Command Center
Real-time overview of your talent engineering operations
Active Searches
24
↑ 3 this week
Placements YTD
7
↑ 2 this quarter
Revenue YTD
$182K
↑ 34% vs Q1 target
Avg Score-to-Place
4.3
↑ 0.2 from calibration
Pipeline Velocity
Submissions → Placements over time
Revenue by Month
Placed revenue + projected pipeline
Hot Pipeline
Candidates most likely to close in the next 30 days
| Candidate | Company | Role | Score | Status | Fee Est | Days Active |
|---|---|---|---|---|---|---|
| Alice Chen |
A
Anthropic Frontier AI |
Sr. Agent Engineer | 4.6 | ● Offer Stage | $52K | 18d |
| Marcus Rivera |
D
Databricks Data Platform |
Staff MLE | 4.4 | ● Final Round | $48K | 24d |
| Priya Patel |
S
Scale AI AI Infrastructure |
Data Eng Manager | 4.2 | ● Interviewing | $46K | 12d |
| James Okafor |
O
OpenAI Frontier AI |
Research Engineer | 4.1 | ● Submitted | $55K | 6d |
Action Items Today
4 overdue
Follow up: Alice Chen offer
Anthropic extended verbal offer 2 days ago. Need written confirmation.
Prep Marcus for Databricks final
System design interview Thursday. Run /placement-ops prep.
30-day check-in: Wei Zhang at Stripe
Placed March 8. First retention check-in due.
Submit James to OpenAI
Score: 4.1 — above threshold. Package ready to send.
Latest Scan Results
12 new
Anthropic — ML Platform Engineer
SF/Remote · $220-280K · Posted 2 days ago
PyTorchK8sMLflow
Databricks — Staff Data Engineer
Remote · $250-320K · Posted 1 day ago
SparkDelta Lakedbt
Scale AI — Head of ML Engineering
SF · $300-400K + equity · Posted today
LeadershipLLMInfra
Stripe — Senior ML Engineer
Remote US · $200-260K · Posted 3 days ago
PythonTensorFlowRay
Portal Scanner
Crawling 154 company portals across 14 categories
placement-ops scan — April 8, 2026
PLACEMENT-OPS SCAN — 154 portals · Data/ML/AI niche
═══════════════════════════════════════════════════════════
✓ Frontier AI Labs (13 portals)
● Anthropic 3 new roles via Greenhouse API
● OpenAI 5 new roles via Playwright
● DeepMind 2 new roles via Playwright
○ Cohere 0 new via Greenhouse API
● Mistral AI 1 new role via WebSearch
○ Inflection AI 0 new via Playwright
...
✓ AI Infrastructure (25 portals)
● Databricks 4 new roles via Greenhouse API
● Scale AI 2 new roles via Playwright
● Anyscale 1 new role via Greenhouse API
● Weights & Biases 1 new role via Greenhouse API
...
✓ Big Tech (14 portals)
● Stripe 3 new roles via Playwright
● Meta AI 6 new roles via Playwright
○ Apple ML 0 new via WebSearch
...
═══════════════════════════════════════════════════════════
SCAN COMPLETE
Portals scanned: 154
New roles found: 38
Match your niche: 12
Duplicates removed: 6
Expired filtered: 4
→ 12 new roles added to pipeline
Saved to data/scan-history.tsv
Matched Roles (12)
Ready to evaluate
| Company | Role | Level | Location | Comp Range | Priority | Scan Method |
|---|---|---|---|---|---|---|
| Anthropic | ML Platform Engineer | Senior | SF / Remote | $220-280K | HIGH | Greenhouse |
| Anthropic | Agent Systems Engineer | Senior | SF | $240-300K | HIGH | Greenhouse |
| OpenAI | Research Engineer — Evals | Mid-Senior | SF | $250-340K | HIGH | Playwright |
| Databricks | Staff Data Engineer | Staff | Remote | $250-320K | MED | Greenhouse |
| Scale AI | Head of ML Engineering | Director | SF | $300-400K | HIGH | Playwright |
| Stripe | Senior ML Engineer | Senior | Remote US | $200-260K | MED | Playwright |
| Meta AI | Research Scientist — LLM | Senior | Menlo Park | $230-310K | MED | Playwright |
| Mistral AI | ML Engineer | Senior | Paris / Remote | €120-160K | LOW | WebSearch |
Candidate Evaluation
Taxonomy-driven scoring with 8-dimension analysis + competency mapping
Compatibility Matrix
Alice Chen → Anthropic Sr. Agent Engineer
Composite Score
4.6
Est. Fee
$52,000
Role Summary
CompanyAnthropic
RoleSr. Agent Engineer
ArchetypeIC-AI
LevelSenior (L5)
LocationSF / Remote
Comp$240-300K
Taxonomy Match
1.0 Python · exact
1.0 LangChain · exact
1.0 RAG · exact
0.9 PyTorch · exact × production
0.6 TensorFlow · adjacent to PyTorch
0.54 Agent Eval · adj × prod × current
1.0 Kubernetes · exact
Recommendation
Strong Submit. Alice exceeds hard requirements on agent systems and LLM infrastructure. Only gap: no formal evaluation framework experience — mitigate by highlighting her A/B testing work as adjacent.
Competency Assessment
How Alice works, not just what she knows
Pipeline Dashboard
24 active searches across 16 companies · $276K total pipeline value
Sourcing
8
Submitted
7
Interviewing
6
Offer / Close
3
All Active Requisitions
| # | Company | Role | Top Candidate | Score | Stage | Fee Est | Days |
|---|---|---|---|---|---|---|---|
| #001 | Anthropic | Sr. Agent Engineer | Alice Chen | 4.6 | ● Offer | $52K | 18 |
| #003 | Databricks | Staff MLE | Marcus Rivera | 4.4 | ● Final Round | $48K | 24 |
| #005 | Scale AI | Data Eng Manager | Priya Patel | 4.2 | ● Interviewing | $46K | 12 |
| #006 | OpenAI | Research Engineer | James Okafor | 4.1 | ● Submitted | $55K | 6 |
| #008 | Stripe | Sr. ML Engineer | Wei Zhang | 4.3 | ● Placed ✓ | $44K | — |
| #009 | Meta AI | Research Scientist | — | — | ● Sourcing | $50K | 4 |
| #010 | Anyscale | ML Platform Eng | — | — | ● Sourcing | $38K | 2 |
Analytics
Funnel metrics, conversion rates, and revenue intelligence
Placement Funnel — Last 90 Days
Conversion rates by stage
Time-to-Fill Distribution
Days from sourcing to placement by archetype
Revenue per Hour Worked
Efficiency by client tier
Client Scoreboard
| Client | Searches | Placements | Conversion | Avg TTF | Revenue | $/Hr | Grade |
|---|---|---|---|---|---|---|---|
| Anthropic | 5 | 3 | 60% | 22d | $142K | $485 | A+ |
| Stripe | 3 | 2 | 67% | 28d | $88K | $380 | A |
| Databricks | 4 | 1 | 25% | 35d | $48K | $220 | B |
| Scale AI | 3 | 1 | 33% | 30d | $46K | $260 | B+ |
Hiring Forecast
Signal detection and expansion predictions for the next 90 days
Active Hiring Signals
Detected from funding rounds, job postings, exec hires, and expansion patterns
Cohere — Series D ($500M) announced last week
Historically, funding rounds of this size lead to 40-60% headcount increase within 6 months. ML Engineering team likely expanding from 45 to 70+.
9.2
Runway ML — New VP Engineering started 3 weeks ago
New engineering leaders typically initiate hiring within 30-60 days. VP came from Google DeepMind — likely building an ML research team.
7.8
Databricks — Job posting velocity up 340% in ML category
Posted 12 ML roles in the last 2 weeks vs. 3 in the prior month. Suggests new product initiative or team expansion mandate.
8.5
90-Day Hiring Probability Heat Map
Company × role archetype probability
Client Expansion Predictions
Existing clients likely to need additional hires
| Client | Last Placement | Expansion Signal | Probability | Est. New Roles | Action |
|---|---|---|---|---|---|
| Anthropic | Feb 2026 | Team doubled since our last placement | HIGH | 2-3 | Reach out this week |
| Stripe | Mar 2026 | New ML product line announced | MED | 1-2 | Monitor signals |
Market Benchmark
Talent scarcity, compensation data, and competitive positioning
placement-ops benchmark — Senior Agent/LLM Engineer
MARKET BENCHMARK — Senior Agent/LLM Engineer · Bay Area / Remote
═══════════════════════════════════════════════════════════
TALENT SCARCITY INDEX
Demand (open roles): 342 across tracked companies
Supply (est. qualified): ~180 in US talent pool
Demand/Supply ratio: 1.9x (critical scarcity)
Typical time to fill: 35-50 days
Scarcity Score: ████████████████████░ 9.1 / 10
→ Justifies retained search or premium contingency (25%+)
COMPENSATION BENCHMARKS
Company Type P25 P50 P75 P90
────────────────────────────────────────────────────
Frontier AI Lab $220K $260K $300K $340K
AI Infrastructure $200K $235K $270K $310K
Big Tech $210K $250K $290K $330K
Enterprise SaaS $180K $210K $240K $270K
Note: Base salary only. Total comp (base + equity + bonus)
typically adds 30-60% at funded startups.
FEE RANGE ESTIMATE
Contingency (20%): $44K - $60K
Premium (25%): $55K - $75K
Retained (25%+): $55K - $85K (recommended for this scarcity)
→ HIGH SCARCITY = LEVERAGE. Recommend retained or 25% minimum.
Company Fit Heat Map — Alice Chen
Compatibility score by company tier
Talent Strategy
Workforce planning and org design — Anthropic ML Team (8 → 15)
Current vs. Target Org
VP Engineering
Sarah Chen · Existing
EM — Training
Existing · 3 reports
EM — Platform
Hire #5 · Month 7
TL — Agents
Hire #1 · CRITICAL
Sr MLE
Sr MLE
DE
ML Plat
Sr MLE
Agent
Existing
New Hire
Hiring Sequence
Phase 1 — Months 1-3
Sr. Agent/LLM Engineer (Tech Lead)
$220-260K · Unblocks Q4 agent system · $44-52K fee
Phase 1
Senior MLE (Agent-focused)
$200-240K · Pairs with Hire #1 · $40-48K fee
Phase 2 — Months 4-6
Sr. ML Platform Engineer
$200-240K · Eliminates bus factor · $40-48K fee
Phase 2
Agent Engineer (Mid-level)
$160-190K · Architecture defined, now build · $32-38K fee
Phase 3 — Months 7-12
Engineering Manager + Eval Eng + Jr MLE
3 hires · Scaling layer · $106-126K fee
Total Fee Opportunity
$262K — $312K
7 hires over 12 months
Risk Assessment
HIGH — Agent Tech Lead (Hire #1)
Linchpin hire. 60+ day fill = Q4 goal at risk. Recommend retained search.
MED — EM span at 11 by Phase 2
Burnout risk. Promote a senior to Tech Lead as interim bridge.
MED — H1B constraint
Removes ~30% of senior ML pool. Expand remote search.
Competency Gap Analysis
Retention Tracking
Post-placement health monitoring · 93% retention rate (above 90% target)
Retention Rate
93%
↑ above 90% target
Active Placements
7
In Guarantee Period
3
Check-ins Due
2
Check-in Schedule
Mar 8 — Day 1
Wei Zhang started at Stripe
Sr. ML Engineer · Score at placement: 4.3
Apr 7 — Day 30 (TODAY)
30-Day Check-in Due
Ask: onboarding experience, team dynamics, early wins, any concerns
May 7 — Day 60
60-Day Check-in
Focus: meaningful contributions, manager feedback, comp satisfaction
Jun 1 — Day 85
Pre-Guarantee Warning
5 days before guarantee expires. Final check with both sides.
Jun 6 — Day 90
Guarantee Period Ends
Fee fully earned. Transition to long-term retention tracking.
Placement Health Dashboard
| Candidate | Company | Day | Health | Next Check-in |
|---|---|---|---|---|
| Wei Zhang | Stripe | 30 | ● Healthy | Today |
| Diana Torres | Anthropic | 65 | ● Healthy | May 2 |
| Ryan Kim | Scale AI | 45 | ● Watch | Apr 10 |
| Sarah Park | Databricks | 120 | ● Retained | Jun 15 |
| Jay Patel | Anthropic | 180 | ● Retained | Sep 1 |
⚠ Alert: Ryan Kim (Scale AI)
Manager feedback indicates slower onboarding than expected. Schedule a 3-way call to align on 60-day expectations. Still within recoverable range.
Calibration Engine
Learning from outcomes to improve matching accuracy
placement-ops calibrate — 15 outcomes logged
CALIBRATION ANALYSIS — 15 outcomes (7 placed, 5 rejected, 3 withdrawn)
═══════════════════════════════════════════════════════════
SCORE-TO-OUTCOME CORRELATION
Score Range Placed Rejected Withdrawn Hit Rate
──────────────────────────────────────────────────────
4.5+ 4 0 0 100%
4.0 – 4.4 3 2 1 50%
3.5 – 3.9 0 3 2 0%
→ INSIGHT: Your threshold of 4.0 is correct.
Candidates below 4.0 have a 0% placement rate. Don't submit them.
ADJACENCY CONVERSION RATES
✓ TensorFlow → PyTorch: 83% accepted (adjacency validated)
✓ Airflow → Prefect: 75% accepted (adjacency validated)
✗ Pandas → Spark: 20% accepted (adjacency too generous)
~ React → Vue: 50% accepted (context-dependent)
→ RECOMMENDATION: Reduce Pandas→Spark adjacency from 0.6 to 0.3
Spark requires distributed systems thinking that Pandas doesn't build.
COMPANY-SPECIFIC PATTERNS
Anthropic: Strict on production experience. "Project-level" depth rejected 100%.
Stripe: Cares more about system design than specific frameworks.
Scale AI: Values velocity. Candidates with startup backgrounds preferred 3:1.
═══════════════════════════════════════════════════════════
Calibration data saved to data/calibration.yml
Next calibration recommended after 5 more outcomes.
Batch Evaluation
Parallel candidate comparison with ranked output
placement-ops batch — 4 candidates vs. Anthropic Sr. Agent Engineer
BATCH EVALUATION — Anthropic · Senior Agent Engineer · IC-AI
═══════════════════════════════════════════════════════════
RANKED RESULTS
RANK CANDIDATE COMPOSITE TECH SENIOR FILL VERDICT
─────────────────────────────────────────────────────────────────
#1 Alice Chen 4.6 4.8 5.0 4.5 STRONG SUBMIT
#2 Marcus Rivera 4.1 4.3 4.0 3.8 SUBMIT
#3 Priya Patel 3.7 3.5 4.0 3.2 MAYBE
#4 David Kim 2.9 2.5 3.0 2.0 HARD PASS
HEAD-TO-HEAD: #1 vs #2
Dimension Alice Chen Marcus Rivera
─────────────────────────────────────────────────
Technical Match 4.8 ████████ 4.3 ███████░
Seniority Fit 5.0 ████████ 4.0 ██████░░
Location 5.0 ████████ 5.0 ████████
Comp Alignment 4.0 ██████░░ 4.5 ███████░
Culture Signals 5.0 ████████ 4.0 ██████░░
Gap Severity 4.0 ██████░░ 3.5 █████░░░
Presentation Risk 4.5 ███████░ 4.0 ██████░░
Fill Probability 4.5 ███████░ 3.8 █████░░░
→ RECOMMENDATION: Submit Alice first, Marcus as backup.
Alice wins on 6/8 dimensions. Marcus has better comp alignment
but lower fill probability due to competing offers.
Market Intelligence
87 companies hiring · 342 open ML/AI roles · Comp hygiene + competitive landscape
Companies Hiring
87
↑ 12 new this month
Open ML/AI Roles
342
↑ 18% vs. last quarter
Market P50 (Sr MLE)
$260K
↑ $15K from 6 months ago
Addressable Fees
$4.2M
Competitive Landscape by Industry
SF + NYC + Remote · Last 90 days
| Industry | Companies | Open Roles | Avg Comp (P50) | Velocity | Recruiter Density |
|---|---|---|---|---|---|
| Frontier AI Labs | 8 | 68 | $260K | ↑↑ Growing fast | HIGH |
| AI Infrastructure | 14 | 56 | $235K | ↑↑↑ Surge | MED |
| Big Tech (AI Div) | 12 | 82 | $250K | → Flat | HIGH |
| Enterprise SaaS (ML) | 22 | 64 | $210K | ↑ Growing | LOW |
| Fintech (Data/ML) | 16 | 42 | $215K | ↑ Growing | LOW |
| Healthtech (ML) | 8 | 18 | $195K | ↑ Growing | LOW |
| Autonomous / Robotics | 7 | 12 | $240K | → Flat | MED |
Comp Trends — Senior ML Engineer (P50)
Talent Flow — Last 90 Days
Where ML talent is moving
Top Talent Sources (losing people)
Meta AI12
Google DeepMind8
Amazon ML6
Top Destinations (gaining people)
Anthropic14
OpenAI11
Databricks8
Adjacent Industry Opportunities
Companies in proximate industries with ML hiring activity — lower recruiter competition
| Industry | Example Companies | Open ML Roles | Avg Comp | Recruiter Competition | Fee Potential |
|---|---|---|---|---|---|
| Fintech | Stripe, Block, Plaid, Brex, Ramp | 42 | $215K | LOW | $840K |
| Healthtech | Tempus, Recursion, Flatiron, PathAI | 18 | $195K | LOW | $351K |
| Autonomous | Waymo, Cruise, Nuro, Figure AI | 22 | $240K | MED | $528K |
| Defense/Gov | Palantir, Anduril, Shield AI | 12 | $210K | LOW | $252K |
Insight: Adjacent industries represent $1.97M in addressable fees with significantly lower recruiter competition than Frontier AI. Fintech ML teams are the best cross-sell — overlapping skill sets with your existing candidate bench, and you already have a Stripe relationship.
Integrations
ATS, HRIS, and data source connections feeding real-time data into your pipeline
🌿
Greenhouse
● Connected
Syncing jobs, candidates, scorecards, and offers every 30 minutes.
Last sync
3 min ago
Jobs synced
12
Candidates
34
Stage changes today
8
🔍
Portal Scanner
● Active
Crawling 154 company career pages daily via Playwright + Greenhouse API + WebSearch.
Last scan
Today 9:00 AM
Portals tracked
154
New roles found
12 today
🌐
Google Jobs (SerpAPI)
● Connected
Competitive intel: scanning ML/AI job postings across all employers for comp ranges and market trends.
Last scan
Today 6:00 AM
Companies tracked
87
Roles indexed
342
Available Integrations
Connect additional data sources to enrich your intelligence
| Platform | Type | What You Get | Cost | Status |
|---|---|---|---|---|
| Merge.dev | Universal ATS/HRIS | Connect 50+ ATS platforms through one API | Free (3 accounts) | Recommended |
| Lever | ATS | Postings, candidates, opportunities, offers | Free (API) | Not connected |
| Ashby | ATS | Jobs, candidates, interview schedules | Free (API) | Not connected |
| LinkedIn Jobs | Intel | Company-specific posting data, headcount | Proxycurl ($50/mo) | Not connected |
| Crunchbase | Signals | Funding rounds → hiring predictions | Basic ($29/mo) | Not connected |
| BambooHR | HRIS | Employee data (company mode) | Free (API) | Not connected |