DevSecOps / Cloud Engineer, Trial Library

• 5+ years in DevSecOps, security, platform, or infrastructure engineering, operating production systems you were accountable for
• Demonstrated security ownership: you have run vulnerability management, remediated real findings, and participated in incident response - not just deployed tooling
• Deep Terraform proficiency: module hierarchies, multi-environment state, drift and refactors, critical plan review
• Hands-on AFT and Control Tower experience - you have vended accounts through AFT and customized the pipeline, not adjacent familiarity
• Broad AWS depth: IAM, Organizations, VPC, Lambda, RDS/Aurora, S3, KMS, CloudTrail, Config, Security Hub, GuardDuty, Secrets Manager
• GitHub Actions as a daily environment, including pipeline hardening and secrets management
• SOC 2 Type II and HIPAA experience in a real PHI-handling environment - you have owned controls, produced evidence, and sat in front of an auditor
• Change and release discipline: you think about blast radius before you apply, have owned deployment and rollback strategies in production, and move quickly inside regulated-environment constraints rather than treating them as obstacles
• Hands-on, autonomous, and clear: you write code daily, take ambiguous problems to documented decisions, and can explain security tradeoffs to non-security people
• You use AI coding and automation tools as a daily part of how you work, and you actively explore how they change infrastructure and security practices
• Genuine interest in improving clinical trial access and health equity

Desirable

• Join our small, high-trust Infrastructure team as its second engineer, reporting to the Director of Infrastructure and partnering closely with our product engineering team
• You will drive two key initiatives: building a cohesive AWS foundation with Terraform, Control Tower, and AFT, and owning end-to-end security engineering for our production PHI and Bedrock AI workloads
• Just as important, you'll build the paved paths, self-service tooling, and guardrails that let our engineers ship quickly
• AI is deeply embedded in how we work
• We use it across coding, testing, and operations, not because of a mandate but because we've seen what it unlocks
• We're looking for someone who already builds this way and is curious about what AI-augmented practice looks like in infrastructure and security work
• We value fast decisions, open feedback, and empowered engineers
• Infrastructure as code. The Terraform codebase - module design, state strategy, drift detection, plan review discipline - and the migration of our CloudFormation/Serverless footprint where it delivers real leverage, without stalling product delivery
• The AWS landing zone. Multi-account structure via Control Tower and AFT: account vending, customizations, service control policies, and OU design
• Security engineering. Security Hub, GuardDuty, Inspector, and Config as living detection tooling: triage findings, tune signals, and run the vulnerability lifecycle from discovery through verified fix. Coordinate penetration tests and own remediation
• Pipeline and supply chain security. Secure the commit-to-production path in GitHub Actions: least-privilege OIDC deployment roles, secrets scanning, SAST and dependency/container gates, branch protection, and artifact integrity
• Developer enablement. Paved-path tooling, self-service infrastructure, and secure defaults that let product engineers move fast without filing tickets
• Disaster recovery and backup. Backup strategy, RPO/RTO targets, Aurora point-in-time recovery, and regular DR testing to satisfy HIPAA contingency planning requirements
• Compliance as code. SOC 2 and HIPAA controls encoded into the platform - encryption, KMS, CloudTrail/Config coverage, log retention - with automated evidence collection
• Identity and access governance. IAM Identity Center, cross-account roles, SSO, periodic access reviews, and joiner/mover/leaver deprovisioning, alongside network foundations: VPC design, WAF, and Client VPN
• Security architecture for Bedrock AI workloads: access controls, guardrails, PHI data boundaries
• Production incident response (reliability and security) and recurrence prevention
• Observability and cost visibility: CloudWatch, alarms, dashboards, tagging
• Partnership with application engineers on Lambda, Aurora PostgreSQL, and Bedrock workloads
• Lightweight threat modeling and security review of new features and third-party integrations touching PHI, including sponsor/CRO data-handling requirements

• Our interview process typically includes four stages, followed by reference checks and an offer. Depending on the role, particularly for technical or senior positions, the process may vary slightly and can include a technical assessment, case study, presentation, practical exercise, additional interview conversations, or conversations with members of our leadership team and investors