LinkedIn X-Ray Search in 2024 – The Ultimate Guide for Recruiters and Sourcers

Start with a precise, field-driven target list for your X-Ray search. Before you query, define the fields you care about: title, location, current company, past roles, e keywords that match your roles. This lets you build a tight list of criteria, reducing noise and keeping outreach focused.

Adopt a two-pass pattern. First pass pulls out-of-network pages and results from search engines that link to LinkedIn profiles, then you verify them in-network. The first pass shows a broad set, so you can batch via bulk filters by fields like title e location. For example, queries such as site:linkedin.com/in intitle:”Marketing” york or site:linkedin.com/in inurl:in “Marketing Analyst” help you lock onto specified targets. Save the hits to a database with phrases you recruit for, then run a refined search using those phrases on the next searches. This approach looks across multiple sources and builds a verified list you can act on quickly.

Employ logical operators to control depth: AND tightens, OR broadens, and quotes anchor exact phrases. Build a list of phrases that describe the role, such as “Senior Marketing Analyst” or “Marketing Manager” and test each phrase against profiles that appear in the database. Use searches with variations, then consolidate results in your database and remove duplicates with a simple checksum. Include the word analyst when targeting this profile type so you pull precise matches. At the end, each hit itself carries context you can act on.

Localize by city clusters to reduce noise. Target york and other major hubs, then expand by adjacent regions. Focus on warm outreach by tagging profiles with notes about prior roles and relevant projects. Personalize messages for candidates in marketing roles and reference concrete experience to improve response quality. Annotate each hit with context: current employer, seniority, and a notes field to guide next steps.

Maintenance matters: keep a lean database with many clean records. Regularly purge inactive profiles, deduplicate duplicates, and refresh data every 30–60 days. A specified workflow with clear ownership reduces friction and lets recruiters stay focused on conversations rather than data. Duplicates arent flagged automatically, so include a manual dedupe step to keep the corpus clean.

Outreach quality matters: avoid generic messages. If contact attempts are not opened, revise the phrases and tailor the tone. Maintain a clean database and ensure duplicates arent flagged in bulk, preventing misaligned campaigns. This keeps your fields aligned and your workflow smooth.

Practical X-Ray Search Strategies for 2024 and Prompting

Use a reusable prompt that splits targets into four fields: someone, role, location, and source. Build templates with anchored strings for x-raying LinkedIn profiles and public pages, then refine results by applying region, seniority, and industry filters. This approach reduces miss signals and gives reliable results across campaigns. Naming conventions for profiles and outreach cadences keep your pool organized and easy to scale through automation.

Prompting basics keep you in control. Create prompts that yield clean search strings, not prose. Include explicit operators, site patterns, and field labels so you can paste results into your tracking sheet or pool. Integrate prompts with your workflow so writing becomes a routine step rather than a guess. After you collect hits, review and refine keywords to improve coverage, collect phone numbers when available, and reduce duplicates.

Integrate results with your CRM and ATS to close the loop; naming saved prompts helps reuse patterns across jobs and pools. Write outreach messages with a single, uncomplicated prompt and tailor each to the role. Collect data, evaluate campaign performance, and refine keywords to reduce misses and increase match quality on the ones that matter.

Craft precise Boolean strings for LinkedIn X-Ray searches

Start with a customized core of 3 blocks: title, company, and location. Keep the strings easy to reuse and save them as templates so your team can scale quickly. For a practical view, target public LinkedIn profiles and use x-ray patterns to capture relevant phrases; each search view returns multiple profiles. This approach uses clear, reusable blocks and you can start doing this in a dedicated section of your searching workflow. weve tested multiple candidates with these patterns and seen strong results.

Structure your strings like this: (site:linkedin.com/in OR site:linkedin.com/pub) AND (intitle:resume OR intitle:profile) AND (“data scientist” OR “machine learning” OR “analytics engineer”) AND (remote OR “san francisco” OR “new york”) -jobs -career

To narrow results further, add blocks for company names, seniority terms (senior, lead, principal), and industry phrases. Use the operators AND, OR, NOT to combine terms and group them with parentheses. This narrows the search, helping you find warmer candidates faster.

For team collaboration, identify phrases that perform best and share them as customized snippets. Use extensions to save multiple strings under a section, track which combinations identify strong candidates, and iterate after each searching pass. weve found that certain phrases convert reliably, so sure to document what works.

Try ready-to-use templates such as: (site:linkedin.com/in OR site:linkedin.com/pub) AND (“software engineer” OR “developer”) AND (remote OR hybrid) AND (USA OR “United States”) AND (public) -jobs

Refine results with targeted filters: company, industry, location, and seniority

Begin with a focused core query and layer four decisive filters to tighten results. Use sitelinkedincomin for an x-ray style scan that looks for exact phrases across profiles. With automation and extensions, you can collect prospects fast while keeping outreach aligned with your target roles.

1. Company anchor: search for the employer name in the profile text using intext and, if needed, inurl:in. Example: sitelinkedincomin intext:”Google” intext:”Mountain View, CA” marks the company and location in one sweep.
2. Industry alignment: add intext:”Industry: Information Technology and Services” or intext:”Industry: Software” to confine to the sector.
3. Location precision: lock in city or region with intext:”Location: New York, NY” or intext:”New York” to prune outliers.
4. Seniority and roles: tag seniority and the target role using intext:”Seniority: Director” or intext:”Manager” along with the role keyword (e.g., intext:”Product Manager”).
5. Validate and export: run multiple variations, then feed results into your navigator or CRM. Use a quick manual check to confirm that the profiles match the intended level and skills before outreach.

Concrete query templates you can copy-paste:

• sitelinkedincomin intext:”Google” intext:”Mountain View, CA”
• sitelinkedincomin intext:”Industry: Information Technology and Services” intext:”New York”
• sitelinkedincomin intext:”Seniority: Director” intext:”Product Manager”
• sitelinkedincomin intext:”Tesla” intext:”Location: Austin, TX” intext:”Senior”

Tips to keep results clean and scalable: use quotes for precise phrases, use OR to cover variants, and build multiple smaller queries instead of one long string. If youre short on time, rely on extensions to run these patterns and to collect results through automation for outreach. Look for profiles that match the desired levels and skills, then proceed with manual checks before contact. When you identify a good prospect, you can save the search blocks for repeat use and refine further as you expand to new companies or industries. Through this approach, you improve the hit rate without overfetching, and you maintain a steady flow for outreach campaigns.

Prompt 1: Build a tailored LinkedIn X-Ray query for senior software engineers in Berlin

This is the best starting point for Berlin-based senior software engineers: a tailored LinkedIn X-Ray query to return high-quality profiles. Googles-style x-ray approach lets you pull fields like title, current company, and location from linkedins, improving your targeting. Core query: site:linkedin.com/in (intitle:”Senior Software Engineer” OR intitle:”Staff Software Engineer” OR intitle:”Principal Software Engineer” OR intitle:”Lead Software Engineer”) (Berlin OR “Berlin, Germany”).

Variant 1 – stack-aware: site:linkedin.com/in (intitle:”Senior Software Engineer” OR intitle:”Staff Software Engineer” OR intitle:”Principal Software Engineer” OR intitle:”Lead Software Engineer”) (Berlin OR “Berlin, Germany”) (Python OR Java OR Go OR Kotlin OR JavaScript OR C# OR Scala). This helps you reach likely candidates whose profiles highlight the key tech signals for your campaigns.

Variant 2 – industry and current company tilt: site:linkedin.com/in (intitle:”Senior Software Engineer” OR intitle:”Staff Software Engineer” OR intitle:”Principal Software Engineer” OR intitle:”Lead Software Engineer”) (Berlin OR “Berlin, Germany”) (Software OR Technology OR “FinTech” OR “Healthcare IT”). Capture fields such as title, current company, location, and industry to build a focused list for outreach. The exception is to avoid overloading your scraper with noise; stay concise in your results.

Practical setup for automation: run these queries on Googles engines, then use a scraper to export results to CSV with columns: name, profile URL, title, current company, location, industry, and key skills. Currently, keep batches of 20–40 profiles for quick validation. Use tools and platforms that support deduplication, flag likely matches, and route profiles to your campaigns. This approach helps you return a higher-quality pool while respecting terms and their guidelines.

Whether you center on Berlin first or widen to nearby cities if the initial pool is small, these queries are flexible. Based on results, tweak stack keywords, adjust location radius, or add related titles to improve accuracy and capture more relevant candidates for your campaigns.

Prompt 2: Expand to remote roles with stack, time-zone, and seniority considerations

Start by defining a remote-ready search plan: pick three target stacks, set a time-zone window, and assign seniority bands. Create a campaign that pairs public LinkedIn profiles with exact keywords and stack terms, and run searches that can be repeated across campaigns. This approach reveals similar patterns across roles and keeps results consistent.

Build search strings that combine intext, keywords, and stack terms. For example, use: site:linkedin.com/in intext:remote (Java OR JavaScript OR Python) intext:senior OR intext:lead OR intext:architect (intext:Spring OR intext:React OR intext:Django) within London. Youre aiming to catch candidates who list remote work and relevant tech stacks in their public profiles. Cant rely on guesswork–structure searches so they feed into a reusable campaign and are easy to reproduce across campaigns. Look for posts, events, and signals that hint at distributed work or contract experience. Using outscraper helps you extract results into a clean list for your client, and you can filter by stack, keyword clusters, and seniority level intext.

Time-zone alignment matters: target profiles that indicate availability within a two-hour window of your hub (e.g., London time) and prefer asynchronous communication for wider access.Within searches, add phrases like remote, work-from-home, distributed, and flexible-hours to surface hidden candidates who dont advertise remote explicitly. Include keywords that reflect seniority such as senior, lead, architect, principal, or staff to ensure you’re not flooded with junior profiles. Use public signals and a consistent rubric to score each entry; this keeps the campaign focused and reduces bias during filtering.

After gathering results, run a quick extract pass to normalize fields: name, current company, title, location, profile URL, stack indicators, and time-zone hints. Enter each result into your helper pipeline and tag with stack, seniority, and remote keywords. Relying on a structured approach prevents miscasts and makes it easy for the client to review. You can search searching again with refined keywords if you see similar gaps, and keep the momentum by refreshing the campaign every few weeks based on events, changed stack popularity, or shifts in London-based hiring tempo.

Prompt 3: Validate, deduplicate, and summarize candidate pools using AI prompts

Validate every candidate record by checking essential fields: name, title, company, location, url, and a usable email handle. If a field is missing or the title is vague (for example, “Engineer” without a function), flag it for review. Run a light intext and x-raying check on the profile text to confirm relevance, then perform a quick after validation pass to ensure only high-confidence records move forward.

Deduplicate with a two-layer approach: first, normalize core identifiers (name, current company, location, and title); second, apply a similarity threshold to group similar records into blocks. Create explicit before/after snapshots for each deduplication step. Use a scraper workflow to flag near-duplicates across fields, relying on canonical name spellings and company aliases to reduce false merges, and keep any records with mismatched critical fields in a quarantine list for manual review. Include references to canada as a location tag when appropriate to avoid mixing regional pools.

Summarize pools with AI prompts by producing a concise, structured brief per batch. Build a parts-based view: demographic, function/role, industries, and geography (canada where relevant). Identify top groups and teams, highlight the most common titles, and extract the best 5–7 skills per pool. Generate a compact output that shows total deduplicated count, distribution by location, and prevailing seniority. Use a navigator-style overview that lets readers move between blocks of candidates and quickly compare segments.

Prompts should be assembled from a helpers set: a validate_fields_prompt, a dedupe_prompt, and a summarize_prompt. Feed the AI a clean list of records with fields: name, title, company, location, skills, years_experience, url, notes. Instruct the model to output a precise JSON-like summary with fields such as count, confidence, top_roles, top_skills, location_distribution, and a compact roster. Before summarizing, list each candidate with a short descriptor to aid rapid scanning, then after the summary, present concrete action steps for follow-up. For emails, flag entries that end with gmailcom as generic or placeholders so sales teams can decide whether to pursue direct outreach or discard those lines from the primary pool.

Drive consistency by creating blocks that map to Salesforce fields: map name to Name, title to Title, company to Company, location to Location, skills to Skills, and notes to Notes. Use the blocks to build a clean feed that teams can export directly into Salesforce records, preserving source provenance and the original pool size. The result looks like a streamlined navigator of candidates, with clear next steps and a low-friction handoff to recruiters and engineers who will engage the leads.

Keep the workflow tight and scalable: rely on the same core prompts across Canada-focused searches and across groups and teams, so you can compare pools side by side and maintain consistent quality. When a batch passes validation and deduplication, create a compact summary that can be shared with groups and managers, then push the final results to the CRM and to the downstream outreach queue for targeted engagement.