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What’s up everyone, today we continue with part 2 of a 3 part series we’re calling The Martech Job Hunt Survival Guide. Part 2 is: How to stand out as a candidate with AI prep, portfolios and tools.

Check out part 1: How to find hidden job opportunities.

Summary: Phil and Darrell spent this episode breaking down what actually moves the needle when you’re searching for a role: building the portfolio that almost no marketing ops professional bothers to save, navigating the AI experience question, knowing when to take a contract role instead of holding out, and skipping the AI job-search tools that make you look like everyone else. The honest observations from Darrell’s own recent job search make this one worth listening to, including why the colleagues most reluctant to make a lateral move are still searching months later.

In this Episode…

• Why Hiring Managers Can’t Actually Evaluate Your AI Experience
• How to Build a Marketing Ops Portfolio When Your Work Is Buried in Tools
• Why Creating LinkedIn Content Works Even When Nobody Is Watching
• What Hiring Managers Notice First on Your LinkedIn Profile
• Why Contract Work Is a Strategic Move for Marketing Ops Job Seekers Right Now
• Which Job Search Tools Help and Which Ones Waste Your Time
• How a Video Introduction or Visual Resume Gets You Into the Next Round

Why Hiring Managers Can’t Actually Evaluate Your AI Experience

Every marketing ops job posting in 2026 has the same line buried somewhere in the requirements: “proven experience delivering results with AI.” Walk into any interview and within the first few minutes someone will ask you to describe what you’ve actually done with it. That question sounds reasonable until you realize the person asking usually has no idea what a good answer looks like.

Darrell came out of a recent job search with a clear read on this. The interview questions had shifted entirely. The old MarTech interview, the 1 that asks about your tool stack and campaign history, has been replaced. AI is now the primary filter. Companies want proof of results. But AI-driven marketing ops, as an actual practice, barely existed 3 years ago. Phil put the absurdity into 4 words: “5 years of AI experience.” Everyone in hiring knows it’s a joke. They’re writing it anyway.

The talent pool has gotten harder at the same time. Amazon’s most recent layoffs displaced over 10,000 people. Layoffs at Google and across the broader tech sector added more. You’re competing against that cohort now, which means the undifferentiated application is in worse shape than it’s ever been. Everything has to be sharper.

The hiring managers writing “proven AI experience required” often can’t define what good AI usage looks like for a marketing ops role. They’re expressing a priority while lacking any rubric to test it. When they ask the interview question, they’re listening for someone who sounds like they know what they’re talking about. Most candidates coming through don’t. You feel it during prep, that uncomfortable awareness that you don’t know exactly what they want from you. The honest truth is they don’t either.

That gap is yours. Research what AI actually does in marketing ops workflows:

• lead scoring automation
• campaign orchestration
• data governance
• intent signal processing

Build one small example and frame your existing work in terms of where AI would fit and how you’d measure it. You can position yourself as a credible AI enthusiast with very little preparation, because the bar inside most marketing orgs is low and most candidates aren’t clearing it.

“The hiring managers don’t really know what to even do with AI. So this is your advantage. You can now do the research ahead of time, understand what the most common AI use cases are, and really weave your story into it, because they’re so inexperienced and so unfamiliar, you have a chance to really prove yourself as an AI expert, or at least an AI enthusiast, with very little preparation.”

The industry required AI fluency before building any way to evaluate it. That’s not a problem. For candidates willing to do the homework most skip, it’s the whole advantage.

Key takeaway: Research 3 specific AI use cases in marketing ops before your next interview: lead scoring automation, campaign workflow agents, and CRM data deduplication are good starting points. Prepare 1 concrete story connecting 1 to work you’ve done or would do. If you haven’t built anything yet, describe the workflow you’d build and how you’d measure its impact. Candidates who speak specifically and confidently about AI applications win these conversations, because they’re often the only ones in the room who prepared.

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How to Build a Marketing Ops Portfolio When Your Work Is Buried in Tools

Most marketing ops professionals have spent years doing meaningful, complex work. They’ve built lead scoring models, managed platform migrations, architected multi-channel campaign workflows. And if you asked them to show you any of it in an interview, most couldn’t. The templates are gone and the results are a rough number someone mentioned once in a meeting.

Darrell has sat on the interviewer side of enough conversations to be direct: the portfolio problem in marketing ops is almost universal. Candidates describe their work verbally, and the person asking often can’t follow it. There’s nothing to point to, nothing to walk through, nothing that makes the experience tangible. In a field full of technical, visual, process-driven work, almost no one has anything to show.

“Marketing ops people, Martech people, they usually have nothing. Nothing. They don’t save anything. In interviews people say, what have you worked on? You have to describe it verbally and the interviewer likely doesn’t get it.”

Show Your AI Tinkering

The bar to stand out is genuinely low. Darrell’s starting point: if you’ve built a custom GPT, a Google Gem, or a basic AI agent using Zapier, that alone puts you ahead of most candidates. It takes about 10 minutes to build 1. It demonstrates something concrete about how you think and work.

If you’re playing around with Claude Code or Cowork for example, document your tinkering, add screenshots of the build to a slide deck and show the end product our output. Show how you’re structuring your file system and memory.

The Case Study Portfolio

The same logic applies to documentation that almost no company does well: a clean diagram of your current or former tech stack, before-and-after views of a migration you led, a lead scoring template, a product requirements document for a tool evaluation. These are ordinary outputs of the job. Almost no one saves them.

Phil’s preferred format is the case study. Take a project you led, strip the confidential details, and walk through it as if you were an outside consultant brought in to solve the problem. What was the situation before you arrived? What did you do? What did it look like after? Specific numbers and percentages help, but they’re not required. A clean diagram showing a tech stack before and after a migration, or a flow chart of a campaign workflow you built, communicates competence without a single metric.

For quantifying impact when the numbers are murky, Darrell’s suggestion is to use AI to reverse-engineer the math. If you cut campaign launch time by 20%, work backward through campaigns per quarter, leads generated, and pipeline influenced. You can build an intelligent, defensible estimate, and most candidates don’t even try.

The format doesn’t need to be elaborate. A Google Slides deck linked from your resume, tracked with a Bitly vanity URL so you can see who opens it, is more than enough. The bigger benefit of building a portfolio at all is what it does to your interview prep. Reviewing your own work, articulating outcomes, distilling a project into a problem-action-result narrative means you’ve already done the thinking before anyone asks the question. Phil’s point: the exercise of building the portfolio and the exercise of preparing for interviews are the same exercise.

Key takeaway: Start with your most recent project and build 1 case study: the problem you walked into, what you built or changed, and the measurable outcome. Add a tech stack diagram if you don’t have 1. Link both as a Google Slides deck from your resume and track opens with a Bitly URL. Even a basic portfolio puts you in a category most marketing ops candidates never reach.

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Why Creating LinkedIn Content Works Even When Nobody Is Watching

You don’t need to be the number 1 marketing ops influencer to start posting some thoughts on LinkedIn. The fear that stops most marketing ops professionals from posting is the same: you imagine the post going nowhere. 3 likes, a comment from someone you already know, a few days of quiet. Happens to all of us, even Darrell. For someone who hasn’t built a following, it feels like performing to an empty room. That fear is real but it’s pointed at the wrong metric entirely.

Phil’s reframe is worth sitting with. Total reach and engagement are the wrong things to optimize for when you’re looking for a job. A single post about your experience migrating from 1 marketing automation platform to another might reach 200 people. But if 1 of those 200 is a hiring manager actively going through that same migration right now, that post is worth more than any paid placement. You only need 1 person to get real value from what you write. It’s not about how many people you reach, it’s about the 1 person for whom it lands.

Darrell’s observation from interviewing candidates and from his own job search puts the bar in context. The majority of LinkedIn profiles in marketing ops are walls of text. Job titles, company names, vague descriptions of what someone was responsible for. If your profile has a single article about attending a MarTech or Inbound conference, you already look different. The difference shows immediately. It signals active engagement in the field and that you’re a real person with actual opinions, not just a resume reformatted as a profile.

The most accessible content approach Darrell described requires no creative brief and no audience. Teach what you’re learning at work. If you’re building a MarTech product requirements document because multiple teams need access to a social tool, write down the experience: what you tried, what didn’t work, where AI helped. It doesn’t have to be elaborate. Darrell’s honest observation: when he tries to teach something to others, he learns it more deeply himself. The act of writing the post is the benefit, independent of whether anyone reads it.

“Just teach what you’re learning at work. If not for anyone else, it’s just for you. When I try to teach something to other people, I just learn it so much better.”

Phil made a point that gets lost in the content advice noise. Tech and SaaS professionals exist inside a bubble. Spend time on Twitter and you can feel hopelessly behind if you’re not running local AI models or building multi-agent workflows. But large chunks of the actual market, in finance, healthcare, and legal, operate with AI almost entirely blocked on company devices. Companies implementing basic marketing automation in 2026 are normal. Content about your experience with that work is relevant to more hiring managers than the algorithm would have you believe.

Key takeaway: Write 1 post this week about something you actually did at work: a problem you solved, a tool you evaluated, or a process you built. Keep it personal and specific, because generic advice posts do nothing for a job search. Post it even if you think no 1 will care. The audience you need is 1 hiring manager currently working through the same problem you just described, and you won’t know when they’re scrolling.

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What Hiring Managers Notice First on Your LinkedIn Profile

When a hiring manager opens your application, they usually check LinkedIn before reading your resume. Sometimes during, sometimes after, but they check. Phil’s experience hiring throughout his in-house career was consistent: he always looked. He read the About section and scanned what was written under each role. The profile doesn’t have to be impressive. It has to be real.

The basics most profiles get wrong are not complicated. Write in the first person. “Darrell is an industry thought leader in marketing operations” makes it obvious that Darrell wrote this about himself and chose the third person to sound more authoritative. An AI-generated About section reads just as hollow, and hiring managers spot both. Use the header image. Most people leave it completely blank, and it’s free space to show personality.

Get a real headshot. Skip the AI-generated versions that remove every imperfection and leave you looking like a LinkedIn stock photo. You show up to an interview and the person on the other side is slightly confused by who walked in. Invest in a basic professional photo. It doesn’t need to be expensive.

“90% of people, when you look at their resume or LinkedIn, it’s just words. There’s just nothing there. If somebody comes onto your thing and it just has 1 article about your experience at an Inbound or a MarTech conference, you’re already better.”

The keyword layer matters more than most people think. Recruiters search for specific tools. A hiring manager looking for someone with Marketo experience runs a search on those exact words. If you’ve built campaigns in Marketo and the word doesn’t appear anywhere on your profile, you’ve filtered yourself out of queries you should be winning. List the platforms you know under each work experience entry. Note major projects. The goal is to surface in the right searches, not just to look polished to a manual visitor.

Darrell’s approach to LinkedIn engagement is worth borrowing directly. He uses the platform as a learning feed as much as a network. He follows people doing interesting work, reads what they post, and comments. He congratulates contacts who announce new roles. When people reach out to connect, he replies. It requires minimal time and no editorial discipline. His description: just be part of the community. Log in instead of opening Instagram, like things, comment on things, let that become a habit.

Phil’s story of how he and Darrell first connected is a useful illustration. Phil was building his audience from almost nothing on Twitter (still almost nothing today). Darrell, who had over 10,000 followers and had written a book, kept liking his posts. Phil’s immediate reaction: who is this person and why are they paying attention to me so generously? That small, effortless gesture created genuine affinity before they ever spoke. Darrell’s honest reflection on choosing positivity over combativeness on social media: it’s actually the harder path.

Key takeaway: Spend 30 minutes this week updating your LinkedIn profile: rewrite your About section in the first person, add specific tools and platforms to your experience entries, and update your headshot if it’s more than 3 years old. Then spend 10 minutes engaging with 3 people in your network. Comment on their posts, congratulate someone on a new role. Hiring managers remember the people who made them feel noticed before they even applied.

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Why Contract Work Is a Strategic Move for Marketing Ops Job Seekers Right Now

Contract work during a job search tends to get treated as a fallback, something you accept when nothing better is landing. Phil and Darrell pushed back on that framing directly. A contract role is a 2-sided trial: the company evaluates you before committing to headcount, and you evaluate the team, the culture, and the actual day-to-day before signing on full-time. Some of the best full-time hires in marketing ops came through contractors who proved themselves before a permanent role opened. The resume line starts building the moment you start working.

For candidates resistant to freelance work, Phil gave a specific shout-out to EMMIE Collective, a community built by Lauren and Sydney for fractional and freelance marketing technologists.

The gap EMMIE fills is awesome, solo freelancing is:

• isolating
• imposter syndrome is constant
• and finding good clients requires a sales process most operators aren’t built for
• support for questions you can’t answer
• coverage so you can actually take a vacation

EMMIE handles client finding and vetting, scoping, pricing, and saying no when a client isn’t a fit. Members get handed work, decide whether to take it, and get paid on reliable terms. Phil’s summary: it removes everything that makes freelancing hard while keeping the parts that make it worthwhile.

Darrell’s own history with moonlighting goes back years, before it became standard career advice. He found his first gig through Upwork and deliberately undercut his price to land it. His reasoning: he hadn’t done it before, but he knew he could. Landing that first client at a lower rate was worth it for the proof of concept. His read on the current market is blunt. Of his cohort of laid-off colleagues, the ones who refused to consider a lateral move, a pay cut, or a step down from their last title are still searching months later. Right now is a bad time to hold out for an upward move.

“My cohort of folks that got laid off, the ones that weren’t willing to either take a step down, take a pay cut, or make a lateral move, the ones that were looking to go up-up, many of them are still looking, no fault of their own. I just don’t think right now is the time to do that.”

Contract work also compounds in less obvious ways. Phil outlined the relationship angle: every person you work alongside during a contract becomes a long-term network node even if the engagement doesn’t convert to full-time. You accumulate portfolio pieces while the work is current and the details are fresh. When you work inside a company, you get access to their tool stack. For someone unemployed, expensive platforms like MoEngage, Marketo or ZoomInfo are completely inaccessible. There is no free version of Marketo. Contract work puts you inside a production environment with real tools, real problems, and new resume lines you can’t get any other way.

Key takeaway: Stop treating contract and freelance roles as fallbacks and evaluate them as strategic options if you’ve been applying for more than 60 days without traction. Set a simple rule for any contract you take: treat every deliverable as an audition, tell the team early that you’re open to a full-time role if 1 opens, and use the tool access to document your work and add it to your portfolio. If building a freelance practice sounds appealing longer term, look at EMMIe-Co as a structure that handles everything that makes going independent hard.

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Which Job Search Tools Help and Which Ones Waste Your Time

The job hunt tech stack that gets discussed on LinkedIn and Twitter mostly makes you look worse. Phil spent the opening of this section naming the things to avoid:

• AI tools that mass-apply to hundreds of jobs a day
• AI-generated resumes and cover letters submitted without reading
• LinkedIn profiles “enhanced” with AI headshots and buzzword bios
• Interview coaching assistants that cross into real-time scripted answers during live calls

Don’t be that cat.

These are common enough that hiring managers have started building detection loops. Some job postings now include phrases specifically designed to catch candidates who copy-paste AI output without reading it. If the last paragraph of your cover letter is about bananas, you’ve already been filtered out.

Darrell shared his approach during his own job search. He didn’t use mass-apply tools. His primary KPI was 5 to 7 daily outreaches to people he knew were well-connected and might have a relevant role in mind. Direct personal messages to specific contacts with context, targeted rather than broadcast. His advice: “apply to 20 jobs a day” is not a real KPI. Using AI to automate that process just scales the noise. The 5 targeted human messages will beat 500 automated applications every time.

“You have to get the process down first, and then you can automate it. People are looking to automate things that just shouldn’t be automated.”

The tools that do earn their place are unsexy by comparison:

• A job application tracker in Trello, Notion, or a spreadsheet matters more for consistent follow-up than any dedicated job platform,
• specialized options like Teal and Huntr exist if you want features like browser extensions and automatic follow-up reminders.

The point is having a system. On the search side:

• Cron jobs, Claude Cowork tasks or good old faithful Google Alerts set up for queries you care about surfaces new postings without daily manual scanning.
• VisualPing monitors specific pages for changes and integrates with Slack, so if a role goes live on a company careers page you care about, you get a notification within minutes.

Agents

The interesting territory is agents, and Phil has been watching builders create serious infrastructure here. A developer used Claude Code with the Playwright MCP to scrape career pages and get a Telegram message within 2 minutes of a new role going live.

Another person built an AI job search agent with openclaw – It scans LinkedIn throughout the day, drops job matches into Trello, texts him updates, and tailors his resume and writes a cover letter when he marks jobs as “Qualified.”

Phil was experimenting with a multi-agent workflow in LobeHub where specialized agents run in parallel: 1 searching for roles, 1 tailoring resumes, 1 ranking opportunities, 1 drafting cover letters.

If you’re currently unemployed and have time to invest, building and documenting this kind of exploration is itself a portfolio piece. You’re demonstrating applied curiosity in the exact domain companies say they want.

The mistake is automating before you’ve built a clear manual process. Know what a strong application looks like for you: the right company type, the specific tools in the description, 1 genuine connection inside the org, a concrete story connecting your background to their situation. Build that criteria manually first. Then consider what can be systematized. Automating a vague process just produces more of the same noise, faster.

If a hiring manager wanted a GPT to write the cover letter, they would have done it themselves. They posted an open role for a human. Use your humanity to apply for it.

Key takeaway: Build your application criteria manually before touching any automation: company type, tools required, 1 connection inside the org, and a specific story connecting your background to their situation. Set up Google Alerts for the 5 to 10 roles you’re targeting and VisualPing on 3 to 5 company careers pages. Use AI as a sounding board on your cover letter drafts, but write the first draft yourself. If you have time, document your exploration of job-search agents; the exploration itself is worth showing.

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How a Video Introduction or Visual Resume Gets You Into the Next Round

When applications go quiet, it’s easy to blame the market. Sometimes that’s accurate. Sometimes the materials themselves are the problem. Phil and Darrell closed the episode with a handful of quick tactics that don’t require a big audience, a fancy tool, or significant time to execute.

Visual resumes are the most underused option. A template built in Canva, even a basic 1 with some color and structure, looks immediately different from the standard Arial font Word document that represents the majority of candidates. Martech roles often require visual thinking. A resume that demonstrates basic visual sense costs 30 minutes and signals something a plain document can’t.

Video introductions get more skepticism than they deserve, in the right context. Darrell has received these from sales candidates and his honest take was that a good 1 stood out from everything else in his inbox. The key variable is personalization. A video that opens with the company name, the specific role, and a 30-second case for why this combination makes sense is genuinely different from a standard cover letter. A template video that opens with “Hello there” is immediately obvious and immediately ignored. If you’ve been sending applications and hearing nothing, Darrell’s read is direct: that silence is a signal to try something different.

“If you are applying and not hearing back, and reaching out and not hearing back, it’s a really good sign that you need to do something different.”

AI tools built specifically for interview prep are worth using if you tend to go blank on questions you weren’t expecting. Upload the job description and your resume, let the tool generate likely questions, and practice answering them out loud. The “tell me about yourself” question consistently trips up candidates who haven’t rehearsed a version tailored to the specific role in front of them. Bouncing rough answers off AI before the real call costs almost nothing.

Phil has been running open-to-work shout-outs in the 404 Martech Newsletter, highlighting 4 candidates per issue who are actively looking. The form is a simple Airtable link in the episode notes. Within 2 weeks of starting the feature, 1 person had already gotten an interview through it. Hiring managers are in that audience. If you’re open to work and willing to say so, it costs 5 minutes to fill out the form and the upside is real.

Key takeaway: Change 1 thing about your current application materials if responses have stalled. Build a Canva resume template, record a 30-second personalized Loom for your top 3 target companies, or spend 20 minutes with AI practicing your “tell me about yourself” answer until you have a version that’s specific to the roles you want. Fill out the open-to-work form in the 404 MarTech Newsletter episode notes if you’re actively looking; hiring managers listen to this show.

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Episode Recap

The central argument of this episode is that the 2026 job market for marketing ops is harder and more AI-filtered than any previous cycle, but most candidates are making it harder on themselves by misunderstanding where the actual edges are. Phil and Darrell aren’t optimistic for the sake of it. Darrell came out of a real job search in this market. His read is grounded in what he watched happen to colleagues who held out for upward moves, in what interviewers actually asked him, and in where he found traction.

The tactical thread connecting every chapter is show your work and be human about it. Build the portfolio because almost no 1 has 1. Post the LinkedIn content because 90% of profiles in this space are walls of words. Take the contract role because it opens tool access and relationship doors that the unemployed simply don’t have. And when it comes time to apply, write the cover letter yourself, because hiring managers can tell when you didn’t.

The AI chapter cuts 2 ways, and that tension is worth naming. On 1 side, AI fluency is the primary hiring filter right now, and candidates who can speak specifically about AI applications in marketing ops workflows have a real edge. On the other side, the AI job-search tools being marketed aggressively, including mass applying, AI-written cover letters, and generated LinkedIn profiles, are actively filtering candidates out. The advantage goes to the person who uses AI to get better at their job, not to automate the application process itself.

The bigger picture implication is about timing. The AI competency gap, where companies demand experience that barely existed 3 years ago, won’t stay open forever. Right now, candidates with even modest real preparation can walk into that gap and look credible. The market is also compressing the career mobility that previous job cycles allowed. This is the wrong moment to hold out for a title jump. The candidates winning right now are the ones who are realistic, visible, and human about the process.

Listen to the full episode for Phil’s breakdown of the specific agent builds he’s been watching, Darrell’s direct advice from his own recent search, and the Airtable link for the open-to-work feature in the 404 MarTech Newsletter.

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Intro music by Wowa via Unminus
Cover art created with Midjourney (check out how)

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