What does Max know about?
For AI evaluation, Max knows about:
The information you can find on a candidate’s profile – their job titles, experience summaries, skillsets, education, and more.
Information about the companies the candidate has worked at – what they focus on, funding information, company size, etc.
General background information about common job roles and experiences, skillsets, and more.
Think of Max as having a huge amount of information about the world and about the candidate, but not yet as a trained recruiter expert in a given field.
Note that many candidates do not give a lot of information about themselves on their profile. In that case, Max won’t be able to guess about experiences or projects someone has worked on if the information is not present. As an expert in your field, consider how likely someone is to state what they are looking for on their LinkedIn profile, and focus your AI criteria on those aspects.
What should go into AI?
AI Criteria are evaluated on the set of candidates that pass the structured filters. If there are more than 10,000+ candidates that pass the structured filters, Max will not be able to look over all of them to make its decisions. If the pool of candidates is too wide, consider narrowing them to candidates that are ‘in the right ballpark’ for your AI criteria. For example, if you want to use AI to evaluate top-performing sales representatives, make sure that your structured filters first select for job titles that sales representatives would have.
Specifying AI Criteria
When you specify AI criteria for Max, remember that you are the expert in your job needs and demands. Max needs specificity from you for what you need for your role or company.
Experience in a Given Domain
Bad examples | Good examples |
Candidate is a backend engineer | Candidate is a backend engineer focused on API services and backend projects for web apps. |
| Candidate is a backend Python engineer focused on technologies like (but not exclusive to) Django, FastAPI, … |
| Candidate is a distributed systems backend engineer focused on building backend infrastructure in C++, Go, Rust, or similar. |
Candidate is a growth product manager | Candidate is a growth product manager working on retention, and has demonstrated experience with A/B testing. |
| Candidate has experience as a growth product manager focused on new user acquisition and activation. |
Candidate has worked in insurance sales | Candidate has experience selling personal lines of insurance (auto, home, life, etc.) online. |
| Candidate is a sales agent focused on corporate liability insurance, and is licensed in California. |
Without specificity, Max will be confused about what kind of candidate you're looking for in a given domain, and will return many candidates that aren't eligible for your role. In the examples above, Max wouldn't know what type of backend engineer (security, distributed systems, python services), growth PM (Acquisition, Retention, Monetization, etc.) or insurance sales (consumer, retail, online, etc.) you're looking for.
Candidate Quality
Bad examples | Good examples |
The candidate is a rockstar engineer | The candidate demonstrates shipping multiple projects to production. |
| The candidate shows a metric (revenue, growth, performance improvement) outcome from their work |
| The candidate has shipped lots of projects quickly and lists them on their resume |
The candidate has been published | The candidate has published papers on Oncology in a peer reviewed journal. |
| The candidate has written articles published in well known tech magazines. |
For subjective criteria like "rockstar" Max won't know what you mean by rockstar. Try describing what a rockstar looks like to you in more detail. For broad areas like "has been published" Max will look for any publications they’ve listed, even that kids book they self published! Try to be specific on what type of publishing you're looking for.
Candidate Career Aspect
Bad examples | Good examples |
Candidate has a successful career | The candidate has listed large, impactful projects they’ve shipped |
| The candidate has moved from a junior role to a senior role within 2 years |
| The candidate has worked as a director level or above at a well known tech company. |
Candidate hasn’t worked at an (agency/startup/company) | Hasn’t worked at a design agency in the past 6 years. |
| Hasn’t worked at pre IPO startups for the past 5 years. |
| The candidate hasn’t worked at Google since 2010. |
The candidate has 4 years of experience as a head of content at a series seed to C B2B startup. The candidate was a law professional with experience working in insurance fraud, and has transitioned to working as a lawyer in tech. | Basic Filters |
| Basic Filters |
Targeting broad terms like "hasn't worked at an agency" will exclude candidates who currently work at agencies and who worked at one 15+ years ago. If you’re open to candidates without recent experience at an agency, try being more specific.
For very specific examples like the third one above, your final candidate pool may be overly constrained with such specific criteria. Many of these points can be covered with basic filters and will give you a better final pool if you move them out of AI Reasoning.
Candidate Detailed experience
Bad example | Good examples |
Has experience mentoring others | Has led teams as a people manager or senior tech lead (likely to indicate mentorship abilities and be listed on a profile) |
| Has been involved building or shaping internship programs. |
| Has been apart of speaking panels or given talks on brand marketing. |
You may struggle to find matching candidates since most people are not likely to write details like this in their profile. You might try asking this question during the chat screen or interview stages of your hiring process.
Candidate Quality
Bad example | Good examples |
The candidate shows great ability to sell | The candidate shows track record of success (i.e. look for things like hitting quota, president's club, etc) |
| The candidate has shown they can close deals over $250,000 |
| The candidate owned their full sales cycle from lead generation to closing deals. |
Max doesn’t know what signals to look for on a typical sales resume!
Candidate Companies
Bad example | Good examples |
Candidate has worked in healthcare | The candidate has experience working with consumer fitness wearables. |
| The candidate has experience in pharmaceutical research and oncology. |
| The candidate has experience coordinating clinical trials for vaccine studies. |
Targeting board industry categories like "Healthcare" include everyone from find people who've worked at brick and mortar hospitals to fitness apps. Max will likely return candidates ineligible for your role with out more guidance on which part of an industry you're looking for.