AI Model Policy Trainer, Generalist (Seattle)

Handshake was founded on a simple belief that everyone deserves a path to a great career regardless of where they went to school or who they know. Today we power 25 million job seekers 1 million employers and 1600 educational institutions.

In 2025 we started Handshake AI and built the fastest-growing AI data business in history. We work directly with frontier AI lab researchers to create evaluations publish benchmarks and push the boundary of data. Weve grown from $0 to $1B run rate and pay $60M to over 30K individuals every month.

Why join Handshake now:

• Shape how every career evolves in the AI economy at global scale with impact your friends family and peers can see and feel

• Partner hand-in-hand with world-class AI labs Fortune 500 partners and the worlds top educational institutions

• Work together with engineers scientists operators and more from Palantir Meta Scale AI and former YC founders

• Build a massive fast-growing business with billions in revenue

About Handshake AI

Human data is the core infrastructure to AI advancement. Frontier AI labs currently improve model capabilities with various data-intensive post-training techniques. We believe that data spend for AI training will increase by 3-5x in the next few years and continue for much longer as models take on new domains. Handshake AI supports all of the frontier AI labs working on their most complex data at the largest scale.

As an AI Policy Generalist you will turn complex customer policies into consistent well-reasoned evaluations of AI model behavior.

You will read user requests model responses and relevant conversation history then determine which policy category best applies. The most interesting cases will not have obvious answers. Two examples may look almost identical until a single word contextual detail or difference in intent changes the correct classification.

We are looking for people who enjoy splitting hairs in a healthy way. You form clear opinions explain precisely why two cases should be treated differently challenge interpretations respectfully and change your mind when better evidence emerges. You understand that productive disagreement is not about winning an argument. It is how a team finds the most accurate and consistent interpretation.

This is not rote annotation. Policies cannot anticipate every possible edge case and good evaluators do not apply them mechanically. You will balance the policys text and intent with customer expectations conversation context precedent and team calibration.

The subject matter will vary. One project may involve distinguishing benign assistance from meaningful facilitation of harm. Another may require evaluating whether an interaction reflects ordinary emotional support or unhealthy reliance. A third may focus on nuanced boundaries within sexual-safety policy. Success requires learning each customers framework on its own terms rather than carrying assumptions from one domain into another.

• Learn new customer policies definitions taxonomies and evaluation rubrics quickly

• Evaluate user requests and AI model responses within the full relevant conversation context

• Distinguish between closely related labels severity levels and policy boundaries

• Select the most defensible classification when a case is genuinely ambiguous

• Write concise evidence-based rationales that cite relevant policy language and conversation details

• Identify policy gaps contradictions unclear definitions and emerging edge cases

• Raise thoughtful questions when existing guidance does not resolve a case

• Participate actively in calibration discussions with evaluators project leads policy teams and researchers

• Challenge interpretations respectfully and update your judgment when new guidance or stronger reasoning emerges

• Apply customer policy consistently without substituting personal beliefs for the policy standard

• Maintain accuracy and attention to detail across repeated evaluations

• Incorporate feedback quickly and apply clarified guidance to future work

• Help improve evaluation frameworks examples decision rules and quality standards

• Move effectively between projects covering different policy domains and customer needs

• You enjoy making precise distinctions between cases that other people might consider equivalent

• You notice when one word contextual detail or change in intent materially affects the answer

• You can hold a strong opinion without becoming attached to being right

• You explain judgment calls clearly enough that another person can audit your reasoning

• You ask productive questions when a policy is ambiguous instead of guessing or forcing certainty

• You can separate your personal views from the standard a customer has asked you to apply

• You are comfortable discussing disagreement directly respectfully and without making it personal

• You can follow the letter of a policy while also understanding its purpose and underlying logic

• You remain careful and consistent during repetitive feedback-heavy work

• You learn unfamiliar subject matter quickly and know when additional context is needed

• You are intellectually curious self-directed and comfortable working in a fast-changing environment

• You communicate clearly and precisely in writing

• You treat sensitive information and difficult subject matter with maturity and sound judgment

Strong candidates may come from quality assurance research editing law teaching operations trust and safety content moderation social science policy investigations compliance customer support or other fields that require careful interpretation and defensible decision-making. We care more about how you reason than where you learned to reason.

• Experience evaluating or comparing outputs from ChatGPT Claude Gemini or other language models in a professional capacity

• Prior work in AI evaluation data annotation RLHF model quality trust and safety policy operations or content moderation

• Experience applying detailed rubrics taxonomies regulatory language editorial standards or quality frameworks

• Familiarity with calibration sessions inter-rater agreement quality audits or adjudication workflows

• Experience writing policy guidance decision trees evaluation examples or structured rationales

• Comfort working with long conversations incomplete context and conflicting evidence

• Familiarity with AI safety responsible AI or the ways language models can assist mislead or cause harm

Prior AI evaluation experience is helpful but it is not required.

This role involves regular and deliberate engagement with sensitive material. Depending on the project evaluations may include sexual content emotional distress self-harm suicide violence weapons abuse exploitation discrimination and other potentially disturbing subjects.

The work is conducted within structured evaluation frameworks and professional guidelines. Candidates must be able to engage with this material carefully responsibly and sustainably while maintaining sound judgment and consistent work quality.

• Location: Seattle WA

• Compensation: $55-$75

• Employment classification: W-2

• Schedule: 8AM - 5PM PT

• Weekly commitment: M-F

California eligibility: We are unable to hire candidates residing in California for this role.

Required Experience:

IC