Secure Data Infrastructure for AI

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 Qualifications and Requirements 7+ years of hands-on experience in data engineering, infrastructure security, or software security, ideally spanning both disciplines Strong System and Software Engineering skills with production-quality code in Python, Bash, and at least one systems programming language (Go, Rust, or C++) Deep experience securing GCP cloud environments, including IAM, VPC design, secrets management, workload authentication, and cloud security posture management Proven track record designing and implementing identity and access management systems, including credential issuance, rotation, and least-privilege enforcement at scale Hands-on experience with Kubernetes security: RBAC policies, namespace isolation, workload identity, pod security Certifications (Highly Desirable): CISSP, OSCP, or GWAPT for core security credentialing, plus AI-focused certifications such as GAISC, Offensive ML (OffSec), or cloud provider AI security tracks (AWS/GCP). Availability to work with team members across US and Europe, with meetings starting at 8am PT and ending at 7pm CET Readiness to travel several times a year for company retreats and business events We value the benefits of in-person collaboration