What to Do If You Get Laid Off: A Step-by-Step Game Plan
By
Liz Fujiwara
•
In 2023 and 2024, thousands of AI engineers, ML researchers, and infrastructure specialists were laid off across companies like Google, Meta, Amazon, and many smaller AI startups. What was once a hot market for machine learning talent has become more uncertain, and layoff anxiety has become a rational response to shrinking teams and unclear hiring plans.
Many engineers now recognize the pattern of hiring freezes, project cancellations, and reorganizations that leave teams under review. If you are feeling uncertain, starting a discreet job search while still employed can help you regain control over your career path.
Key Takeaways
Tech layoffs across AI, ML, and infrastructure teams have made it wise to quietly prepare by updating your resume, reaching out to your network, and exploring platforms like Fonzi before any announcement happens.
Applying while still employed improves your negotiating power, protects your mental health, and strengthens your financial position if a layoff does occur.
This article provides a step-by-step job search plan, compares different job search paths, and answers common questions about layoffs, benefits, severance, and how AI is used in hiring.
Should You Apply for New Jobs If You’re Worried About Layoffs?
Yes, it is usually wise to start applying before layoffs are announced, especially in cyclical or VC-funded tech environments. The job market for AI professionals has shifted from a reliable numbers game to a more targeted process where preparation matters more than ever.
Starting early matters for several key reasons:
You avoid competing with hundreds of other laid off employees who enter the market at the same time
You can be selective about roles rather than accepting the first offer out of desperation
You maintain negotiating leverage by interviewing while still employed
You protect your mental health by taking action instead of waiting anxiously
Watch for these concrete signals that layoffs may be coming:
Frozen headcount since mid-2024, with no new backfills approved
Travel, training, and conference budgets slashed across the board
Leadership hinting at “efficiency,” “runway,” and “doing more with less” in all-hands meetings
Repeated reorgs of AI/ML teams without clear product rationale
Your company’s competitors announcing significant cuts
Looking for a new job does not mean you are disloyal. It is standard risk management in fields where projects and priorities can change quickly. Company restructuring happens for business reasons that have nothing to do with employee performance. If you suspect a Q2 or Q4 layoff cycle based on budget cuts and hiring patterns, consider planning a 60 to 90 day quiet job search sprint starting now.
Step-by-Step Game Plan If You’re Nervous About Being Laid Off
This section works as a checklist an AI or ML engineer can follow over two to four weeks. Each step builds on the previous one, moving you from worried observer to prepared candidate.
Step 1: Assess your financial runway. Calculate exactly how many months you could sustain your current lifestyle without income. Factor in emergency savings, upcoming RSU vesting dates, and any bonus schedules.
Step 2: Gather key documents and accomplishments. Before you lose access to internal systems, document your achievements: performance reviews, project metrics, architecture diagrams (sanitized of proprietary information), and colleague contact information.
Step 3: Discreetly update LinkedIn and GitHub. Make subtle improvements to your profiles without announcing “Open to Work” if your company monitors these changes. Pin your strongest repos—MLOps tooling, fine-tuning scripts, open-source contributions.
Step 4: Refresh your resume with impact metrics. Quantify everything: model latency reductions, infra cost savings per month, throughput gains on LLM inference clusters, or shipping a retrieval-augmented generation feature.
Step 5: Map your target roles. Create a list of 15–20 companies working on problems you care about. Include AI-first startups, scale-ups, and established tech companies with strong ML infrastructure teams.
Step 6: Start low-risk networking. Reconnect with former colleagues, attend virtual meetups, and engage thoughtfully on technical content. This often uncovers roles never posted publicly.
Step 7: Apply selectively through high-signal channels. Rather than mass applications, focus on curated marketplaces like Fonzi, warm referrals, and targeted cold outreach to hiring managers.
Step 8: Prepare for interviews. Dedicate specific hours each week to technical practice, behavioral question prep, and project deep dives.
Step 9: Plan for different outcomes. Run scenarios for staying (if layoffs don’t happen), leaving voluntarily (if you get a great offer), and being laid off (so you know exactly what to do).
Step 10: Protect your mental health. Set boundaries around job hunting activities and maintain routines that keep you grounded.
This game plan is designed for AI engineers, ML researchers, infrastructure or platform engineers, SREs, and LLM specialists. The examples throughout, such as optimizing training pipelines on A100s or launching RAG features, reflect the type of work many engineers in these roles perform.
Stabilize Your Finances Before You Jump
Financial clarity reduces panic and lets you make better decisions about how aggressively to pursue job hunting or negotiate a potential severance package. Before you send a single application, get honest with yourself about your numbers.
Start by estimating 3–6 months of essential expenses:
Fixed costs: rent/mortgage, utilities, loan payments, health insurance coverage
Variable costs: food, transportation, subscriptions
Buffer for unexpected expenses
Next, check your current financial position:
Cash in your bank account and liquid savings
Upcoming RSU vesting dates (many engineers lose unvested equity when leaving)
Bonus schedule and whether you’d forfeit by leaving before a certain date
Status of your emergency savings fund
Run scenarios for staying until a layoff versus leaving earlier for a better offer. Sometimes waiting for a layoff notice makes financial sense because employees may receive severance and qualify for unemployment benefits. Other times the stress is not worth it, and a proactive move leads to a better opportunity.
In mid to large tech companies, severance packages often follow a formula such as two weeks of pay per year of service, sometimes with a minimum around eight to twelve weeks. Some packages also include temporary health coverage extensions through COBRA, though employees usually pay much of the premium. Do not rely on severance based only on rumors. These packages are not guaranteed by labor laws in most states, and your employment contract may specify different terms.
For unemployment insurance basics, many states offer up to 26 weeks of unemployment benefits, though amounts vary. Each state runs its own program with different eligibility and payment calculations. Voluntary resignation usually disqualifies you from receiving unemployment benefits, while being laid off typically does not. Check your state’s unemployment office website for the exact rules.
If layoffs seem possible, avoid taking on large new financial commitments. This is usually not the time to buy a house, finance a new car, or take on new debt that assumes your current income will continue.
Quietly Refresh Your Professional Story (Resume, LinkedIn, GitHub)
Before sending out applications, AI and ML talent should make sure their public profile clearly communicates their niche and recent impact. Your professional story is your first impression, so make it count.
Update your resume with concrete impact metrics:
Model latency reduced by X% through inference optimization
Infrastructure cost savings of $Y per month via better resource allocation
Throughput gains on LLM inference clusters (tokens/second improvements)
Successful deployment of production ML systems serving Z requests per day
Open-source contributions and their adoption metrics
For LinkedIn, approach updates strategically. If your company is small or leadership actively monitors employee updates, avoid switching to “Open to Work” status immediately. Start with subtle changes such as updating your job description, adding recent skills, or refreshing your summary. These changes do not trigger notifications and are less likely to alert your current employer.
Polish your GitHub or portfolio with intention. Pin 2–4 serious repos that demonstrate real engineering depth:
MLOps tooling you’ve built or contributed to
Fine-tuning scripts for LLMs (Llama, Mistral, etc.)
Open-source contributions to frameworks like PyTorch, JAX, or Ray
Infrastructure automation (Kubernetes operators, Terraform modules)
Remove or archive half-finished side projects that don’t represent your best work. Prospective employers reviewing your GitHub want signal, not noise.
Add a short three to four line “About” summary tailored to AI roles. Include the stacks you use such as PyTorch, JAX, Ray, Kubernetes, and Terraform, the domains you have worked in such as recommendation systems, multimodal models, or NLP infrastructure, and the type of role you want next.
Choosing Where to Apply: Traditional Job Boards vs. Curated Marketplaces
AI and infrastructure candidates are overwhelmed by low-signal postings on big boards like LinkedIn and Indeed. The sheer volume of irrelevant roles, recruiter spam, and generic ATS portals makes job hunting feel like shouting into a void. You need channels that respect your time and understand your specialized skills.
Here’s how different job search paths compare for AI/ML professionals:
Approach | Signal Quality | Time Investment | Candidate Experience | How AI Is Used | Typical Outcomes for AI/ML Roles |
Mass Job Boards (LinkedIn, Indeed) | Low; flooded with generic postings and recruiter spam | High; requires filtering through hundreds of irrelevant roles | Poor; generic applications, automated rejections, ghosting | Keyword matching, automated screening that often misses nuanced AI skills | Many applications, few responses, long timelines |
Cold Outreach / Networking | Medium to High; depends on your network quality | Medium; requires relationship building over time | Variable; can be excellent with warm intros, frustrating otherwise | Minimal AI involvement; relies on human connections | Strong when it works; 40% of offers come from human connections |
Curated Platforms like Fonzi | High; vetted companies actively hiring AI/ML talent | Low; detailed profile once, then receive matched opportunities | Strong; structured process, clear expectations, reduced noise | Skills-based matching, transparent rationale, human oversight | Faster timelines, higher conversion rates, better fit |
How Fonzi Uses AI in Hiring Without Turning You Into a “Data Point”
Many engineers have learned to distrust AI in hiring. Opaque ATS scoring, arbitrary keyword filters, and generic coding tests that do not reflect real work have created frustration. Too many systems treat candidates as data points rather than people with unique skills and career goals.
Fonzi uses AI differently, focusing on creating clarity rather than adding confusion:
Skills parsing and normalization: The system intelligently groups related experience. “Llama-2 fine-tuning,” “LoRA,” and “PEFT” all get recognized as practical LLM fine-tuning experience, rather than being treated as separate unrelated keywords.
Multi-dimensional matching: Candidates are matched to roles based on tech stack, seniority level, compensation expectations, and product interests; not just job title keywords.
Transparent match rationale: You can see why you’re being matched. For example: “Your experience building vector search over 500M documents matches Company X’s RAG infrastructure role.”
Humans remain central to Fonzi’s process. The team vets every company on the platform, reviews edge cases the matching model cannot interpret, and steps in when matches are borderline or unusual. This differs from typical AI hiring systems where blind resume screening leads to automated rejections and candidates never know why they were not considered.
Regarding bias, Fonzi’s matching focuses on demonstrated skills and outcomes rather than pedigree alone. This approach can support fairer consideration for candidates outside FAANG or top-tier PhD programs. Your ability to ship production ML systems matters more than where you went to school.
Inside Fonzi’s Match Day: A High-Signal Way to Get in Front of Top Companies
Match Day is a periodic event (typically every 2–4 weeks) where Fonzi presents a curated cohort of AI/ML candidates to a set of vetted companies simultaneously. Think of it as a structured introduction that compresses months of back-and-forth into days.
Here’s how the candidate experience works:
Profile once, match many: You fill in a detailed profile covering your technical skills, work preferences (remote vs. hybrid, preferred locations, salary bands), and the problem domains that interest you most.
Wait for curated introductions: Rather than endlessly applying, you opt into Match Day and let the matching work for you.
Receive clear, actionable outreach: On Match Day, companies reach out with specific role details, compensation ranges, and interview process outlines rather than vague requests to chat.
Compressed timelines: Many candidates move from introduction to onsite interview loops within 1–2 weeks instead of months of drawn-out processes.
Applying While Still Employed: Tactics, Timing, and Discretion
Most readers will still be in their current role while they test the market. This requires some extra care to avoid alerting your current employer or creating awkward situations.
Practical tactics for discreet job searching:
Use personal email and phone for all job search communications, never your work accounts
Never search on company devices or networks; your employer may monitor this
Schedule interviews early morning (before work), late afternoon (after typical hours), or during PTO days
If you need to take calls during work hours, use neutral language like “appointment” rather than “interview”
Keep your calendar blocks vague
How much should you tell your manager or peers? Generally, keep plans private until you have an offer in hand or are in a late-stage interview process. Exceptions might include trusted mentors outside your direct reporting line who can provide references or advice. Even well-intentioned managers may treat you differently once they know you’re looking, so discretion protects your current standing.
Prioritize quality over quantity. For senior AI/ML roles, 5–10 targeted applications per week through high-signal channels will outperform 50 generic submissions. Each application should involve tailoring your resume, researching the company, and potentially reaching out to employees for insights.
Consider “testing the waters” first with low-stakes interactions: recruiter screens, informational chats with former colleagues at target companies, and industry meetups. These help you calibrate current compensation expectations and market demand before committing serious interview time.
Explaining Layoff Risk or Past Layoffs to Future Employers
Many candidates worry that mentioning layoffs will make them seem weak or risky, but tech hiring managers know layoffs happen for business reasons beyond an employee’s control. The key is to communicate the situation clearly and quickly highlight your value.
Here are scripted example answers that work:
For “Why are you looking to leave?” when worried about layoffs:
“I’ve had a great experience at [Company], and I’m proud of what we built. That said, the company has gone through a few restructurings this year, and I want to be thoughtful about my next move. I’m particularly excited about [Target Company] because of [specific reason related to their AI work].”
For “Why did you leave your last role?” when already laid off:
“The company went through a significant restructuring as part of broader cost reduction efforts; my entire team was affected. Before that, I led [specific accomplishment with metrics]. I’m now looking for a role where I can bring that same impact to [type of work].”
For explaining short tenures (9–12 months) in 2022–2024:
“I joined [Startup] excited about their vision for [AI product]. Unfortunately, the funding environment shifted, and they had to reduce headcount significantly. In my time there, I still managed to [concrete achievement]. I’m now looking for a company with a more stable foundation where I can contribute long-term.”
Be honest without oversharing. Mention reorgs, shifting priorities, or funding constraints without criticizing your previous employer or colleagues. Unlike layoffs, which are no-fault separations, being fired usually relates to performance or misconduct, and hiring managers understand the difference.
Most prospective employers will not penalize you for macroeconomic events. Many hiring managers have experienced layoffs themselves or have had to make difficult team cuts. A straightforward, non-defensive explanation followed by enthusiasm for the new opportunity is exactly what they want to hear.
Using AI Tools to Run a Smarter Job Search (Without Losing Your Voice)
Many candidates now lean on ChatGPT-style tools to draft resumes, emails, and practice questions, but worry about sounding generic. The tools can help; if you use them thoughtfully.
Effective ways to use AI in your job search:
Generate first drafts: Let AI create initial versions of resume bullets, cover letters, and LinkedIn summaries that you then heavily edit
Summarize job descriptions: Quickly extract the key requirements and responsibilities to tailor your applications
Prepare question lists: Generate potential interview questions based on the role and company
Simulate mock interviews: Practice behavioral and technical explanations before the real thing
The critical step is editing everything to sound like you. Include your specific metrics, tech stacks, and concrete examples from real 2022 to 2024 projects. A generic bullet about “improving model performance” becomes impactful when it reads “reduced GPT-4 inference latency by 40 percent through batching optimizations and KV-cache improvements, saving $12K per month in compute costs.”
Fonzi’s platform complements these efforts by translating your structured profile, including skills, preferences, and past outcomes, into high-signal matches without requiring you to send dozens of applications. The AI handles matching; you focus on the conversations that matter.
One important note: don’t outsource your entire job search to AI. Personal outreach, authentic communication, and genuine relationship-building still matter most. Former colleagues who remember working with you, hiring managers who sense your real enthusiasm, and recruiters who trust your background; these human connections drive results that AI cannot replicate.
Taking Care of Your Mental Health During Layoff Anxiety
Even before a layoff happens, constant fear can drain focus, creativity, and confidence. The uncertainty is exhausting in ways that are hard to explain to people who haven’t experienced it. Taking care of yourself is essential for performing well in interviews and making good decisions.
Practical supports that help:
Talk with trusted peers in Slack communities, Discord servers, or local meetups; people who understand what you’re going through
Limit doom-scrolling layoff news and Blind threads; set specific times for checking updates rather than constantly refreshing
Create specific weekly “job search office hours” to contain the activity and worry rather than letting it bleed into every moment
Maintain routines outside work: exercise, hobbies, time with friends and family
Remember that your self-worth is not tied to any one employer, funding round, or job title. Whether you’re a “Senior Staff Engineer” or between roles, your skills and experience remain valuable. The tech industry’s volatility reflects macroeconomic conditions, not your individual worth.
Concrete self-care practices that resonate with engineers:
Time-boxed deep work on side projects that genuinely interest you (not just resume-padding)
Regular exercise routines; even 30 minutes of walking helps regulate anxiety
Digital detox evenings where you step away from screens entirely
Maintaining sleep hygiene even when stress makes it difficult
If anxiety or depression starts affecting sleep, relationships, or daily functioning, reach out for professional support. Many health insurance plans cover therapy, and some employers offer EAP services with counseling sessions. Taking care of your mental health is not a weakness; it is how you remain effective during challenging periods.
Conclusion
Worrying about layoffs is rational, but staying passive is optional. By preparing early, stabilizing finances, updating your professional story, and engaging with high-signal job search channels, AI and ML professionals can often upgrade their roles rather than simply replace them after a cut.
Modern hiring uses AI, but not all platforms are equal. Look for tools like Fonzi that make the hiring process clearer and more human, connecting you with companies that need your skills instead of burying you in an opaque ATS.
The market is challenging but full of AI-first companies seeking strong engineering talent. Your experience building production systems, optimizing inference pipelines, and shipping real ML products is exactly what they need, and the key is positioning yourself to find these opportunities before you have to.
FAQ
What does it mean to get laid off vs. getting fired?
Should I start applying for new jobs if I’m worried about layoffs at my company?
What should I do immediately after getting laid off?
How do I explain a layoff to a future employer without it hurting my chances?
What benefits or severance am I entitled to if I get laid off?
