Can you articulate a structured evaluation methodology? Do you understand the dimensions of quality that matter for RLHF training, or do you just have a vague sense that some responses are ‘better’?

Evaluate against five dimensions: 1. Factuality — verify every specific claim against authoritative sources; flag unsupported assertions, hallucinations, and outdated information. 2. Helpfulness — does the response fully address the user’s intent, including implicit needs the question didn’t explicitly state? 3. Coherence — is the response logically structured, grammatically correct, and internally consistent? 4. Tone — does the response match the appropriate register for the context (formal, conversational, instructional)? 5. Safety — does the response avoid harmful content, inappropriate outputs, or reinforcing biases? Document your reasoning for each dimension — the annotation is only as valuable as the explanation behind it.

Do you understand the technical mechanism behind the work you’re doing? Sub-Type A candidates who understand RLHF conceptually are more effective evaluators because they understand how their feedback propagates into model behavior.

RLHF (Reinforcement Learning from Human Feedback) is the training methodology that uses human preference data to align language models with human values. The process: 1. SFT (Supervised Fine-Tuning) — train the base model on high-quality human-written demonstrations. 2. Reward model training — human raters compare model outputs and rank them; these rankings train a reward model that captures human preferences. 3. RL optimization — the language model is optimized against the reward model using policy gradient methods (PPO). Why it matters: human feedback is the primary mechanism for ensuring AI systems are helpful, harmless, and honest. Every annotation decision influences what the model learns is “good” behavior. The quality of the training data directly determines the quality of the trained model.

Sub-Type B question. Can you think through stakeholder needs, tool selection, change management, and measurement? Or do you just describe generic training content?

Design across five phases: 1. Needs assessment — identify current AI tool usage, skill gaps across business functions (trading, compliance, client services, operations), and executive sponsorship. 2. Content design — create role-specific use cases; compliance teams need different examples than traders. Map to enterprise tools in scope (Microsoft Copilot, Bloomberg AI, proprietary systems). 3. Delivery — phased rollout; executive champions first, then function leads, then broad population. Mix of live workshops, office hours, and self-service resources. 4. Adoption metrics — track tool activation rates, daily active usage, and productivity indicators. Tie to business outcomes. 5. Sustainability — AI use-case library, internal champions network, and quarterly refreshes as tools evolve. Regulatory context: financial services AI usage may be subject to SEC guidance and internal risk management policies.

Compensation is the answer here. Domain expertise unlocks premium tiers that general annotators cannot access. Do you understand your own competitive positioning and the market dynamics of specialist annotation work?

Domain expertise operates on a premium tier system: 1. Generalist work — text evaluation, basic Q&A, general writing tasks; $20–$35/hour on freelance platforms; accessible with bachelor’s level writing and reasoning. 2. Specialist work — medical, legal, STEM, financial annotation; $40–$60+/hour; requires verifiable credentials (MD, JD, PhD, CPA). Some platforms require licensed credentials for compliance-sensitive domains. 3. Expert/researcher tier — PhD-level domain expertise for evaluating highly technical outputs; can command significantly higher rates for niche specializations. Why it matters: frontier AI models are being trained to handle expert-level queries in medicine, law, and science. The humans evaluating those outputs need equivalent expertise to detect subtle errors that a generalist would miss entirely.

This is a self-awareness question. Interviewers want to know you understand the two-track structure of the role and can articulate why your background fits one path over the other.

Sub-Type A (Model Training): Works within annotation platforms (Labelbox, Scale AI, Label Studio). Daily activities: response evaluation, RLHF ranking, SFT data creation, red-teaming. Reporting to ML Engineering or Head of Data Science. Employers: Scale AI, Appen, DataAnnotation.tech, frontier AI labs. Primary skills: analytical writing, domain expertise, critical evaluation, RLHF methodology. Sub-Type B (Adoption Coaching): Works within L&D, IT, or Digital Transformation. Daily activities: training design and delivery, change management, adoption metrics, use-case library development. Reporting to CLO or CTO. Employers: enterprise companies across every sector undergoing AI adoption. Primary skills: instructional design, adult learning principles, change management, AI tool proficiency. The key distinction: Sub-Type A trains AI systems; Sub-Type B trains the humans using AI systems. Both tracks are growing — every new model release requires Sub-Type A work; every enterprise AI deployment requires Sub-Type B work.