech firms to conduct millions of virtual experiments within days rather than years. Our innovative platform is already trusted by leading global pharma companies, and we are embarking on a significant expansion phase. As a founder-led and talent-rich company from Europe, we prioritize high-quality work, agility, and ownership. We seek individuals eager to tackle complexity, take on real responsibilities, and help shape an evolving company at scale.

Role Overview

In the position of Machine Learning Engineer - Scaling, you will be instrumental in building, optimizing, and expanding production applications of bio foundation models. Collaborating tightly with researchers and product engineers, you will transform model training, inference, and deployment workflows into reliable production-grade systems. You will also explore advanced methods through prototyping, contribute to our core machine learning infrastructure, and convert research concepts into efficient, iterative software solutions. This role demands technical excellence and ownership, ideal for engineers passionate about cutting-edge AI infrastructure, model innovation, and system architecture.

Responsibilities

• Create and sustain scalable training and inference pipelines for foundation models such as Transformers and State Space Models (SSMs).
• Enhance model performance by optimizing latency and throughput across different environments.
• Design reusable and modular machine learning components for internal use and open-source contributions.
• Work with researchers to upgrade notebooks into robust, production-ready systems.
• Manage essential ML infrastructure including data loading, distributed computing, and experiment tracking mechanisms.

Requirements

• A Master’s or PhD degree in Machine Learning, Computer Science, Applied Mathematics, or a related discipline.
• Expertise in Python programming with in-depth experience in frameworks like PyTorch, JAX, or TensorFlow.
• Proven track record of constructing and scaling machine learning pipelines in practical environments.
• Familiarity with MLOps tools and workflows such as Weights & Biases, Ray, and Docker.
• Solid understanding of contemporary ML architectures including Transformers, Diffusion Models, and SSMs.
• High personal initiative, ability to iterate rapidly, and comfort operating within ambiguous, early-stage setups.

Additional Advantages

• Contributions to open-source machine learning libraries or tools.
• Experience with distributed training, model compression techniques, and scalable serving systems.
• Expertise in scaling AI systems for extensive post-training processes.
• Knowledge on integrating ML systems with user-facing applications or APIs.
• A keen interest in the biological and pharmaceutical sectors, which can be rapidly developed on the job.