Engine Phase 2: fix the pre-Opus funnel (lifts ALL matches)

The sift was feeding Opus a magnitude-blind, under-trusted, truncated shortlist — only 68% of the
genuinely-best jobs reached the curator. Fixes:
- embed.py: embed the FULL description (was desc[:600]) + role_category + industry; richer profile
  text (current_role + experience summary + seniority). The best semantic signal, fed real content.
- sift.py: magnitude-preserving min-max fusion (was rank-position, which flattened cosine 0.95 vs
  0.72) and embedding-LED weights (W_VIBE 0.6 / W_WHITEBOX 0.4). Embed runs FIRST so the white-box
  f_semantic reuses the real cosine (threaded as job["_vibe_cosine"]) instead of token overlap.
- config.py: SIFT_TOP_K 18 → 28 (the gate was cutting good jobs before Opus).
- curate.py: richer Opus briefs — full responsibilities (was desc[:500]) + role_category/industry/
  seniority/pay, so even the shortlist is fully described.

Regression (120 pairs): sift membership recall 0.68 → 0.92, score MAE 24.7 → 18.4. 31 tests pass.

app/engine/embed.py

@@ -11,24 +11,32 @@ from app.engine.llm import embed_client
 
 
 def _job_text(job: dict) -> str:
+    # The embedding is the best semantic matcher — feed it the FULL signal, not a 600-char stub.
+    # role_category + industry anchor non-tech JDs (narrative prose, few repeated title tokens).
     d = job.get("details") or {}
     parts = [
         job.get("title", ""),
         job.get("organization", ""),
-        " ".join(d.get("skills") or []),
-        (d.get("description") or "")[:600],
+        d.get("role_category") or "",
+        d.get("industry") or "",
+        " ".join(d.get("skills") or job.get("required_skills") or []),
+        (d.get("description") or "")[:2000],
     ]
     return " · ".join(p for p in parts if p)
 
 
 def _profile_text(prefs: dict, ctx: dict | None) -> str:
     ctx = ctx or {}
+    # Embed the candidate richly too — current_role + experience summary + seniority, not just title+skills.
+    exp = ctx.get("experience_summary") or ctx.get("summary") or ""
     parts = [
         prefs.get("title", ""),
         " ".join(prefs.get("role") or []),
-        " ".join(ctx.get("skills") or []),
         ctx.get("current_role", "") or "",
+        " ".join(ctx.get("skills") or []),
         " ".join(prefs.get("industry") or []),
+        " ".join(prefs.get("experience") or []),
+        str(exp)[:1200],
     ]
     return " · ".join(p for p in parts if p)