Christopher Frost
Product Designer
Auto-hiring lets clients automatically onboard hundreds of freelancers based on criteria that they set up just once while posting a job.
This is the second article in the series dedicated to Work App. Read about bulk hiring in our previous article.
Work App — hiring on autopilot
While using the app we noticed that for clients the process of hiring usually repeats from applicant to applicant. Before hiring, a client reviews the freelancer’s profile by scanning properties like rating, past jobs, experience, bid, and others. It’s tedious, very routine, and being repeated over dozens (or hundreds!) of candidates ruins the experience. Bulk-hiring fixes this in some way, but still requires the client to come back to the app to review a batch of candidates. So we decided to automate the entire hiring process.
The design of the feature was not straightforward, and we went through a few challenges while making it reliable, safe, and truly convenient.
How it works
Unlike manual hiring, you set up auto-hiring when you first create a job, before any applicants apply. Once you set your criteria and budget, auto-hiring runs quietly in the background. When freelancers apply, the system checks their profiles against your criteria. If they match, the system hires them, moves their payment into escrow and sends them their onboarding instructions automatically.
Setting up auto-hiring criteria and quantity, and paying escrow to begin hiring.
Filtering criteria
An automated hiring system is only as reliable as its filters. If the targeting is too broad, the system will onboard unqualified workers; if it is too restrictive, the pipeline stalls. We could not just show every single filter option to the client. Overloading the screen with too many complex choices makes the app hard to use.
Bulk-selection & actions
The list of applicants is designed for dense, high-speed triage, giving clients the visual information needed to execute mass actions without second-guessing. The ultra-space-efficient row layout highlights only the essential data points required for a split-second evaluation: the applicant’s bid amount, star rating, country, and verified badge.
Auto-hiring begins with setting the criteria for who to hire.
Progressive disclosure filters
To solve this, we looked at how high-volume clients actually hire to find the filters they use the most. We organized these so the most common options appear first. The primary criteria are quick-toggle selectors for location, timezone, freelancer category and minimum star rating. Our research showed these four parameters satisfy over 90% of basic auto-hiring requirements.
For more specialized workflows, clients can expand the drawer to reveal secondary criteria like minimum jobs completed, specific cost tiers and more, also allowing them to target or exclude previously hired freelancers. This keeps the main screen clean while still giving clients the precise control they need for specific jobs.
Hiring filters expand to show further in depth criteria.
Scale & velocity
If a lot of people apply at once, a system with no restrictions may automatically hire too many workers too quickly, making it hard for clients to manage the team or track their spending.
Quantity and pacing limits
We designed two simple limits to keep clients in control of their hiring. First, clients have to set a total hire limit before turning on auto-hiring. Once this cap is reached, the system pauses automatically.
Second, clients often want to check early work before hiring more people. We added an hourly limit to space out the hires. By capping how many people can be hired per hour, clients get a buffer to review early work and adjust their settings before the system hires the rest of the group.
Client sets the quantity and pace of auto-hiring.
Escrow & payouts
We needed to secure funding upfront so freelancers are guaranteed payment when hired. At the same time, we didn’t want to ask a client to pay each individual freelancer.
Shared escrow pools
To make payments work smoothly, we designed a shared escrow pool that clients fund just once upfront. When someone is automatically hired, the system reserves their project budget from this central pool and locks it in escrow. It remains secured in escrow until the freelancer submits their completed work and the client manually reviews and approves the deliverable.
If the central pool balance drops below the threshold required to fund the next potential hire, the client receives a notification to top up the pool. If the pool runs out of money, the system pauses new hires, but already-hired freelancers keep working because their funds are already locked and guaranteed.
Client gets notified when escrow needs to be topped up.
Auto-hiring is initiated by the client paying into the escrow pool.
Further auto-hiring features
Automation should never feel like a black box. Once auto-hiring is active, the screen changes to show clear progress, letting clients track things and change settings on the fly. Clients can edit criteria, adjust pacing limits or add money directly to the active campaign.
A master toggle lets clients pause auto-hiring instantly at any time, freezing the hiring process and stopping all spending. For close tracking, a status screen lists every automatically hired freelancer, their progress and their individual controls. By combining automated speed with manual controls, Work App balances fast hiring with complete safety.
In our next article we will explore how we built on these background systems to design AI-driven contract negotiation workflows.
