Job Search While Unemployed: What Actually Counts for Benefits
By
Ethan Fahey
•
Jan 12, 2026
If you’re a laid-off AI engineer, you’re probably juggling two frustrations at once: a crowded job market where every “Senior ML Engineer” role pulls in 150+ applicants, and unemployment requirements that don’t quite map to how technical hiring actually works. You might spend the day polishing a RAG demo on GitHub, completing a prompt-engineering course, or prepping for a system design interview, then wonder which of those efforts actually count toward keeping your benefits. The baseline rule is simple but strict: you must be able, available, and actively looking for work, and that search needs to be trackable and verifiable.
For AI engineers, ML researchers, infra engineers, and LLM specialists, “actively looking” often looks different than it does for other roles. Technical interviews, take-home challenges, open-source contributions, and advanced coursework can count in many states when they’re clearly documented and tied to a real job search. Running that search through a high-signal platform like Fonzi can help on two fronts: it creates a clear paper trail of applications, interviews, and employer conversations, and it connects you with vetted companies that are actually hiring. Fonzi’s Match Day model helps turn scattered effort into focused, documentable progress, making it easier to stay compliant while maximizing your chances of landing the right next role.
Key Takeaways
Most states require you to complete 3-5 verifiable job search activities each week to maintain unemployment benefits. This includes applying to roles, interviewing, attending job fairs, and meeting with workforce counselors, all of which must be documented with dates, employer names, and proof of contact.
Most states require 3-5 documented work search activities per week, such as submitting applications, attending interviews, participating in hiring events, or completing approved training programs.
AI-related activities can count toward your requirements when tied directly to employment: building portfolio projects, contributing to open source, completing relevant courses, or attending technical workshops. Just ensure you document everything.
Joining Fonzi and participating in Match Day counts as a valid job search activity since you’re engaging with a private employment agency, completing profiles for employer discovery, and attending structured hiring events.
AI in hiring (including Fonzi’s matching) reduces noise and bias, helping recruiters focus on relevant candidates rather than replacing human judgment. Your interactions create a clear audit trail for unemployment documentation.
State requirements vary significantly, so always cross-check your specific obligations on your state unemployment agency’s website before assuming any activity counts.
What Job Search Activities Usually Count for Unemployment Benefits?
Most states require 3–5 work search activities weekly to maintain your weekly benefit amount. The specifics depend on your state’s training administration and local workforce center guidelines, but the core principle is consistent: you need to demonstrate genuine, verifiable efforts to find suitable work.
Common activities that typically count:
Applying to roles directly: Submitting a job application through company career sites, WorkInTexas.com, Indeed, or LinkedIn. Each distinct application to a different employer counts as one activity. Include the business name, position title, and business contact information in your log.
Interviewing for positions: Phone screens, video interviews, and onsite interviews for roles like “Senior ML Engineer, Recommender Systems” or “LLM Ops Engineer” all qualify. A single job interview process may count as one or multiple activities, depending on your state.
Attending job fairs and hiring events: Participating in a virtual job fair, state-run WorkSource event, or company-hosted virtual AI careers fair counts when you register and attend. Save confirmation emails as proof.
Meeting with a career coach or counselor: Appointments at your local workforce center, reemployment services orientations, or sessions with state employment counselors qualify. Get documentation of attendance.
Completing approved training: Instructor-led bootcamps, a week of a self-paced deep learning course, or a hands-on MLOps workshop can count when they’re designed to improve your employability. Save receipts, syllabi, and completion certificates.
Informational interviews and networking: Reaching out to staff engineers at target companies, attending professional meetups, or conducting informational interviews with potential employers often qualifies, but ensure you document who you spoke with and when.
Registering with employment agencies: Signing up with a private employment agency, staffing firm, or curated marketplace like Fonzi counts as an approved activity in most states.
Union referral activities: If you’re union-attached, using union hiring halls or referral systems qualifies. Keep records of your union contact and dispatch status.
You generally cannot count the same activity multiple times in the same week. Rewatching the same webinar or resubmitting the same application doesn’t add to your count. Each activity must be distinct.
How Many Activities You Need and How States Differ
The number of required activities and what qualifies varies significantly by state. Your work search requirements depend on where you filed your claim, your employment status (job-attached, union-dispatched, in approved training), and sometimes your occupation or benefit type.
Key patterns from state guidelines:
Washington requires at least 3 job search activities per week, with at least one being a direct employer contact
Maryland requires 3 activities per week, including at least 1 direct contact with a potential employer
Texas recommends 3 activities weekly, with strong emphasis on using WorkInTexas.com and attending Workforce Solutions events
Colorado requires 3–5 activities, depending on your circumstances and benefit type
Some states waive or reduce search requirements if you’re job-attached (expecting recall within 16 weeks), union-dispatched through a full-referral union, or enrolled in an approved training program. These exceptions are always communicated in writing; don’t assume you qualify without official confirmation.
Federal employees and contractors may face additional considerations during federal funding disruptions. Check with your state agency for specific guidance.
Sample State Work-Search Requirements (Table Section)
State | Required Activities per Week (2026) | Direct Employer Contact Required? | Common Exceptions | Documentation/Audit Notes |
Washington | 3 activities | Yes, at least 1 | Union attachment, approved training, job-attached | Keep logs for at least 30 days after benefit year ends |
Maryland | 3 activities | Yes, at least 1 | Approved training, employer recall | Subject to audit for up to 2 years; must repay overpaid benefits plus interest |
Texas | 3 activities (recommended) | Strongly encouraged | Union referral, temporary layoff with recall date | Log via WorkInTexas.com; random audits conducted |
Colorado | 3–5 activities | Varies by program | Approved training, work search exemption for some claimants | Maintain records throughout benefit year |
Typical State | 2–3 activities | Often required | Training enrollment, union dispatch, short-term layoff | 30-day to 2-year retention requirements |
Note: Requirements change periodically. Always verify current numbers on your state’s official unemployment website before filing your weekly claim or request payment.
What Actually Counts for AI and ML Professionals
AI engineers, ML researchers, infra engineers, and LLM specialists often engage in activities that don’t fit neatly into traditional unemployment frameworks. Open source contributions, research projects, hackathons, and advanced coursework may or may not count, depending on how directly they tie to your employment goals.
AI-focused job search activities that usually count:
Applying for technical roles: Submitting applications for positions like “Machine Learning Engineer – Ranking,” “Staff Infrastructure Engineer – ML Platform,” or “LLM Ops Engineer” through company ATS systems or job boards
Technical interviews: Participating in phone screens, coding assessments, system design rounds, and take-home challenges for AI/ML positions
Company-hosted challenges: Completing LLM evaluation challenges, ML competitions, or technical assessments when invited as part of a recruiting process
Attending AI-specific hiring events: Participating in virtual AI career fairs, company tech talks with recruiting components, or state workforce-sponsored tech industry events
Industry networking: Conducting informational interviews with staff ML engineers, attending AI/ML meetups, or connecting with hiring managers at target companies
Training activities that often qualify:
Attending a week-long online course on “Advanced Retrieval-Augmented Generation” through a recognized platform
Completing a short MLOps bootcamp with a certificate of completion
Finishing a state-approved AI upskilling program under WIOA Title I-B
Taking instructor-led courses on fine-tuning LLMs, distributed training, or ML infrastructure
Portfolio work that can count when linked to applications:
Building a production-ready RAG demo on GitHub that you reference in cover letter materials and applications
Contributing to high-visibility open source libraries like Hugging Face Transformers or vLLM
Preparing technical write-ups or blog posts that you send to hiring managers as work samples
Purely academic or hobby projects with no connection to your active job hunt may not qualify unless they’re part of a recognized training or workforce program. When in doubt, confirm with your state agency or career coach.
All these activities must be documented with dates, links (GitHub repos, course dashboards), and evidence (emails, certificates) in your weekly log to count toward benefits.
Examples of Valid AI-Focused Work-Search Activities
Here are concrete scenarios showing how to turn real actions into valid work-search log entries:
January 15, 2026: Applied to “Staff ML Engineer, Recommendations” at a Series C startup via their ATS. Saved confirmation email showing job description and submission timestamp.
February 8, 2026: Attended a virtual AI hiring fair hosted by the Washington Employment Security Department. Registered in advance, attended two company sessions (ML infra startup and enterprise AI platform team). Saved registration confirmation and session attendance log.
February 22, 2026: Completed “Prompt Engineering for Production LLMs” course on Coursera. Downloaded the completion certificate and logged the activity with the course URL and date.
March 3, 2026: Participated in a structured mock interview with a state-approved workforce partner. Topics covered: data structures, distributed training architectures, and system design for recommendation systems. Received email confirmation from counselor.
March 10, 2026: Completed Fonzi’s onboarding survey and technical profile to unlock Match Day invites. This counts as registering with a private employment agency, logged with date and platform confirmation.
March 15, 2026: Had an informational interview with a Staff MLE at a target company, discussing their ML platform architecture and open roles. Logged contact name, company, date, and follow-up email thread.
What doesn’t count without proper context:
Casually experimenting with a new model at home with no intent to use it in job applications. Playing with open source tools for personal interest. Browsing online job boards without actually applying. These activities lack the employment connection that makes them a valid work search.
Using Fonzi as a High-Signal, Compliant Job Search Channel
Fonzi is a curated talent marketplace built specifically for AI engineers, ML researchers, infra engineers, and LLM specialists. Unlike generic job boards where you compete with hundreds of applicants for roles that may not even exist, Fonzi partners with top-tier, high-intent employers actively hiring for technical AI positions.
Why Fonzi works for your job search:
Pre-screened on both sides: Fonzi vets both candidates and companies, reducing spammy outreach, ghost jobs, and the endless application black hole that plagues 75% of generic platform submissions
Focused on serious AI roles: Companies on Fonzi are hiring for model training, applied ML, LLM ops, AI infrastructure, and related technical positions, not generic software roles mislabeled as “AI”
Structured, time-bound process: Application review happens within days, with specific Match Day windows, making it easy to document activities with precise dates
Onboarding steps that count as job search activities:
Completing a detailed technical profile with your skills, experience, and target roles
Uploading or linking to GitHub repos, publications, and portfolio projects
Specifying target compensation bands and work preferences (remote, hybrid, relocation)
Enabling employers to discover and match with your profile
Using Fonzi aligns with state search requirements because it’s functionally equivalent to registering with a private employment agency, making direct employer contacts, and participating in structured hiring events. Log your Fonzi activities just as you would any other work search channel.
How Fonzi’s Match Day Works (and Why It Matters for Benefits)
Match Day is a scheduled event, typically a specific week each month, where pre-vetted companies and pre-vetted AI talent are algorithmically matched based on skills, interests, location preferences, and compensation expectations.
Here’s how it works:
You complete your Fonzi profile and indicate you’re ready for matching
During Match Day, Fonzi’s algorithm identifies mutual fits between your skills and company needs
You receive a concentrated set of high-signal interview requests or introductions from companies actively hiring
Each interaction, such as reviewing matched roles, responding to interview invites, and completing technical screens, is timestamped
For unemployment documentation, Match Day is valuable because a single week can generate 3–5 qualifying activities: direct employer contacts, scheduled interviews, and formal hiring event participation. Everything is logged with dates and company names, making your work search log straightforward to complete.
The advantage over generic online job boards is clear: instead of scattershot applications with 5% response rates, Match Day produces fewer, better-targeted conversations that are easy to document and more likely to result in a job offer.
How Fonzi Uses AI in Hiring, and Why It’s Different
Many job seekers worry about black-box hiring tools and biased screening algorithms. In 2026, 68% of firms use AI tools in hiring (per Gartner research), yet 52% of candidates fear dehumanization in the process. This concern is valid; poorly implemented AI can filter out qualified candidates based on irrelevant factors.
Fonzi takes a different approach.
How Fonzi uses AI responsibly:
Matching, not rejection: AI clusters candidates by skills, experience levels, and preferences, then surfaces relevant matches to human recruiters. The algorithm helps with discovery, as it doesn’t automatically reject anyone.
Skills-based matching: The system prioritizes practical evidence like GitHub repos, published models, on-call SRE experience for ML infra, and real project outcomes over pedigree or keyword-stuffing.
Bias reduction: Fonzi anonymizes or down-weights sensitive fields, calibrates matching models against fairness metrics, and regularly audits outputs. Internal benchmarks show a 35% reduction in gender bias compared to traditional resume screening.
Comparison with traditional ATS:
Most ATS systems auto-filter based on keywords, years of experience, or school names. This creates false negatives for candidates with non-traditional backgrounds or those who simply didn’t use the right buzzwords. Fonzi’s approach surfaces candidates based on what they can actually do.
Humans still make the decisions:
On Fonzi, hiring managers and recruiters review profiles, schedule interviews, and evaluate candidates. AI helps them focus on the most relevant matches, but every hiring decision involves human judgment.
This human-centered approach creates a clear audit trail useful for unemployment documentation: you can see which companies you were matched with, which interviews you completed, and what stages you reached.
Protecting Candidate Experience and Privacy
Fonzi is designed to avoid the pain points that make modern job searching miserable:
Profile visibility control: You decide which companies can see your profile and set preferences for remote work, relocation, and role types
Privacy-first data handling: Only job-relevant data is used in matching. Fonzi doesn’t sell candidate data to third-party advertisers.
Deactivation and deletion options: Once you land a new job, you can deactivate or delete your profile completely
Anti-ghosting expectations: Fonzi sets clear response time expectations with partner companies, reducing the endless waiting that characterizes most job searches
Transparency dashboards: Unlike opaque AI tools, Fonzi shows match logic so you understand why certain opportunities are surfaced
If your unemployment agency ever questions your use of specialized platforms, you can confidently explain that Fonzi is a legitimate private employment agency using ethical AI practices to connect technical candidates with verified employers.
Documenting Your Job Search: Logs, Proof, and Audits
Accurate documentation is as important as the search itself. States can audit your activities months or even years after you receive benefits. If you can’t prove you completed your required activities, you may have to repay benefits, sometimes with interest and penalties.
Common documentation requirements:
Keep a weekly log of at least 3–5 activities (depending on your state)
Record employer/company name, role title, date, type of contact, and outcome for each activity
Retain records for at least 30 days after your benefit year ends, as some states require up to two years
Forms of proof to save:
Application confirmation emails with timestamps
ATS screenshots showing submitted applications
Job fair or hiring event registration confirmations
Course payment receipts and completion certificates
GitHub commit history tied to portfolio projects referenced in applications
Letters from unions or workforce counselors
Calendar entries for interviews and appointments
Email threads from informational interviews
Simple tracking tools:
A spreadsheet with columns for date, employer, job title, activity type, proof link, and outcome
Your state’s workforce portal (like Maryland Workforce Exchange or WorkInTexas.com)
A personal tracker app synced with your calendar
For Fonzi activities, write entries in plain, verifiable terms:
“03/07/2026 – Responded to Match Day interview request from Company X for Senior LLM Engineer role via Fonzi; scheduled 03/10/2026 video interview. Proof: email confirmation and calendar invite.”
Warning: If an audit finds you didn’t complete or can’t prove required activities for certain weeks, you may have to repay your weekly benefit for those weeks, plus interest, and potentially face penalties that affect your good standing.
Sample Weekly Job Search Log for an AI Engineer
Here’s a compliant one-week log for an unemployed AI engineer in April 2026:
Week of April 1–5, 2026:
April 1: Applied to “Machine Learning Engineer, Personalization” at TechCorp via company careers page. Activity type: Application. Proof: Email confirmation, screenshot of ATS submission.
April 2: Attended Virtual AI Hiring Fair hosted by state workforce agency. Participated in sessions with three companies. Activity type: Hiring event. Proof: Registration email, attendance confirmation.
April 3: Completed “Scaling LLM Inference” workshop (4 hours) on recognized training platform. Activity type: Approved training. Proof: Completion certificate, course dashboard screenshot.
April 4: Participated in Fonzi Match Day; completed technical phone screen with matched company for “Staff ML Engineer, Infrastructure” role. Activity type: Interview. Proof: Email invitation, calendar entry, interviewer name.
April 5: Conducted informational interview with Staff MLE at target company to learn about their ML platform and upcoming openings. Activity type: Networking/employer contact. Proof: Email thread, LinkedIn message, calendar entry.
This example demonstrates how a single week can easily meet a 3-activity requirement while documenting a realistic, high-quality AI job search.
Staying Eligible: Accepting Offers, Exceptions, and Edge Cases
Unemployment benefits require not just actively searching for work, but also accepting suitable work when offered. Refusing a reasonable job offer without good cause can result in benefit denial, repayment requirements, or disqualification from future weeks.
What counts as “suitable work” for AI roles:
Comparable pay to your prior position (states allow some reduction over time, typically 10–25% after several weeks of unemployment)
Similar level of responsibility and skill requirements
Matching technical skills, moving from NLP to recommendations is reasonable; moving to unrelated manual labor likely isn’t
Reasonable commute or remote arrangements consistent with your stated availability
Full-time work or part-time, based on your job search preferences and stated availability
Important considerations:
States like Maryland and Washington allow labor departments to review offer refusals
Employers can report when candidates decline suitable jobs, triggering investigations
Refusing a job offer from a Fonzi-matched company without good cause could impact your benefit eligibility
Common exceptions to ongoing work search requirements:
Being job-attached with a confirmed return date (typically within 16 weeks)
Being union-dispatched through a full-referral union system
Participating in an approved full-time training program
Having a work search exemption due to documented circumstances (varies by state)
Handling delayed start dates:
If you accept a job starting in three weeks, you must continue logging job search activities until your first day of paid work. Your unemployment claim remains active until you’re actually on payroll.
Report your start date promptly to your state agency. Stop certifying for weeks once you’re fully employed, or your income exceeds your weekly benefit threshold.
Handling Remote, Contract, and Gig Work
Remote-first AI roles, contract positions, and gig work create complexity for unemployment claims. Here’s how to handle them:
Short-term or part-time contracts:
A 3-month LLM consulting engagement at 20 hours/week may reduce but not eliminate your weekly benefit amount. Report the hours and pay accurately; partial benefits may still be available depending on your earnings relative to your weekly benefit threshold.
Gig work (ride-share, delivery apps):
Gig work is typically treated as self-employment. States may require you to:
Report all earnings with dates and approximate hours
Continue your job search for positions in your primary field
Keep screenshots or invoices as proof of work and income
Some states count gig work as a valid employment activity when it’s part of seeking longer-term work, but this varies. Check your state’s guidelines on app-based delivery and driving income.
Remote and contract roles via Fonzi:
Positions offered through Fonzi or similar platforms are typically considered suitable work if they meet your skills and stated pay criteria. Declining them without good cause, even if they’re contract or remote, can impact your benefit eligibility.
Preparing for AI Job Interviews While on Benefits
Strong interview preparation helps you land roles faster, shortening your unemployment period and reducing stress. Time spent in formal prep contexts can sometimes be logged as work search when tied directly to job applications.
Preparation activities that may qualify:
Participating in mock interviews with recognized partners (university alumni offices, state workforce centers, approved career services)
Attending interview skills webinars sponsored by workforce agencies
Completing system-design or coding workshops specifically aimed at technical roles
Meetings with a career coach focused on interview strategy
Core preparation for AI/ML interviews:
Refresh fundamentals: data structures, algorithms, ML theory, distributed systems basics
Practice coding questions on platforms like LeetCode, focusing on problems relevant to ML engineering
Walk through your recent projects end-to-end, ready to explain trade-offs in model architecture, training infrastructure, and deployment
Rehearse system design for ML: feature stores, model serving, monitoring pipelines, guardrails for LLMs
Build or update your portfolio:
Fine-tune an open-source LLM on domain-specific data
Build a scalable feature store or model serving system
Implement an evaluation framework for generative models
Document an on-call monitoring system for production ML
For each interview or formal prep activity, log the date, company or provider, and topic covered (e.g., “LLM system design, monitoring, and guardrails for production deployment”).
Showcasing Your Skills on Fonzi
Structure your Fonzi profile to maximize interview invites:
Technical signals to highlight:
Specific tech stacks: PyTorch, JAX, Ray, Kubernetes, Triton, TensorRT
Domain expertise: search ranking, recommendations, generative AI, computer vision, NLP
Impact metrics: “Improved ranking CTR by 7%,” “Reduced inference latency by 40%,” “Cut model training costs by 30%”
Add concrete evidence:
Links to GitHub repos with well-documented projects
Research papers (NeurIPS, ICML, ACL, or preprints)
Kaggle competition results and rankings
Technical blog posts explaining complex topics accessibly
Fonzi’s matching algorithm uses these signals to align you with companies building real AI products, such as model-training platforms, RAG systems, AI copilots, and infrastructure tooling, rather than generic software roles.
Updating your profile counts:
Significantly enhancing your Fonzi profile (after finishing a new project or course) can be logged as a job search activity in some states when done with the explicit goal of securing employment.
Bookmark or export your Fonzi activity timeline as a reference when compiling weekly or audited work search logs.
Turning a Stressful Search into a Structured Plan
Unemployment benefits require a disciplined, well-documented job search, but for AI and ML professionals, that doesn’t mean limiting yourself to outdated definitions of “applying.” Building a RAG demo, completing an MLOps course, or attending a virtual hiring event can all qualify when they’re clearly tied to your goal of landing a new role and properly logged. The key is intent and documentation: showing that each activity moves you closer to paid employment in your field.
This is where platforms like Fonzi help bring order to an otherwise messy process. Fonzi turns a noisy job search into a focused, high-signal one by connecting AI engineers and recruiters with vetted companies that are actively hiring. Every application, interview, and conversation is timestamped and easy to track, which simplifies work search reporting while keeping momentum high. Used responsibly, AI reduces friction and frees everyone up to focus on real conversations and real problems. If you want a smarter, compliant way to job hunt, joining Fonzi and preparing for the next Match Day is an efficient way to move forward without wasting time on low-signal applications.
