Recruiter candidate outreach

Senior candidates see hundreds of recruiter messages per quarter. Generic 'I have a great opportunity for you' messages get auto-deleted. The templates below are designed to be AI-personalized against each candidate's real LinkedIn activity — recent posts, recent role changes, conference talks, GitHub projects.

Template 1 — Senior IC engineer (specific work reference)

Context: Reaching a senior engineer for a senior or staff engineering role. Best paired with AI personalization that scrapes their public work.

Hi [name] — saw your work on [specific project/post/talk]. The approach you took to [specific technical detail] is exactly the pattern my client is hiring for. Senior role at [company name], [comp range]. Open to a quick conversation?

Use when: Engineering roles where the candidate's public technical work is visible (GitHub, talks, blog).

Expected reply rate: 18–28% reply rate — referencing their specific work signals you actually read their profile.

Template 2 — Senior leader (career-arc framing)

Context: Reaching a Director / VP-level candidate. Frame in terms of their next career step, not the job's features.

Hi [name] — your trajectory from [previous role] to [current role] caught my attention. There's a [target role] open at a [stage / context] that looks like a logical next step. Worth a 15-min conversation about whether the timing fits?

Use when: Senior leaders in mid-career with clear progression patterns.

Expected reply rate: 12–20% reply rate. Senior candidates respond to career-arc framing, not job-spec framing.

Template 3 — Passive candidate via mutual connection

Context: Mutual connection to the candidate. Use the mutual to break in.

Hi [name] — [mutual name] mentioned you when I described what I'm hiring for. They thought you'd be exactly the right brain for [role]. Open to a quick chat? Happy to send a one-pager first if that's useful.

Use when: You can credibly cite a mutual who'd vouch for the introduction. Don't fake this.

Expected reply rate: 25–35% reply rate. Mutual referral is the highest-converting recruiter outreach there is.

Template 4 — Recently-changed-roles candidate

Context: Candidate who started a new role within the last 90 days. Don't pitch the next move; show you noticed.

Hi [name] — congrats on the move to [new company]! Genuinely curious how the first weeks have been; the team there has been doing interesting work. (Not pitching anything; just wanted to say it.)

Use when: Candidate just changed roles. Pitching them to move again would burn the relationship; this is relationship-investment outreach for 12 months out.

Expected reply rate: 15–22% reply rate. The non-pitch makes it disarming.

Template 5 — Conference / talk follow-up

Context: Candidate gave a recent talk, panel, or webinar. Reference it specifically.

Hi [name] — caught your talk at [event]. The point about [specific argument] resonated; been thinking about it for the last week. Reaching out because I'm hiring for [role] at a company solving exactly that problem. Worth a chat?

Use when: Within 30 days of the talk. Reference the specific argument or moment, not just 'great talk.'

Expected reply rate: 20–30% reply rate when the reference is specific enough to be unfakeable.

Template 6 — Re-engagement (cold candidate from 6+ months ago)

Context: Candidate who didn't move on a previous opportunity. Re-engage with new context.

Hi [name] — we connected last [season] about [previous role]. Different role/company on my desk now: [new context]. Worth a fresh look? If still not the right time, also a totally fine no.

Use when: 6–18 months after a previous outreach that didn't convert. Don't re-engage sooner; you'll feel pushy.

Expected reply rate: 15–25% reply rate. Re-engagement candidates already know who you are.

How to use these with AI personalization

These templates are scaffolds, not scripts. The strongest version of each comes from AI personalization that fills in the [specific reference] placeholders with real, prospect-specific details. Most of the reply-rate variance comes from the quality of those references — mail-merge filling them with generic strings underperforms hand-written, which AI synthesis can match at scale.

See how AI personalization works in Infonet.