Helical Ltd.

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 Ltd. is seeking a Platform Engineer to build and scale the infrastructure behind our AI-enabled biology lab. This hands-on role covers Kubernetes operations, cloud infrastructure, and production deployment across ML pipelines. You will own CI/CD, observability, and migrations, while collaborating with ML, backend, and product teams to deliver scalable, secure, and robust systems for accelerated drug discovery.

Helical Ltd. is seeking a Software Developer with a strong background in computational biology to join our core product development team. You will bridge biological data science and software engineering to bring bio foundation models into production and optimize workflows for large-scale data processing. The role involves collaborating with ML scientists, backend engineers, and biologists to design scalable pipelines and robust services that power next-generation drug discovery tools.

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. About the Role We are looking for a Software Developer with a strong background in computational biology or bioinformatics to join our core product development team. This unique role bridges cutting-edge biological data science and software engineering, helping bring novel bio foundation models into production and optimize workflows for biological data processing and model execution. The ideal candidate is passionate about biology, enjoys coding and problem solving at scale, and is eager to collaborate across teams of ML scientists, backend engineers, and biologists. Responsibilities Design, implement, and maintain scalable pipelines for biological data processing and analysis Integrate and optimize Bio Foundation Models for production use, collaborating with ML researchers Develop tools and services for data visualization, annotation, and quality control of biological datasets Collaborate closely with AI infrastructure and software engineers to improve workflow reliability and scalability Contribute to the architecture and implementation of backend services and APIs supporting biological workflows Requirements MSc or PhD in Biology, Computational Biology, Bioinformatics, or a closely related life sciences field - you studied the science 2+ years of professional software development experience - you've shipped production code, not just notebooks Python - strong, production-grade foundations; comfortable with package structure, clean code patterns, and debugging across complex codebases Single-cell analysis - hands-on experience with the modern single-cell stack: AnnData, scanpy, scib, scipy, and related tools Biological reasoning - you can look at a set of model predictions and assess whether they make biological sense; you understand experimental design, perturbation biology, and what "evidence" means in a discovery context Data fluency - comfortable working with large biological datasets, understanding file formats (h5ad, csv, parquet), and writing efficient data processing code Familiarity with Git workflows and collaborative development practices Nice to Have Exposure to bio foundation models - you've worked with or fine-tuned models like scGPT, Geneformer, scVI, or similar Experience with pytest and test-driven development practices Familiarity with cloud development (AWS, GCP, or Azure) - running workloads beyond your local machine Experience with CI/CD pipelines and automated deployment workflows Working knowledge of Docker and containerised development environments Experience in pharma or biotech - you understand the compliance and validation expectations that come with the territory 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

Helical Ltd. is building in-silico labs for biology. We are seeking a Machine Learning Engineer - Scaling to design, optimize, and scale real-world applications of bio foundation models. You will productionize training and inference workflows and push the frontier of model methods while shaping core ML infrastructure. This deeply technical role offers high ownership as you translate research into fast, iterative code and collaborate with researchers and product engineers across the stack.

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 Ltd. is building the in-silico labs for biology and transforming drug discovery by enabling millions of virtual experiments in days. We seek an Applied Research Engineer - Post-Training to own end-to-end post-training for biological foundation models and to ship therapeutically useful tools for pharma clients. You will design pipelines, validate biologically, and work with ML infra and biology teams.