Monthly Technical Interview Retainer for Hiring Teams
Consistent technical evaluation capacity for teams hiring AI, ML, and data talent at volume.
What this service is
A recurring interview-as-a-service package for companies hiring directly. Designed for end clients that need ongoing support evaluating technical candidates across multiple openings or a steady stream of applicants. The focus is on reducing interviewer load, improving consistency in candidate evaluation, and giving hiring teams clearer hiring recommendations through structured interviews and scorecards.
Companies with active AI/ML hiring programs face a consistent problem: the engineers best qualified to interview candidates are also the ones you most need building things. Pulling a senior ML engineer off a sprint to conduct three candidate interviews a week is an expensive trade-off — and the quality of those interviews often reflects it. The Monthly Retainer for End Clients replaces ad hoc internal interviewing with a structured, recurring evaluation service calibrated to your active roles. You get consistent candidate assessment, reduced load on your engineering team, and hiring recommendations delivered on a predictable schedule — so your pipeline keeps moving.
Problems this solves
- Your best engineers are spending 15–20% of their time on interviews — a direct drag on output that compounds during high-volume hiring periods
- Interview quality is inconsistent across your team: some engineers ask probing questions, others do not, and the feedback is hard to compare across candidates
- You have multiple AI/ML roles open simultaneously but cannot evaluate candidates fast enough to maintain hiring momentum
- Hiring decisions are delayed because you are waiting for internal engineers to complete evaluations before candidates can move forward
- Your evaluation process lacks a consistent framework, so scorecards look different every time and it is hard to compare candidates across a hiring cycle
What you receive
- Agreed monthly interview volume across your active openings
- Role-calibrated evaluation criteria for each position in the engagement
- Structured scorecards with hire/no-hire recommendation per candidate
- Consistent evaluation criteria applied across all candidates throughout the engagement
- Regular cadence and predictable delivery timeline agreed at setup
How it works
Discovery call
We align on your active roles, seniority targets, evaluation priorities, and monthly volume. Once agreed, the retainer agreement is signed and the engagement is formally set up.
Kickoff intake
You share role briefs for each open position, candidate submission preferences, scheduling logistics, and any context we need before interviews begin. This step establishes the evaluation framework for each role.
Ongoing evaluation
Candidates are evaluated as they move through your pipeline — consistently, every time. The same competency areas, the same scoring bar, the same structured output regardless of when in the engagement the interview happens.
Structured output
Scorecards and recommendations are delivered promptly so hiring decisions never wait on interview backlog. Invoiced monthly against the agreed retainer terms.
Who this is for
- Companies with ongoing or high-volume AI, ML, or data science hiring across multiple openings
- Hiring managers whose internal engineers are already overloaded and cannot sustain interview volume
- Organizations that want consistent evaluation standards applied to every candidate, regardless of who is available to interview
- Companies building AI/ML teams without deep internal interview expertise at the domain level
Roles we cover
This service covers AI, ML, and data science roles — each evaluated against domain-specific criteria, not a generic technical checklist.
AI Engineer Interviews
Expert evaluation for candidates building LLM-powered products, RAG systems, and AI-native applications
ML Engineer Interviews
Structured ML engineer interviews that evaluate both modeling depth and production engineering execution
Data Scientist Interviews
Structured data scientist interviews for teams who need to evaluate statistics, experimentation, modeling, and business judgment — all in one assessment
Ready to get started?
Buy a single interview or book a call to discuss ongoing capacity.
Frequently asked questions
How does the monthly volume commitment work?
At engagement setup, we agree on expected monthly interview volume based on your active headcount and hiring pace. If a month runs higher or lower than expected, we handle that within reasonable flexibility — the goal is predictable capacity, not rigid over/under billing. Volume adjustments are discussed at the start of each engagement month.
Can we adjust role coverage as our hiring needs change?
Yes. As you open new AI/ML/DS roles or close existing ones, we update the evaluation criteria accordingly. Role brief changes are handled through a quick update and applied from the next candidate onward. Ongoing roles maintain consistent evaluation criteria across all candidates.
Does this work alongside our internal interview process?
Yes — most clients use our evaluation as one stage in a broader process, not as a replacement for all interviews. A common structure is to use our technical evaluation for first-round or second-round screening, with final rounds handled internally. We calibrate to wherever in your process we can add the most reliable technical signal.
How do you maintain consistency across candidates in a long engagement?
Evaluation criteria are documented and applied consistently across every candidate in the engagement — the same competency areas, the same scoring bar, and the same structured output regardless of when the interview happens. This makes candidate comparison and retrospective analysis straightforward.
What happens if a candidate cancels or does not show?
Cancellations and no-shows are handled without charge. Only completed interviews count toward the monthly volume. We coordinate scheduling on your behalf or accept candidate coordinates from your team — depending on your preference.
Can we share scorecards with the hiring manager or other internal stakeholders?
Yes. Scorecards are written to be shareable with internal stakeholders — hiring managers, founders, department heads, or anyone involved in the hiring decision. They present evidence-backed evaluations in plain language so non-technical readers can engage with the findings without needing technical translation.
More questions? See the full FAQ or contact us directly.
Related services
Interview as a Service
Expert-led candidate evaluations — without pulling your engineers off real work.
ProductizedCustom Interview Design & Scorecard Development
Purpose-built interview frameworks and scorecards for the exact role you are hiring.
Standalone or add-onWritten Candidate Debriefs
Clear post-interview write-ups that make hiring decisions faster and more confident.
Ready to hire with more confidence?
Get a structured technical evaluation delivered by a practitioner who knows the domain — not a generic screener.