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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in defining and shipping data products for AI model training and evaluation, with a strong focus on safety metrics and adversarial scenarios. Proficient in translating complex research goals into actionable product specifications while collaborating effectively with cross-functional teams.
Highest-signal resume keywords
Product Management ExperienceML And AI Data FamiliarityAI Safety Concepts KnowledgeTechnical Depth In Code ReviewExperience Designing Risk Metrics
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Product ManagementLLM Training And EvaluationReward ModelsBenchmark DesignAI Safety ConceptsTechnical TradeoffsMetrics DesignAdversarial Thinking
Soft Skills
Clear CommunicationCreative Problem-Solving
Tools & Technologies
PythonDockerVirtual MachinesBrowser Automation
Industry Keywords
Agentic AITrust And SafetyCyber-Trap EnvironmentsMisalignment EvaluationSafety Training Data
Tech Stack
Tools & technologiesDockerPython
About the role
Key responsibilities & impact- Define and ship data products for frontier labs training and evaluating AI models for personalization, computer-use, and GDPval tasks
- Translate lab research goals into product specifications for safety training data, RL environments, and evaluations
- Design sandboxed RL environments with misalignment traps, cyber traps, honeypots, and other adversarial scenarios
- Specify safe-behavior rubrics, verifiers, and reward signals for agentic tasks
- Define measurable safety and trust metrics, including attack success rate, over-refusal, trap trigger rate, and task completion under adversarial pressure
- Track blind spots and drive product iteration
- Frame problems, assess business and technical tradeoffs, and distinguish MVP from ideal state
- Collaborate with lab researchers, ML and platform engineers, red-teamers, policy experts, and delivery leads managing expert annotator pools
- Monitor agentic risks, alignment research, and emerging attack techniques and convert them into roadmap items
- Write product specifications, customer briefs, and safety findings for researchers and executives
- Build cyber-trap environment suites, integrity-trap tasks, misalignment evaluation harnesses, and trap libraries with quality frameworks
Requirements
What you’ll need- 5+ years of product management experience, including technical products in ML, AI data, security, or trust & safety
- Hands-on familiarity with LLM training and evaluation, including SFT, RLHF/RLAIF, reward models, verifiers, and benchmark design
- Working knowledge of agentic AI, tool use, computer-use agents, multi-step planning, and related failure modes
- Strong grasp of AI safety concepts including reward hacking, deceptive alignment, jailbreaks, and prompt injection
- Technical depth to read code, review environment specifications, and make sound technical tradeoffs
- Experience designing metrics that capture risk, system performance, and user impact
- Track record of shipping 0-to-1 products in fast-moving, ambiguous environments
- Ability to explain complex technical ideas clearly to researchers, customers, and executives
- Creative, adversarial thinking and ability to anticipate model shortcuts and risks
- Comfort with Python and sandboxed environments (Docker, VMs, browser automation) is a plus