Analysts on a named pod, not IDs in a queue
India produces vast engineering and STEM talent that currently reaches AI labs as unmanaged freelancers with high variance. We organise that supply into vetted, trained, calibrated teams and pay it properly.
- Pass rate
- ~10%
- Reviewer ratio
- 1 : 6
- Gold seeding
- 3–5%
- Calibration
- Weekly
Selection is the quality strategy. Paid training and a real ladder from contributor to reviewer to lead is what keeps churn, and therefore recalibration cost, low.
Roughly one in ten applicants reaches the bench.
The pass rate is itself a client-facing selling point. Every stage after screening is paid, because asking people to work for free to prove they can work selects for the wrong things.
- Source100%
Campus networks, referrals, engineering and Kaggle communities, IIT/NIT/IIIT alumni groups.
- Screen~30%
Structured application plus an async, domain-matched skills test.
- Assess~40%
A paid trial batch on gold tasks and a live rubric interview.
- Onboard~90%
KYC, NDA and a paid calibration week before any client work.
- Bench~10% e2e
Skills-tagged, score-tracked and deployable. Roughly one in ten applicants.
Contributors are professionals here.
Low churn is a quality strategy, not an HR nicety. A pod that stays calibrated is worth more than a pod that is cheap.
Paid training
Trial batches and the calibration week are paid. You are never asked to work for free to prove you can work.
Transparent pay
Top-quartile Indian technical freelance rates, published for the role, paid on time every cycle.
A real ladder
Contributor → reviewer → senior reviewer → project lead. Most project leads are promoted from the contributor base.
Named, not anonymous
You are a Newbieget Labs analyst on a named pod with a lead who knows your work, not an ID in a queue.
Weekly feedback
A personal quality dashboard with your acceptance rate, gold-task accuracy and review outcomes. No black-box scoring.
Real work
Frontier evaluation, agent benchmarking, RLHF for teams building the models, not bounding boxes.
A path no gig platform offers.
The ladder is the retention engine. Most project leads are promoted from the contributor base, because they already know the rubrics.
- 01Contributor→Reviewer
Sustained personal acceptance rate and gold-task accuracy above the floor.
- 02Reviewer→Senior Reviewer
Review accuracy against audit; demonstrated coaching of contributors.
- 03Senior Reviewer→Project Lead
Rubric authorship and ownership of a QA gate across a full program.
- 04Project Lead→Head of Delivery
Program-level SLA ownership across multiple concurrent clients.
Software Engineering
Python, JS/TS, Go, Rust, systems
Machine Learning
Training pipelines, evaluation methodology
Medicine
Practising clinicians for clinical-safety review
Law
Qualified practitioners for legal-reasoning tasks
Finance
CAs and analysts for quantitative review
Mathematics
Proof verification and formal reasoning
Sciences
Physics, chemistry, biology at graduate level
Linguistics
Multilingual evaluation and annotation
Applications
Technical contributors, reviewers and expert-bench specialists. Screening is a structured application plus a domain-matched skills test.