Reflection - Member of Technical Staff - Research Software Engineer - Safety Evaluations Infrastructure

• Design and build secure, sandboxed infrastructure for running sensitive model evaluations, including CBRN and other dangerous-capability domains. • Build controlled data pipelines and storage for sensitive evaluation material, applying least-privilege and need-to-know access, role-based access control (RBAC), encryption at rest and in transit, audit logging, and data-minimization safeguards. • Partner with safety researchers and domain experts to translate evaluation designs into reliable, reproducible, and scalable systems. • Build eval-orchestration tooling and harnesses that let researchers run high-throughput evaluations against models and agents in isolated environments. • Develop infrastructure for measuring AI capability uplift in high-consequence domains, and integrate results into the pipelines that inform release decisions. • Implement guardrails, monitoring, and compartmentalization so sensitive work stays appropriately siloed, applying least-privilege, need-to-know, and defense-in-depth principles across compute, data, and tooling. • Write production-quality Python (and related tooling) for high-throughput data processing and evaluation systems. • Improve the reliability, security posture, and developer experience of the safety team's evaluation platform over time. • Strong software engineering skills, particularly in Python, with a track record of building reliable, scalable infrastructure or platform systems. • Experience building sandboxed, isolated, or otherwise security-sensitive execution environments (e.g., containerization, VM isolation, secure compute) for Trust and Safety teams. • Solid grounding in security engineering fundamentals: principle of least privilege, need-to-know access, role-based access control (RBAC), secrets management, encryption, audit logging, compartmentalization, and defense-in-depth design. • Experience building data pipelines and handling sensitive or restricted data with appropriate safeguards. • Ability to own entire problems end-to-end, including ambiguous, cross-functional ones. • Comfort working on sensitive projects that require discretion, integrity, and sound judgment. • Thrive in a fast-paced, high-agency startup environment with a bias toward action. • Strong candidates may also have • Experience building evaluation, benchmarking, or experimentation infrastructure for ML systems. • Experience working with LLMs, agents, or ML training/inference pipelines. • Familiarity with dangerous-capability or dual-use domains (CBRN, cyber, etc.) and the information-security considerations they involve. • Familiarity with compliance frameworks relevant to sensitive data handling. • We encourage you to apply even if you don't meet every qualification. Not all strong candidates will match every item listed.