About the role
At Inceptive, you will drive forward development that could help billions of people. To accomplish this, you will be part of a collaborative, antedisciplinary team building our biological software. Our AI models depend on rich, high-quality biological datasets. The integrity, security, and reliability of those datasets and of the infrastructure that supports them are critical to everything we do. As we scale, we need someone who can architect and own the systems that keep our data and our customer’s data safe, well-governed, and optimally accessible to our machine learning pipelines. This is a senior, hands-on role: you will design and build, not just advise. You will work closely with our ML researchers, data engineers, and computational biologists to understand data flows end to end. This includes data ingestion, training, inference, analysis, logging, result output, and model serving. Your work will help secure our infrastructure at every stage. It will also protect our most sensitive assets, including model configurations and weights, training data, and experimental results, from external adversaries and insider threats. Your Mission, should you choose to accept it Embody our vision of an antedisciplinary environment and embrace learning about areas outside of your traditional area of expertise Architect, implement, and own secure data infrastructure supporting our AI model training and deployment pipelines — from raw data ingestion to model weight storage and access Build and operate foundational security services: authentication systems, access brokers, secrets management, key management platforms, and egress/ingress controls across our multi-cloud environment Design and enforce data governance frameworks, such as RBAC/ABAC policies, audit logging, encryption at rest and in transit, workload identity, and data lifecycle management Embed security directly into our MLOps pipeline: CI/CD security controls, container and Kubernetes security, namespace isolation, and pod security standards Conduct threat modeling and secure design reviews for existing and new systems, proactively identifying attack surfaces across the full AI tech stack Identify, prioritize, and drive remediation of vulnerabilities across our data systems, cloud environments, and ML tooling, including AI-specific risks like data poisoning, model extraction, and unauthorized access to model weights Build detection and alerting pipelines for anomalous data access patterns and potential exfiltration events Establish security best practices and educate team members on secure coding, infrastructure patterns, and secure data handling for AI systems Partner with ML and biology teams to ensure data handling practices meet the highest standards for sensitive research data