Helical

Helical is building the in-silico labs for biology and seeks an Applied Research Engineer - Post-Training to maximize the performance of cutting edge foundation models in real world drug discovery. You will own the full post training lifecycle-from alignment strategy to production deployment-for therapeutic contexts. You'll work directly with real drug discovery problems, collaborating with ML infrastructure and biology teams, making core technical decisions, and shipping improvements to

Helical is building an AI-native biology lab and production-ready AI-driven workflows. We seek a Platform Engineer to scale the infrastructure behind our virtual AI lab, debugging Kubernetes and refining architecture while shipping to production. You will own cloud infra, ML training and inference pipelines, and CI/CD across multi-tenant environments. The role involves collaboration with ML, backend, and product teams to enable scalable, secure, and observable systems for AI-driven drug

Helical is the AI-native lab for biology. We turn biological foundation models into production systems for discovery-so scientists can run experiments in silico at the speed of inference. We're already deployed with top pharma, supporting work from target identification to biomarker discovery. The Role We're hiring a Platform Engineer to build and scale the infrastructure behind our virtual AI lab. This is a hands on role: debugging Kubernetes one moment, improving architecture the next, shipping to production throughout. You'll be working on the system that makes AI-driven drug discovery actually usable at scale. What You'll Do Run and scale Kubernetes (incl. GPU workloads) Own cloud infrastructure and infra-as-code Support ML training & inference pipelines Manage CI/CD, observability, and deployments Handle databases, storage, and migrations Build automation (Python/Bash) Work closely with ML, backend, and product What We're Looking For 4+ years in platform / DevOps / infra Strong Kubernetes, Docker, cloud (AWS/GCP/Azure) GPU workloads / ML infra Experience designing & operating multi-tenant architectures with strict tenant isolation Familiarity with autoscaling & service isolation Experience with CI/CD, databases, infra-as-code Comfortable debugging production systems Nice to Have Security/compliance (SOC2, HIPAA) Exposure to AirFlow and MLFlow for scientific workflows MSC/PHD in Machine Learning Biotech / pharma experience Why Helical Live with top pharma High ownership, small team Work at the intersection of AI, biology, and systems Build something that actually changes how medicine is made High ownership, low ego

Helical is building the in-silico labs for biology Drug discovery still relies on wet labs: slow, expensive, and constrained by physical trial-and-error. Helical is changing that. We build the application layer that makes Bio Foundation Models usable in real-world drug discovery, enabling pharma and biotech teams to run millions of virtual experiments in days, not years. Today, leading global pharma companies already use Helical, and we're at the start of a highly ambitious growth journey. We're a founder-led, talent-dense team building a category-defining company from Europe. We care deeply about the quality of our work, move fast, and expect ownership. If you're excited by complexity, real responsibility, and shaping how a company actually operates as it scales, you'll feel at home here. Your Role As a Machine Learning Engineer - Scaling 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 foundation models (e.g. Transformers, SSMs). Optimize model performance, latency, and throughput across environments. Design modular, reusable ML components for internal and open-source use. Collaborate with researchers to scale notebooks into production-grade systems. Own ML infrastructure components (data loading, distributed compute, experiment tracking, etc.). 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. Scaling AI Systems For Large Post-Training Runs. Knowledge of how to integrate ML systems into user-facing applications or APIs. Interest in the biology/pharma space (not required, but you'll pick it up fast here!)

Helical is building the in-silico labs for biology, enabling pharma and biotech teams to run millions of virtual experiments in days, not years. As a Machine Learning Engineer - Scaling, you'll build, optimize, and scale production ML workflows that apply bio foundation models to real-world drug discovery challenges. You will collaborate with researchers and product engineers to productionize training, inference, and deployment pipelines, push the limits of foundation models, and own ML