The AI specialist bench
that frontier programs
run on.
2,500+ vetted. RLHF, red teaming, code eval, PhD experts. Deploy in days.
We have vetted and deployed specialists for NVIDIA Nemotron, Amazon Nova, and Alibaba Qwen programs — RLHF and SFT trainers, red teamers, cross-model code evaluators, PhD domain experts in finance, legal, and STEM, ML engineers, MLOps, and DevOps. 300+ on payroll right now. Your tools, your PMs, your direction. No platform to adopt, no management layer to accept.
Not a marketplace.
A trained, on-payroll team.
Every specialist below is on the AquSag payroll, with hands-on delivery history in RLHF, red teaming, model evaluation, ML engineering, and post-training workflows. Available to join your program this week.
LLM integration, RAG systems, production NLP pipelines. Previously contributed to generative AI products serving 10M+ users.
Distributed training, fine-tuning pipelines, GPU cluster optimization. Shipped production models at two Fortune 100 companies.
Golden response generation, preference ranking, reward model calibration. Contributed to post-training at two frontier model labs.
Statistical modeling, A/B testing, recommendation engines. Domain expertise in fintech and e-commerce with regulatory data experience.
Model deployment, Kubernetes orchestration, CI/CD for ML, GPU cluster management. Maintained inference infrastructure at 99.9% uptime.
Named entity recognition, document classification, clinical NLP pipelines. Built production NLP systems processing 500K+ documents daily.
Multi-turn dialogue generation, adversarial prompt design, rubric development. Generated 10,000+ golden responses across math, code, and reasoning.
Multi-turn evaluation, failure taxonomy, cross-model benchmarking. 98% inter-annotator agreement across 3 concurrent evaluation programs.
LLM output validation, regression testing for non-deterministic systems, automated test pipeline development. Built QA frameworks for 3 production AI products.
Object detection, sensor fusion, real-time inference. Production experience in ADAS programs with LiDAR and camera fusion pipelines.
Cloud infrastructure, container orchestration, CI/CD for ML model serving. Managed inference pipelines handling 1M+ daily predictions.
Tool-calling architectures, agentic workflow design, computer-use benchmarking. Built and evaluated agent systems across 8+ task domains.
Availability updated weekly. Tell us your program requirements and we'll confirm capacity within 24 hours.
When your client programs surge beyond internal capacity — RLHF, red teaming, code evaluation, PhD domain review — AquSag supplements your bench. Behind your brand, inside your tools, under your management. No platform friction, no quality drop.
NVIDIA Nemotron. Amazon Nova.
Alibaba Qwen. 2,500+ specialists. 8 months.
A leading AI training platform needed to scale RLHF, red teaming, code evaluation, and PhD-level domain review across multiple concurrent frontier model programs — fast, to a high quality bar, with no churn. Generic annotation marketplaces couldn't meet the profile. We built a bench of 2,500+ vetted specialists from scratch and delivered to production in under 60 days.
From conversation to
engineers in your workflow
No procurement overhead. No platform onboarding. Four steps from first call to engineers actively working in your queue — not weeks from now, days.
Scope & Match
You tell us roles, domain, team size, and timeline. We match against the active bench within 24 hours. No job postings, no recruiters.
Profile Review
Review engineer profiles, run your own technical interviews if you want, and confirm the team. Every profile is real — no ghost candidates.
Activation
Engineers onboard into your tools, your workflow, your Jira. No AquSag process layer inserted. They show up where your team works.
Scale Anytime
Add engineers, swap domains, or scale down between sprints. Bench depth means 5 to 50 without proportional delay.
Getting onto the AquSag bench
is harder than most AI lab hires
Top 3% acceptance from applicant pool. Every engineer has shipped production AI programs — not coursework, not demos. This is what we actually verify before anyone joins the bench.
Technical Assessment
Domain-specific coding challenges, system design reviews, or annotation quality tasks — evaluated by senior engineers, not automated scoring. ML engineers solve real model debugging problems. RLHF specialists complete preference ranking calibration against known baselines.
Domain Depth Evaluation
Industry-specific knowledge assessment. A finance specialist answers questions about regulatory NLP. A healthcare engineer demonstrates HIPAA-aware pipeline design. An ADAS specialist walks through sensor fusion architecture. We verify domain depth, not just AI familiarity.
Production History Review
Portfolio and reference verification. We confirm prior project delivery, not skill claims. Every engineer on the bench has shipped work in a production AI program. Engineers who have only done coursework or personal projects do not make it past this stage.
Collaboration Screen
Live interview assessing clarity of technical communication, ability to work asynchronously across time zones, and experience inside client-managed teams. This is where we filter for engineers who integrate without friction — not just engineers who are technically capable.
Security, compliance, and IP protection — included by default
Enterprise AI programs require more than capable engineers. Every AquSag engagement is built on a compliance foundation that enterprise and frontier AI lab buyers require.
SOC 2 Type II
Operational security controls audited annually. AquSag's processes meet the security, availability, and confidentiality criteria required for enterprise AI programs.
GDPR Compliant
Data handling protocols for EU-resident specialist and client data. Lawful basis, data minimization, and rights management aligned with GDPR requirements.
IP Always Yours
All work product created during engagements belongs to the client. Signed at contract. No ambiguity, no carve-outs. Your models, your training data, your outputs.
NDA by Default
Every engagement begins with mutual NDA before any profile sharing. Every engineer signs client-specific confidentiality before activation. No exceptions.
Domain depth from
day one — not week four
Generic AI engineers slow you down. Our bench carries specialists with real program history in six high-demand verticals. Select a domain to see what that looks like in practice.
Regulatory AI, trading models, and risk systems that require domain fluency — not just ML familiarity
Finance AI fails when engineers don't understand regulatory NLP, the sensitivity of financial data, or the compliance constraints that govern every pipeline stage. Our finance-domain engineers have shipped production programs in trading model development, risk assessment AI, and large-scale document processing — and they arrive knowing the domain.
Clinical NLP, medical imaging, and life sciences AI — with HIPAA awareness built in
Healthcare AI programs stall when engineers need weeks to understand clinical terminology, HIPAA data handling, and the compliance expectations that govern every system. Our healthcare engineers have operated inside clinical data environments and know the constraints before day one.
Computer vision, sensor fusion, and real-time inference — with production ADAS program experience
Autonomous systems demand engineers who understand LiDAR processing, camera fusion pipelines, and real-time inference constraints at the hardware level. Our ADAS engineers have shipped in production autonomous driving programs — not just run academic benchmarks.
Recommendation engines, search ranking, and conversational AI at consumer scale
Consumer AI programs run at a scale and velocity that exposes gaps in engineers who've only worked in enterprise batch environments. Our consumer tech engineers have operated production systems handling millions of users and hundreds of millions of data points.
LLM integration, RAG systems, and AI-powered workflows for enterprise products
Enterprise AI integration requires engineers who understand multi-tenant architectures, SOC 2 compliance constraints, and the deployment cycles of enterprise software. Our SaaS engineers have shipped LLM integrations and RAG systems inside enterprise products with real compliance requirements.
Post-training programs, RLHF pipelines, and evaluation infrastructure — the deepest part of our bench
The most technically demanding programs we run. Our frontier lab bench includes engineers who have contributed to post-training at multiple frontier AI organizations — not as contractors sourced after you signed, but as active bench members who currently run RLHF and SFT programs.
a standing team
This is what separates a deployed-ready bench from a hiring pipeline. Measured across active AquSag programs.
Sustained across all active programs — not a launch-week number. The same engineers who hit 95%+ in month one maintain it in month six. Quality compounds because the team stays.
Versus 30–40% on gig platforms. On-payroll engineers accumulate institutional knowledge. Your rubric nuances, edge cases, and program context stay in the team rather than resetting every few months.
What teams say after
six months on the bench
We needed 40 RLHF engineers integrated into our pipeline within two weeks. AquSag delivered 45, and 95% passed our internal quality bar on the first calibration cycle. Six months later, the same team is still in place with zero involuntary turnover.
We tried three staffing vendors before AquSag. The difference was immediate. Their ML engineers understood our training pipeline on day one. We did not have to explain what a reward model was.
The 4 to 7 day deployment is real. We signed on a Thursday and had 12 RLHF engineers in our Jira board the following Wednesday. All 12 passed our internal calibration on the first cycle.
Staffing firms recruit after you sign.
AquSag activates.
Gig platforms find freelancers. Staffing firms begin recruiting when you engage. Platform vendors lock you into their infrastructure. Here is how a standing, on-payroll bench performs against every alternative — across the dimensions that actually matter.
| AquSag Standing team on payroll |
Gig Platforms (Toptal, Upwork) |
Staffing Firms (Traditional) |
Platform Vendors (Scale AI, Appen, Turing) |
|
|---|---|---|---|---|
| Time to deploy | 4 to 7 days |
2 to 6 weeks | 4 to 12 weeks | 2 to 4 weeks |
| Talent model | On-payroll, active engineers |
Freelancers recruited on demand | Candidates recruited after signing | Platform crowd or marketplace |
| Vetting depth | 4-stage, human-led, domain-specific |
Automated or self-reported | Resume + interview | Platform-specific screening |
| Annual churn | <5% |
30 to 40% | 15 to 25% | 10 to 20% |
| Works in your tools | Yes, always |
Sometimes | Yes | No — their platform |
| Your PMs, your direction | Yes, by default |
Varies | Yes | No — their PMs required |
| AI/ML domain depth | Deep — production program history |
Broad, shallow | Generalist | Varies by platform |
| Scale 5 to 50+ | Same week, from bench |
Weeks of recruiting | Months of recruiting | Limited by platform |
| Replacement policy | No-cost replacement within 14 days |
Start the process over | 4 to 8 week replacement cycle | Platform-dependent |
| Cost vs. US in-house | 40 to 60% lower |
30 to 50% lower | Similar or higher | 20 to 40% lower + platform fees |
Based on publicly available information. Comparison reflects typical engagement structures; individual experiences may vary.
Three operating modes.
One standing team.
Every team has a different management style. Choose the operating mode that fits — or start with one and evolve as the program matures.
Your team. Your process. Our engineers.
AquSag engineers join your team and report directly to your project managers. You assign tasks, run standups, review output, and manage the workflow end to end. We handle sourcing, bench management, payroll, and no-cost replacement if needed. Zero AquSag overhead in your daily workflow.
- Your tools, your sprints, your code reviews
- Direct Slack or Teams access to your engineers
- No AquSag presence in your daily standup
- No-cost replacement within 14 days if fit isn't right
How this works on day one
Engineers receive access to your tools on activation day. Within 48 hours they are in your Jira, your Slack, and your codebase. Your PM runs the kickoff. AquSag is not in the room.
You set direction. We keep the team executing.
AquSag provides a team lead who coordinates day-to-day operations, handles quality calibration, and manages execution alongside your PMs. You define standards and strategic direction. We make sure the team performs consistently — especially useful when you are scaling across multiple workstreams quickly.
- AquSag team lead handles operational coordination
- Your PMs retain strategic direction and output standards
- Quality reporting and calibration cycles included
- Works across 2+ concurrent workstreams without chaos
When teams choose this model
Usually when scaling from one workstream to three or more simultaneously. The AquSag lead handles the operational surface area so your PMs can focus on quality and direction rather than coordination.
Define the requirements. We handle everything else.
AquSag manages the full program: team composition, daily operations, quality assurance, and delivery reporting. You define requirements and review output. We handle everything in between — including calibration, replacement, and scale adjustments. Best for organizations building AI capability without yet having internal AI operations infrastructure.
- End-to-end program management by AquSag
- Regular delivery reports and quality dashboards
- Scale up or down with a single conversation
- IP, NDA, and SOC 2 controls maintained throughout
What you receive
Weekly delivery reports with quality metrics, throughput data, and any variance flags. Quarterly program reviews. Direct escalation path for any issues. Your work product is yours — all IP assignment happens at contract.
Ready to activate your AI engineering team?
4 to 7 business days from conversation to engineers in your workflow. No platform to adopt. No management structure to accept. No-cost replacement within 14 days if the fit isn't right.
Real programs.
Measurable outcomes.
How AquSag engineers have performed inside client-managed AI programs — frontier model development, enterprise platforms, and large-scale annotation at production scale.
Eliminating quality variance across a Fortune 100 LLM training program
A Fortune 100 AI platform needed to eliminate quality inconsistency and churn from their annotation vendor ecosystem without slowing project timelines. Previous vendors averaged 35% annual workforce turnover, creating constant quality resets.
RLHF and golden response generation for frontier model development
AI research teams needed ML engineers and domain experts who could generate training data for frontier models — not just follow annotation rubrics. The program required engineers who understood reward model calibration at the research level.
80+ engineers across 5 concurrent AI programs — activated in one week
An enterprise platform serving Fortune 500 clients needed coordinated multi-domain AI engineering teams deployed simultaneously across five programs. Previous attempts with staffing firms had resulted in 6-week delays and quality inconsistency across workstreams.
Questions before your first call
The questions AI labs, enterprise AI teams, and data platforms ask before they engage with us. If yours isn't here, ask it directly.
Talk to us directly →Your AI team is
already on the bench.
Tell us your program requirements, team size, and timeline. We confirm availability from the active bench within 24 hours and can have engineers in your workflow within the week.