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 and expect candidates to primarily work from our Palo Alto or Berlin offices

Preferred Technical Skills

PhD or advanced degree in Computer Science, Electrical Engineering, or a related field or equivalent practical experience
Familiarity with AI/ML-specific security risks: data poisoning, model extraction, prompt injection, unauthorized model weight access, and adversarial attacks on training pipelines, and practical mitigations for each
Experience securing ML infrastructure, including model registries, training cluster access, dataset versioning, experiment tracking systems, and GPU compute environments
Proficiency with Terraform infrastructure-as-code and GitOps security practices, including automated misconfiguration detection and remediation
Experience with compliance frameworks relevant to sensitive research data (SOC 2, HIPAA, GDPR) and translating them into concrete engineering controls
Background in offensive security techniques, including threat modeling, penetration testing, vulnerability research, or red team exercises. In a nutshell, the ability to think like an adversary
Experience building detection pipelines for insider threats, data exfiltration, and anomalous access patterns
Knowledge of cryptographic protocols and their practical application in distributed systems (key management, TLS, secure enclaves)
Prior experience in a fast-moving startup or research environment where security must scale alongside rapid growth

Compensation

$200K – $275K + Bonus + Equity

What we offer

A competitive compensation package
30 days paid vacation per year
Comprehensive health insurance for US based Beginners
401K with company match for US based Beginners and Direktversicherung for German Beginners
Quarterly company-wide retreats
Monthly wellness benefit
Budget for multiple visits per year to our offices in Berlin, Palo Alto or Switzerland
Learning & Development budget to attend conferences, take courses, or otherwise invest in your professional growth, as well as access to the Learning & Development platform EdX and Hone
A buddy to help you get settled

*Varies by country and does not apply to internships

At Inceptive, we are creating tools to develop increasingly powerful biological software for the rational design of novel, broadly accessible medicines and biotechnologies previously out of reach. Our team brings together vast expertise in molecular biology, machine learning, and software engineering, and we are all working towards becoming antedisciplinary, meaning we deepen the knowledge we have in our area of expertise while also expanding our knowledge of completely new fields.

We approach our goals with a Beginner's mind, humbly and with fresh eyes, and aim to become the pioneers of a new discipline rooted in biology as much as in deep learning, whose impact will be realized together with out-of-the-box thinkers in business and entrepreneurship, defying established categorizations. We are building a company culture centered around growth, learning, and discovery. We believe in humility and open-mindedness in how we approach each other, as well as problems we don't yet have solutions for.

It is the policy of Inceptive to ensure equal employment opportunity without discrimination or harassment on the basis of race, color, religion, sex, sexual orientation, gender identity or expression, age, disability, marital status, citizenship, national origin, genetic information, or any other characteristic protected by law. Inceptive prohibits any such discrimination or harassment.

Inceptive is also committed to welcoming and providing accommodations to people with disabilities. Please let us know if you need any accommodations throughout your application process.