My Senior PM Job Search System

Harshal
3 hours ago
22 min read

AI coaching, AI-built tools, pipeline tracking, and behavioral prep.

I recently ran a senior IC PM search with many parallel opportunities and multiple offers. I started with one resume and solo interview prep. I applied agent-building skills and systems thinking to the whole pipeline. By the end I had a pipeline tracker for parallel opportunities, an AI coach that knew my history across companies, agents that prepped and debriefed each round, and structured research on every employer and interviewer. The result was a reusable operating system I used at every interview stage.

Parts 1 and 2 install the infrastructure. Parts 3 through 9 cover learnings at each step of the interview pipeline. I note what held up under high load and what I will not generalize from my own funnel.

Job search operating system, parallel paths, prep

Who this is for

For:

Senior, staff, or principal individual-contributor product roles
Candidates pursuing multiple opportunities in parallel

Not for:

People-manager, director, VP, or head-of-product tracks, as these learnings omit people-management skills
Early-in-career PMs. Cases worked for me without prep, so I do not cover case interview technique, proving baseline PM craft in early-career loops, or getting more interview invites
Candidates focused on 1 or 2 target companies only, as this system assumes parallel pipelines and high volume

Read this as one experienced IC's operating system for a high-volume search.

Sharing these learnings at Product Camp Dublin

How this post is organized

Parts 1 and 2 are infrastructure you install once. Parts 3 through 9 follow the process in order. You reuse the same coach, repo, and Trello board throughout. Update the card when you prospect, apply, and after each round.

Part 1. One Workspace, One AI Coach
Part 2. ATS: Pipeline and Portfolio Approach
Part 3. Resume, pitch, and LinkedIn
Part 4. Prospect
Part 5. Apply
Part 6. Recruiter screen
Part 7. Interviews
Part 8. Product memo
Part 9. Offer and negotiate
Close. Run the search with a team

How to get started with this post

Take what parts you like from this journey.
You do not need all the infrastructure before your first interview. Do not defer search for months while you build the system. Run search and system-building in parallel.
Minimum before interview 1: write a digital introduce-yourself for that role before the call and debrief by writing down 5 questions you were asked in the call. The repo, board, and story bank compound from interview 1.

Part 1. One Workspace, One AI Coach

In previous years, I had a workspace in Google Drive where I used one Google Doc per company I was interviewing with. This time, I moved from Google Docs to markdown files to use AI.

I forked interview-coach-skill, an open-source GitHub project by Noam Segal, from Lenny Rachitsky's ecosystem, and used it as the base for my search prep. In the first week I sharpened my pitch, edited my resume and LinkedIn, and rehearsed for interviews. That was enough until interview volume outgrew the setup.

When you run many companies in parallel, the coach has to know your pipeline, your deadlines, and your history. I kept modifying the repo for my workflow instead of treating it as a static template. I set coaching directness to level 5 so feedback stayed blunt. I converted older documents to markdown so everything was diffable and searchable in one repo.

Wire Granola MCP for interview transcripts, or debrief in writing right after each call and feed that into the repo. Either path gives the coach ground truth on what you were asked and how answers landed. Before early rounds I used prep and hype commands on the same repo. Recruiter screens, behavioral interviews, product memos, and offer negotiation all build on this infrastructure. Each adds stage-specific habits on top of the same tools.

One folder template per company

I maintained a fixed set of documents per employer and scripted copying from templates for each new company folder (YYYY_MM_DD_company).

Typical layout:

Company folder: product memo and company research
Company-specific question list: what this employer is likely to probe
One file per person you meet
A saved copy of the job description: postings disappear and some processes run for months
Referral message drafts saved for AI reuse, not only WhatsApp or LinkedIn threads
Application short-answer write-ups
Per-company *_preps.md files, for example a pricing role I had never done before
Agent notes in templates: instructions for what AI should fill per section

A script created the skeleton so I never debated folder structure mid-search. The folder grew as recruiter screens, live interviews, and product memos added files.

AI for prep outside the interview

Some interviewers worry candidates use AI live to answer questions. I never tried that and do not see a need to. I used AI heavily outside interviews for story drafts, mocks, debriefs, application copy, and calendar-adjacent prep.

Use AI to prepare answers you can own on the call. On the interview itself, you think, listen, and respond in real time.

Cursor second-brain, interview-prep workspace

Part 2. ATS: Pipeline and Portfolio Approach

I used a CRM-style setup to store people notes, company notes, and deep research per employer. I treated the search like sales with portfolio discipline. You sell your candidature to many prospects. Many fit your ideal customer profile. You do not need to win every deal. What matters is the subset that moves forward. That framing kept me from treating every "no" as a verdict on me. It also kept research reusable when the same company returned with a better role 6 months later.

Pipeline stages: prospect through archived

Model each opportunity in one system with explicit stages: prospect, applied/referred, recruiter screen, hiring manager, loop, offer, archived. Archived marks closure without turning the board into a moral scorecard. When a round ends without a next step, I moved the card to archived or closed. The outside world may call that rejected. In your tracker, lighter language protects the energy you need each morning.

Trello became the system of record for opportunities. I wired Trello MCP so agents could read and update cards. Employers have ATS. Now you, as a candidate, have one too.

I also built a small app on Railway with a cache on top of the board, plus a cron job that snapshots the board into the workspace. Those snapshots let the agent plan my day without hitting live APIs every time. The Railway app let me visualize my pipeline anytime. In hindsight, I could have gone with a SQLite database instead of Trello, but Trello gave me a great UI/UX even before I built other apps or dashboards.

For every company I recorded the next interview or action date. When something ended, I logged why. Over time those reason codes revealed patterns by stage, role fit, and loop type instead of a vague sense that the search was going badly.

Do not log rejections where you never interviewed. There is no reliable signal, only noise, and you rarely know why. After at least 1 interview, employer feedback or your own hypothesis about what missed belongs in the tracker.

Pace volume and Prep

Invest in interview prep daily or weekly: story bank, mocks, and answer refinement.

Start with a cap of 2 interviews per day. Scale toward 4 only if you are still sharp and not cutting debrief or recovery. Some readers may struggle to get two interviews in a month. Others get many invites and schedule them back to back. I recommend spacing interviews out even when volume is high.

Keep motivation and energy high

If motivation dips or you want skills to compound during the gap, build something. A small project, an agent, or daily AI workflows all count. Searching and shipping in parallel kept me fluent in the tools employers kept asking about. Examples from my own builds: a newsletter-to-private-podcast n8n workflow, vibe-coded PM tools like Opportunity Solution Trees from my AI-native PM stack, 2026, and the broader shift in daily build vs write.

Part 3. Resume, pitch, and LinkedIn

By now you have a coach repo and a board with no cards yet. This phase is profile and narrative before you prospect or apply at scale. If an opportunity comes to you first, apply anyway.

Coach for resume, LinkedIn, and pitch

Use the AI coach to:

sharpen your pitch: a short, general introduce-yourself,
edit your resume, and
edit your LinkedIn profile.

For example, it helped me decide what to highlight in my LinkedIn headline, how to think about the cover photo, and what to improve in my About section. It also flagged gaps in each job description, especially for my most recent roles.

I have coached dozens of candidates over the years for product management job search, and one thing I have noticed is that candidates focus too much on trying to perfect their resume. The AI coach highlighted the same and made me move forward from the resume editing very quickly. My approach to writing the resume was to write dozens of bullet points with a lot of results quantification efforts that I did for my recent jobs, and then use AI to shortlist that and bring that down to more focused bullets.

Make the agent write in your tone and style

I used Cursor to draft emails, LinkedIn messages, and WhatsApp messages. Every time I edited its draft message, I saved before/after in a file and had saved the inferred preferences in another file. Each send made the next draft closer to my voice. I had years of blog-editing preferences I could import as a baseline. With modern LLMs, start from scratch if you need to. When you apply, reuse the same preference files for application short answers.

Part 4. Prospect

This phase is list-building and outreach planning before you submit applications.

Do not rely on one job board like LinkedIn Jobs. I ran outreach as 3 parallel lists: people to network with, companies to target, and jobs to apply to.

List from job postings

This is the standard step everybody knows. Look up job boards. Target companies hiring for your role. Set LinkedIn job alerts for your country and for other markets where comp and language fit, not only where you live.

Ireland's market is smaller than Europe's. I set alerts for remote roles in the UK, Netherlands, and Germany, and skipped Spain, France, and Portugal because salary bands or language expectations did not fit. Those roles may not be tagged to Dublin even though they might hire there.

Own company list

Combine:

Curated lists of startups and scaleups, like in Wellfound and Sifted
List competitors of your present and former employers, and adjacent companies in your domain
List products and their competitors you have used for work and personal projects

The goal is your own target list. Do not wait for the perfect posting to appear in one feed.

Startup list sources: Sifted, Wellfound, and related lists

For list building, I stayed manual-first. Copy a full list page into ChatGPT, ask for a CSV, then review companies one by one. Bot blockers there often return false "no opening" negatives. I would have loved to use AI for this, but web browsing is very token expensive, and many websites block agents.

Many startups skip LinkedIn because of posting cost, so check careers pages directly, instead of relying solely on job boards.

Job list sources: LinkedIn Jobs across countries

Many employers in Ireland and Europe prefer candidates they do not need to sponsor. I did not face that friction. If you need sponsorship, take interviews anyway when you have capacity. Internal champions can still form even when sponsorship is a long shot.

People List

Build a people list first: people already in your target role, for example AI product managers, even at companies not hiring now.

Reach out to learn from their experience. Ask what helped them get there.

Use these relationships for introductions and for sharpening your resume, pitch, and interview answers.

Part 5. Apply

There are three paths to enter the interview pipeline for a company:

apply: you submit through a careers page or job board.
referred: someone inside forwards your profile, and
inbound: recruiters or hiring managers reach out,

I used all three. The mix that worked for me was not what conventional advice predicted.

Apply when you are about 50% fit to a role. Do not wait until you feel 90% fit. Research has shown this repeatedly across gender and background: for example, men versus women, or white candidates versus other minorities. Groups that apply more readily often get into interviews even with a low fit. Do that for yourself too.

Applying directly

1 - apply directly on each company's careers page.

2 - apply with a Markdown or text or docx format of your resume instead of PDF where possible because that will improve the ATS parsing. 3 - Many companies will ask for more than your resume. Make sure you are saving your responses for the first one or two attempts and then using AI for subsequent applications. Use AI for job-application prompts like "Why this role?" and similar questions. Point the agent at the pasted job description, your resume, LinkedIn, and your writing-preferences file from resume and pitch prep above. Draft, edit, save the delta, and let preferences compound the same way as outreach.

4 - Do not track cold applications on the board. Many of them will end up rejected with no information for why you got rejected or anything actionable, but it will bring down your motivation, so do not track that.

Referred

I expected referrals to matter a lot at startups and at big companies. Big companies get a lot of applications. A referral helps your resume stand out for recruiter review. At startups, every employee is a much larger share of the company. Every hire needs more vetting, and the team has less time to do it.

My funnel surprised me. At some places, referrals barely gave me any lift in getting through the interview process. I planned to apply through a referral at one company, but applied directly before the person responded and still got an interview invite. At other places, a referral got me an interview invite, and I still did not move forward. At two other places, my profile matched the referred role and a former colleague referred me. I still did not get interview invites. This is anecdotal. Your funnel may differ.

I also had ideal companies and roles on my list. I had so many interviews each week that I lacked time to pursue referrals there. I did not reach out to second-degree connections. I did not ask first-degree connections for introductions to people at those employers. In some cases I did not know anyone there. Referrals take effort. When interview volume is already high, that time has a real opportunity cost.

Add a card to your pipeline when you get referred for a job. Update stage and next-action dates when you hear back.

Inbound

Most of my interview volume started here. Profile and visibility did the heavy lifting before I had a referral story to tell.

3 levers that moved inbound for me:

a complete profile,
regular posts for activity ranking in recruiter search, and
reconnections.

Reconnections

Reconnections mean messaging and getting on calls with people you worked with in the past, or friends across geographies and companies. These are dormant connections, not warm today. Dormant connections are people who knew you well but drifted. They often sit in different networks now. Warm intros from them beat cold outreach.

We are not reconnecting to ask for a referral into their employer. That is a different channel. Reconnect to bring up your job search. If they know someone, ask for an introduction. Ask if it is okay to go through their LinkedIn connections and request introductions.

I sometimes defaulted to reaching out to peers and juniors. Reaching decision-makers is harder than reaching peers or juniors. The skill overlaps with PM stakeholder work. Job search is a low-stakes place to practice it.

Reconnections are maybe 10-20% of your time. They are high upside when they land. One example: a friend reconnected me to her former colleague, who was hiring for an AI agents product role.

Complete Profile on LinkedIn

Complete your profile as we discussed earlier. Then, mark yourself open to work.

LinkedIn Open to Work has 3 modes: not open, passively looking, and actively looking. I moved from passive to active during the search. I also set it to be visible to recruiters/talent acquisition sourcers only, without the public green badge on your profile. I had no research behind that choice. The public badge felt desperate to me, like a flashing sign that I was on the market. Recruiters are who need to know you are looking, not your full network.

Post Regularly on LinkedIn

Post weekly on LinkedIn. Write about things you have built or learned. Job-search posts are a weak signal. Posts about your learnings or projects are stronger. A very strong signal is presenting or giving a talk, or attending or hosting an in-person event.

I am not recommending you post AI-generated content as that does not feel good. Instead, post a short take in your voice. That is good enough. Perfect hashtags, hooks, emojis, or thumbnails are optional.

Surprising Patterns with Referrals

Most of the offers I got were inbound. Referrals did not change my likelihood of getting interviews. Once I saw that in my own data, I stopped spending relationship capital on interesting companies with uninteresting roles. I still applied so my name was in the system if something better opened later, but I did not ask friends to spend political capital on those paths.

For roles that were very adjacent to my background, I still reached out for networking. That also rarely helped, which surprised me given conventional job-search advice.

Part 6. Recruiter screen

Salary expectations in early screens

When recruiters asked for salary expectations, I said I was flexible. Total comp, team fit, and project scope mattered more than base alone. No recruiter let me proceed without a number. Early on I gave a single figure. Later I gave a range.

I often quoted above their opening number. That was sometimes the only way they revealed their budget. In other cases my number sat inside their band and they still refused to share their range. They did not want to give me room to ask for more than I had already stated.

Quoting above budget was fine. Recruiters know active candidates may accept less for the right fit. When they shared a range and asked if it worked, I often said it was in the ballpark and kept the process moving.

Do not anchor to one number, refuse to update it, and turn down interviews because comp differs slightly. 3 reasons to defer hard anchoring:

Fit clarity comes from conversations more than the JD alone.
Real comp bands show up only after mutual interest.
Strong interview performance can move level and pay up.

Tailor "introduce yourself" per opportunity

I used to open chronologically, with light emphasis shifts by role. Friends and the AI coach kept pushing me to lead with the slice of experience that maps to this employer's problem. After I switched, recruiters reacted immediately with "this is exactly what we are looking for." Treat the intro as a positioning pitch for this role.

Recruiter process control

When recruiters ask "Are you in other processes?", always answer yes. Treat your pipeline as applied onward, not only final rounds.

When they ask "What's your timeline?", reflect the question back and match their stated pace instead of anchoring a hard date.

When you cold apply through ATS, do not follow up on process status by email or elsewhere. If you got referred, follow up with your champion. If you are already in process, want the role, and need to move fast, follow up with the recruiter or hiring team.

Calendar buffers

I automated calendar blocks with n8n. Each interview got 15 minutes before for prep and 15 minutes after as buffer. Prep was for last-minute notes, closing coding agents and extra Chrome tabs, stopping Spotify, and opening only what I needed.

Prep screen layout:

Center: Google Meet or Zoom
Right: transcript tool or scratch notes for a post-call debrief
Left: tailored introduce-yourself and why this company

Interviews often ran long. The buffer let me lean in. I asked more questions, gave fuller answers, and captured what was asked instead of racing off the call to the next meeting. I kept the same blocks for hiring-manager and loop rounds.

Recruiter phone screens do not need more than an hour of prep. Save deep prep for hiring-manager and loop rounds. I sometimes prepared for a full day for an upcoming recruiter call. The call then showed the job differed from what I expected, sat in the wrong time zone, or required a country move. I limited AI-assisted prep on recruiter calls and saved depth for later rounds.

Part 7. Interviews

Update your ATS after each interview. Record the next step, or move the card to archived with a reason. Use the same coach and debrief notes from the workspace setup above.

Story bank in SAR

Write SAR or STAR answers as bullets you can speak from.

I built a story bank for the questions that show up everywhere: biggest accomplishment, failure, and convincing stakeholders.

Typing one story at a time was exhausting. I spoke for 30 to 45 minutes with Wispr Flow, then asked AI to compress the transcript into SAR bullets. Voice input worked better than drafting in a doc.

Pick 3 meaty experiences with enough depth to sustain 30+ minutes if probed. Reuse the same three across those question types instead of inventing new material each time.

In live interviews, steer answers toward those stories when the question allows. List compelling project names on your resume so interviewers ask about work you want to discuss. CV bait.

That cut prep time and moved mocks toward delivery instead of invention.

Story bank entry: SAR bullets (mock example)

Refine answers when interview debriefs show gaps.

Debrief every interview

After each conversation, capture ground truth while memory is fresh. Write a quick debrief yourself in the buffer after the call, or use Granola MCP for full transcripts. Log:

questions you were asked
the answers you gave
what land

ed versus what fell flat

how you felt on the call

Run the coach against that record. That habit matters as much as prep before the next round. In rare cases the recruiter will give you feedback before your next interview or the end of all of your interviews (positive or negative). Make sure you are feeding that back to your AI job coach so it can amplify your debrief notes and help you course correct.

Thank-you notes

After every interview, connect with the person who interviewed you on LinkedIn or email within 24 hours. Send thanks while the conversation is still fresh.

You often will not have their email. Use a LinkedIn connection request note. The note field caps at 300 characters, so keep it tight. Draft with AI if helpful. Anchor the note in something genuine from the call: their work, career path, company direction, or a personal anecdote from the conversation.

3 reasons I do this:

You gain another channel to reach them if the process continues.
LinkedIn acceptance is a signal. If they accept and reply warmly, you are more likely moving forward. Ignore does not prove dislike. This is a weak signal, but it could help.
Even when they are on the fence, showing interest can increase theirs. This is a small way to bias the process in your favor.

Use person notes in live interviews

Use notes on the person in the room for your interview:

Open with one genuine thing you liked from their profile: career path, post topic, or shared background.
Mid-interview, link your answer to their similar experience and ask how theirs went.
Close by asking questions tailored to their profile and based on information from prior rounds.

A goal is to shift the conversation from you answering to them talking.

For prep, I copied LinkedIn manually: expand About, experience, and posts; skip repost-only feeds. When budget allowed, I used Perplexity Comet AI browser for the same. I did not use scraping tools as LinkedIn blocks bots.

Interview practice motivation

After a while, interview practice only inside an AI coach chat window felt flat. I faced 3 challenges:

The AI coach kept giving me the same feedback.
It was not an engaging way to do interview practice.
Although it could point out gaps and areas of improvement, it could not help me shape a story.

MockIF interview interface for AI interview mocks

I tried a few online mock-interview sites and signed up for one. It ran voice-led AI mocks, a different format from the chat-window drills in my AI coach. StellarPeers runs shared-calendar peer PM mock loops of about 1 to 1.5 hours. Worth knowing as an option.

The mock interviewing was good, but I thought it did not have all my context. So I borrowed interaction patterns and built my own app to wire it to my own content. I built a simple interview flashcard app instead. Story bank markdown was the backend. A lightweight HTML frontend handled flip-and-drill practice. Same stories, better ergonomics for repetition.

Human Expert Coach

After a few loops I hired an interview coach on Topmate, following a friend's recommendation.

I knew my failure modes in interviews were in some behavioral stories, for example, stakeholder management stories ran long or I dove into how I built an AI agent when the room needed to know how I do product management. My coach helped me frame stories.

Why I paid for a human expert instead of stopping at AI and peers:

AI kept flagging verbosity. I suspected a deeper framing issue.
Story crafting was something neither the AI coach nor peer format solved well. The expert helped me frame stories, not just flag length.
Peer mocks are time-expensive at high volume. Roughly half the session goes to interviewing the other person. With many live interviews running, that split was too costly. Paid expert time was a full hour on my failure modes.

Reach offer stage before you judge the opportunity

In every conversation, ask questions to learn whether the role fits. Save the sharp or potentially aggressive questions for after you have an offer. Your near-term goal is to reach offer stage with as many companies as practical, including lower-priority options.

2 reasons.

First, interest can flip late. Your 6th interview for an opportunity might unlock enthusiasm when interviews 1 through 5 felt like a waste
Second, doing everything right still may not land an offer because of headcount, internal candidates, or timing.

Do not ruminate on pros and cons before an offer exists. Once there is an offer, the company and the people you met are predisposed to answer hard questions honestly and to stay helpful even if you decline.

War stories from my search:

An 8 to 10 step process died when the CEO closed a role that had been open 6 months.
I got an offer, but could not follow up for 1 week because of interview load. The employer read it as low seriousness.
For one role, early rounds felt boring; later rounds felt compelling.
One process ran 4 months. Another reached offer in 10 days.

Part 8. Product memo

Many processes add case study homework, take-home assignments, or product memos during interview processes. Processes today often include product memos, take-homes, and sometimes paid trials. I hit paid-trial stages with 3 out of 32 companies.

Product memos as default homework

Write a product memo for every company you interview with. Structure I used: situation, users, needs, gaps, feature ideas, execution, GTM and metrics, trade-offs. Use AI to pre-fill a base memo before recruiter screens, then deepen where interviews will go deeper or where your read finds gaps. I used ChatGPT and Perplexity deep research and converted to my template, or Codex and web research to fill my format directly.

Your output will differ based on the company size. A fuller memo including GTM for startups and mid-size. Stay on product and discovery for big companies; keep execution and GTM thin unless you have sufficient information.

How to use the memo in live calls

Give the short answer first, then: "I wrote something on this; happy to screen share and walk through if useful." or ask for feedback on your thinking, not a show-off deck.

I iterated on my process:

Looked too framework-y, so I removed visible TOC and headings. I customized the section headings to the memo, and that was sufficient.
A CTO told me a direction then changed it in a week. A perfect memo became completely wrong when I presented it to another interviewer, so I know to add context when discussing.
I used AI to draft v1 of a take-home because I already had all the research for it. But this anchored me to a bad structure and writing style. In later assignments I wrote and used AI to edit and brainstorm.

Take-homes and presentations

For take-home assignments and interview presentations, I moved from Notion and Google Docs to coding agents.

I work in markdown, then convert to PDF for submission or screen share. I used Mermaid for flowcharts and diagrams where they clarified the story. I added screenshots where the product or UI mattered.

That setup made it possible for agents to edit the same files quickly. I kept Memo, take-home, and deck drafts in the workspace. The agent iterated on structure and copy instead of me rebuilding slides by hand.

When I needed to present live, I used Reveal.js so an agent could generate a deck from company memo, research notes, and assignment draft already in the repo. Same source of truth, 2 outputs. Readable PDF for reviewers. Slides for the room.

Memo workflow demo: markdown and Mermaid

Research every company, product, and team

Your prep shows on the call. In one recruiter round, I inferred who the likely hiring manager was, from LinkedIn, and what I liked about working and growing with them. I covered how I understood the product they were interviewing for and how my experience connected directly to the product and its upcoming releases. I tried to use the product or their competitors when possible. When that was not possible, I read customer case studies, watched videos, and read reviews.

Part 9. Offer and negotiate

Use the same AI coach for negotiation and offer-decision calls. Ask the tricky questions you deferred during loops, once you have an offer in hand.

Pace processes for optionality

Run multiple processes in parallel on purpose. If only one company is interviewing you, move that process a bit slower so you can open other doors. If several are in final rounds, accelerate new inbound companies until they join your finals, then slow them down again. The goal is overlapping offers so you can negotiate with a real best alternative to a negotiated agreement, or BATNA.

After your first offer, keep inbound and new processes warm until you have overlapping offers or you have signed an offer. Ask for 1-2 weeks time to see your existing interviews to completion. Do not close the pipeline the day one comp package lands.

Calendar: Plan your interviews across the week

Decide offers with a written rubric

I wrote priorities down while processes were still live, before the first offer landed. I grouped them into must-haves (scope, manager quality, comp floor), strong preferences (builder pace, durability, workload, domain fit), and explicit non-factors (brand, title). Meeting culture and evening load mattered more once offers were real than they had on paper. I reordered once or twice when BATNA changed. I used the same list in negotiation prep with the coach.

Offer-stage prep

Before a negotiation call or an offer-decision call, use the same coach and workspace from the start of this post. Prep likely questions, draft answers, tradeoffs against your rubric, and what you need clarified in comp: base, equity, level, and start date. BATNA pacing and the written rubric are in the sections above. Treat it like a loop round with prepared answers.

Close. Run the search with a team

Job search is like a job. It is also one-sided. The employers have a team. You are one person. Join communities of other seekers and meet regularly. For me that included the Never Search Alone book and peer community, Rand's leadership Slack group, especially the #job-search-journal channel for morale and pattern sharing, Irish Tech Community Slack, and Lenny's Community. The point is a pulse check on how you are feeling, separate from more tactics.