2,500+ Vetted Specialists for Frontier AI Programs
Not Volume. Expertise.
A leading AI talent platform needed to rapidly scale its workforce for multiple concurrent frontier AI programs serving NVIDIA, Meta, Microsoft, Amazon, Google, and Tencent.
Generic annotation marketplaces could not meet the bar. The client needed PhD-level evaluators in computational biology, finance, and legal. Code reviewers fluent in Python, JavaScript, C++, Golang, Java, and TypeScript. Red teamers who understood adversarial testing across model architectures. All on payroll, vetted, ready to start within days.
The challenge was not finding people. It was finding the right people, at the right quality bar, at a speed that matched the pace of frontier model development.
Six Service Lines. One Bench.
RLHF and SFT
Preference ranking, reward model calibration, golden response generation, DPO training data. Multi-turn conversation design with turn-level metadata and evaluation criteria.
Red Teaming
Adversarial prompt suites exposing logic errors, unsafe behavior, instruction non-compliance, and judge inconsistency. Converted failures into targeted SFT training sets.
Code Evaluation
Cross-model comparison across 7+ models. Python, JS, C++, Golang, Java, TypeScript. Correctness, complexity, edge-case handling. Gold-standard reference solutions for RLHF/SFT datasets.
PhD Evaluators
Domain experts in computational biology, finance, legal, healthcare, STEM. Assessed model outputs for factual accuracy, domain-specific hallucinations, and regulatory compliance.
ML Engineering
Production ML engineers and DevOps. CI/CD pipelines across Python, CloudFormation, Java, Node.js on AWS. Team management and code review at scale.
LLM Benchmarking
Human-in-the-loop evaluation comparing AI agent response quality. Benchmark validation. Dataset limitation identification across text-based and multi-modal inputs.
Delivered Across Frontier Programs
Multi-turn instruction/response conversations with golden responses and metadata. Calibrated scoring for consistency. Detected misalignment, unsafe behavior, instruction non-compliance. Team progressed from Trainer to Pod Lead to Calibrator.
Same prompt suite across 7+ models. Advanced DS/Algo to PhD-level domain problems (finance, physics). Built failure taxonomy and gold-standard response set supporting downstream RLHF/SFT dataset creation.
OSWorld-style tasks across 8+ app domains. Benchmarked against Claude family variants. Generated SFT training sets from failure modes using structured Annotator patterns. Improved evaluator robustness.
Application deployment through GitHub Actions pipelines. Python, CloudFormation, Java, JavaScript, Ruby, Node.js. Managed team of 6 engineers. Completed within timeline.
Kaggle dataset workflows (regression, NLP, prediction). Prompt refinement to guide LLMs to correct outputs. Human-in-the-loop evaluation comparing AI agent quality across standardized datasets.
The Full Stack of AI Training
Hands-On With Frontier Models
Our specialists worked directly with these model families across training, evaluation, and red teaming. Cross-model comparison was core: same prompt suites across 7+ models to benchmark and generate improvement data.
The AquSag Difference
Payroll, Not Marketplace
Every specialist on AquSag's payroll. Not gig workers. Not freelancers sourced on demand. Consistent quality, institutional knowledge that carried across projects, under 5% annual churn. When one program ended, specialists redeployed to the next from the same vetted pool. Zero ramp-up. Zero re-sourcing.
Triple-Vetting Bar
Technical interviews, assessments, and client-specific delivery rounds. Not resume screening. Production-grade qualification before any deployment.
Surge Infrastructure
1,000 candidates through vetting in 5 working days when a program needed to scale urgently. The bench absorbed demand spikes without compromising quality.
Role Progression
Trainer to Pod Lead to Calibrator. Internal career path meant the client retained experienced specialists who grew in responsibility and quality ownership.
Speed to Production
Contract signed within 2 weeks. Sourcing began immediately. First invoice raised within 30 days. From standing start to full production delivery in under 2 months.
The engagement proved that a pre-vetted, on-payroll bench with deep domain expertise can match the quality bar of the world's most demanding AI programs while delivering at a speed that marketplace models cannot.
AquSag Internal Review, 2025Ready to scale your AI workforce?
2,500+ vetted specialists. RLHF, red teaming, code evaluation, PhD domain experts. On payroll. Deployable in days.
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