I need to hire a Staff Machine Learning Engineer for our AI agents team. They need deep Python, LLM application experience, production ML systems, and ideally experience with search, ranking, or recommendation systems. We prefer Seattle or Bay Area, but we can consider remote. Build the sourcing strategy, identify title variants, must-have and nice-to-have criteria, target companies, run the search, verify the candidate fit, and give me the first 10 strongest candidates with reasons.

Find rising-star AI infrastructure engineers who have evidence across GitHub, academic work, or public profiles. Focus on people with LLM systems, retrieval, evals, distributed inference, or agent infrastructure experience. Search across all relevant SeekOut verticals, dedupe the results, and rank candidates by evidence strength, not just title.

Map the AI platform and applied ML talent at Anthropic, OpenAI, Google DeepMind, Meta, and Databricks. I want to understand titles, seniority, skills, geographic distribution, likely feeder companies, and which candidate segments are most reachable for a startup.

Compare Seattle, San Francisco, New York, Toronto, and London for Staff-level ML infrastructure engineers with Python, distributed systems, and LLM production experience. Show talent pool size, top employers, common titles, skill concentration, and which market gives us the best odds.

I like this candidate profile because they have startup experience, strong backend systems depth, and recent LLM product work. Reverse-engineer what makes them a strong fit, then find 15 similar candidates. Avoid clones from the same company unless they are exceptional.

Here is my feedback on the slate: candidate 1 is too enterprise SaaS, candidate 2 is great because of hands-on infra depth, candidate 3 is too research-heavy, and candidate 4 is close but too junior. Refine the search and bring me a better slate of 10 candidates.

Build a ranked shortlist of the top 5 candidates for this role. For each person, include why they fit, what evidence supports the match, concerns or gaps, suggested interview focus areas, and a one-line hiring manager summary.

Find Federal Account Executives in the DC metro area with security clearance signals, experience selling into federal civilian or defense agencies, and prior work at defense contractors or federal SaaS vendors. Show me the best candidates and explain which clearance or federal-sales signals you found.

Find cardiologists licensed in California who have experience with heart failure or electrophysiology and are affiliated with major hospital systems. Prioritize people in the Bay Area or Los Angeles, but show me how much the pool expands statewide.

Find nurse practitioners in Texas with emergency medicine or urgent care experience. Include license-state signals, specialties, likely current employers, and broaden the search carefully if the pool is too small.

Find academic and industry experts in AI evaluation, LLM benchmarking, and agent reliability. Prioritize people with publications, conference signals, or strong research depth. Give me a ranked expert map and identify who might be reachable for an industry role.

Search our internal talent for people who could move into an AI solutions engineering role. Look for employees with customer-facing experience, Python or ML exposure, strong product judgment, and recruiting-domain knowledge. Group them by readiness: ready now, ready with training, and long-shot.

Analyze our bench depth for three critical roles: Principal ML Engineer, Staff Backend Engineer, and Product Manager for AI Recruiting. Show who internally has overlapping skills, where we have gaps, and which roles are most exposed if someone leaves.

Search our ATS for past candidates who could fit a Senior Backend Engineer role today. Focus on people who reached onsite or final stages, were rejected for timing or compensation reasons, and now have more relevant experience. Rank the best rediscovery candidates.

Give me a pipeline overview for our active engineering reqs. Break down candidates by stage, recruiter, rejection reason, and timeline. Highlight bottlenecks, stale stages, and where we should intervene this week.

For the top 5 candidates in this slate, draft personalized outreach. Make each message specific to their background, avoid generic AI-sounding language, and give me one short LinkedIn version and one email version per person.

For the top 20 candidates, retrieve available emails, add them to a workspace called “Staff ML Infra Q3,” and prepare them for export to our ATS. Before using credits or exporting, summarize what will happen and ask for confirmation.

Analyze this talent pool for diversity and representation signals. Then suggest ways to broaden the search without lowering the bar: alternate titles, adjacent companies, schools, geographies, and non-obvious candidate backgrounds.

For this Senior Product Manager, AI Recruiting role in Bellevue, give me compensation context and market difficulty. Compare Bellevue, San Francisco, New York, and remote. Tell me whether our likely range is competitive and where we may struggle.

We need to hire a Staff Machine Learning Engineer for an AI recruiting agents team. The person needs production LLM experience, Python, distributed systems, search/ranking/recommendation experience, and strong product instincts. Seattle or Bay Area preferred, remote acceptable. Start by turning this into a sourcing strategy. Then compare Seattle, Bay Area, New York, and Toronto. Search public, GitHub, and academic sources. Build a ranked shortlist of 10 candidates with evidence. Identify which 5 I should contact first, draft personalized outreach, create a workspace, and prepare the candidates for ATS export after I approve.