Title tier of relevance_score accepts token containment

Live case: the job titled "Generative Engineer" sourced a pool titled
"Generative AI Engineer" -- the exact phrase never occurs in that title,
so every genuine match fell through to the scattered 35 tier and the
whole pool compressed into the 40s. A current title containing every
job-title token now earns the full 55. The headline tier stays
phrase-only so keyword-stuffed headlines still cap at the 35 tier.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

backend/talent/plugins.py

@@ -418,11 +418,17 @@ def _match_tokens(*texts) -> set[str]:
 def relevance_score(job: dict, profile: dict) -> int:
     """0-100 job-fit rank for sorting, computed when a profile is persisted.
 
-    Deterministic and free. Title component: the job title as an exact PHRASE
-    in the person's current title scores 55, in their headline 45; scattered
+    Deterministic and free. Title component: a current title CONTAINING every
+    job-title token scores 55 — containment, not exact phrase, because job
+    titles rarely reappear verbatim ("Generative Engineer" vs the pool's
+    "Generative AI Engineer"; seen live: the phrase rule dropped every real
+    match to the scattered tier and compressed the whole pool into the 40s).
+    The job title as an exact phrase in the headline scores 45; scattered
     token overlap caps at 35 — a keyword-stuffed headline ("AI/ML Engineer |
     Python | FastAPI | ...") must not outrank someone whose title IS the job
-    title, which is exactly what token overlap alone did on live data.
+    title, which is exactly what token overlap alone did on live data. The
+    headline tier stays phrase-only for the same reason: stuffed headlines
+    contain every token of every hot title.
 
     Skills component (up to 45): GRADED token overlap between the content
     words of the job's requirements + optional skills and the person's
@@ -432,16 +438,20 @@ def relevance_score(job: dict, profile: dict) -> int:
     whole live pool on exactly 60.
     """
     job_title = _clean_phrase(job.get("title"))
+    job_title_tokens = set(job_title.split())
     title_text = _clean_phrase(profile.get("current_title"))
     headline_text = _clean_phrase(profile.get("headline"))
-    if job_title and job_title in title_text:
+    if job_title and job_title_tokens <= set(title_text.split()):
         title_component = 55.0
     elif job_title and job_title in headline_text:
         title_component = 45.0
     else:
-        title_tokens = set(job_title.split())
         role_tokens = set(title_text.split()) | set(headline_text.split())
-        ratio = len(title_tokens & role_tokens) / len(title_tokens) if title_tokens else 0.0
+        ratio = (
+            len(job_title_tokens & role_tokens) / len(job_title_tokens)
+            if job_title_tokens
+            else 0.0
+        )
         title_component = 35 * ratio
 
     job_tokens = _match_tokens(

backend/tests/test_talent_plugins.py

@@ -404,7 +404,25 @@ def test_headline_phrase_scores_below_title_phrase():
     scattered = plugins.relevance_score(job, {"current_title": "Engineer", "headline": "Agentic AI | Python"})
     assert in_title == 55
     assert in_headline == 45
-    assert scattered == 35  # both tokens present but never as the phrase
+    assert scattered == 35  # tokens split across title and headline never combine
+
+
+def test_title_containment_scores_like_an_exact_title():
+    # Live case: job "Generative Engineer", pool titled "Generative AI
+    # Engineer" — the exact phrase never occurs, so every genuine match fell
+    # to the scattered 35 tier and the whole pool compressed into the 40s.
+    job = {"title": "Generative Engineer", "requirements": [], "optional_skills": []}
+    interleaved = plugins.relevance_score(job, {"current_title": "Generative AI Engineer"})
+    senior = plugins.relevance_score(job, {"current_title": "Senior Generative AI Engineer"})
+    assert interleaved == 55
+    assert senior == 55
+    # Containment applies to the TITLE only: the same tokens scattered across
+    # a keyword-stuffed headline still cap at the 35 tier.
+    stuffed = plugins.relevance_score(
+        job,
+        {"current_title": "Developer", "headline": "Generative AI | Engineer | Python"},
+    )
+    assert stuffed == 35
 
 
 # ---------------------------------------------------------------- detail extraction