Lead Adversarial Engineer
Surge AI
Lead Adversarial Engineer
Surge AI
Delivers high-quality AI training data
Our mission is to raise AGI with the richness of human intelligence — curious, witty, imaginative, and full of unexpected brilliance.
Surge was founded by engineers and researchers who dreamed of building the next generation of AI. Our platform powers the most capable models in the world, in partnership with the AI models you use every day.
At Surge, we believe the path to AGI isn't just about scaling compute—it's about embracing the unlimited ceiling of human intelligence and creativity in the data that shapes these systems. Our platform combines elite human expertise with cutting-edge tools for scalable oversight, from building rich RL environments to conducting rigorous evaluations that go beyond benchmarks. We've run a profitable business from day one without raising venture funding.
As a Lead Adversarial Engineer, you’ll run end-to-end red-teaming workstreams against frontier models — scoping threat models, designing campaigns, coordinating operators, and synthesizing results into clear risk pictures and decision-ready reports. You’ll orchestrate structured adversarial exercises across modalities and tools, ensuring coverage, reproducibility, and crisp learning loops.
You won’t just find failures — you’ll build the operational engine that repeatedly surfaces them under realistic constraints. This is a role for someone who thrives on program design, loves turning messy attack spaces into disciplined test plans, and can drive cross-functional execution from kickoff to readout.
Stand up a recurring red-team cadence: scoping targets, recruiting operators, defining success criteria, and executing multi-week campaigns
Create scenario banks and attack taxonomies; ensuring breadth/depth coverage and tracking families of exploits across versions and contexts
Produce executive readouts and issue trackers that distill severity, exploitability, and user harm, with crisp reproduction steps and artifacts
Partner with research, product, and ops teams to validate fixes and rerun focused regressions; maintaining dashboards for trendlines and residual risk
Red-Team Program Leadership – Experience planning and running adversarial campaigns (jailbreaks, prompt injection, tool abuse), including playbooks, ops cadence, and after-action reviews
Methodical Experimentation – Strength in designing scenarios, controls, and metrics; comfort triaging findings and prioritizing next passes based on evidence
Stakeholder Command – Ability to brief partners, align on objectives, and translate results into actionable remediation tracks with clear owners and timelines
Surge AI provides high-quality training and evaluation data for AI models. It creates human-annotated datasets, preference data, and reinforcement learning from human feedback (RLHF) to help developers of large language models and other machine learning systems improve accuracy, alignment, and real-world performance, especially for advanced generative AI tasks. The company targets the high end of the data labeling market by prioritizing quality, scalability, and the ability to handle complex, model-critical labeling rather than simple commodity annotations. Its goal is to support better-performing, safer, and more reliable AI systems by delivering data that directly feeds model training and evaluation.
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