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Discovery call
Share your model, data sources, and accuracy goals. Fit confirmed in 24 hours.
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Available now · Onboard in 7 days
Hire an AI Data Trainer to turn raw data into measurable model accuracy.
Onboard a senior AI Data Trainer to design dataset strategy, run RLHF, build evaluation harnesses, and continuously improve LLM and ML model quality with disciplined human feedback loops.
DTSKRN+9
4.9/5
Trusted by 120+ teams shipping AI products
- 5+ years curating AI / LLM datasets
- RLHF, DPO & human feedback loops
- Eval-driven model improvement
- Onboard in 7 days
Why hire an AI Data Trainer
Outcomes you can ship in the first 90 days
Higher model accuracy
Cleaner data, sharper guidelines, and structured feedback loops directly lift model quality scores.
Faster fine-tuning cycles
Repeatable dataset pipelines and evaluation harnesses cut iteration time from weeks to days.
Reduced bias & risk
Bias audits and edge-case coverage protect users and meet Responsible AI standards.
Trusted ground truth
Versioned datasets with quality SLAs become a durable AI asset, not a one-off batch.
Core responsibilities
What your AI Data Trainer will own
Datasets, annotation, RLHF, evaluation, drift, and bias governance — the full data lifecycle behind model quality.
Dataset Strategy & Curation
- Define data needs for pretraining, fine-tuning, RAG, and evaluation
- Source, clean, and version datasets with clear provenance
- Handle multilingual, multimodal, and domain-specific corpora
Annotation & Labeling
- Author annotation guidelines and decision trees
- Train and manage labeling teams across vendors and platforms
- Run inter-annotator agreement and quality audits
RLHF & Human Feedback
- Design preference data collection for RLHF and DPO
- Build review interfaces, rubrics, and rater calibration
- Track rater quality, drift, and incentive alignment
Evaluation & Benchmarks
- Create eval harnesses, golden sets, and regression suites
- Benchmark models on accuracy, safety, and task-specific KPIs
- Run blind comparisons and structured human evaluations
Drift & Continuous Learning
- Monitor data drift, concept drift, and model degradation
- Schedule recurring relabeling and dataset refreshes
- Feed production signals back into training data
Bias, Safety & Compliance
- Audit datasets for bias and representation gaps
- Apply PII redaction, consent, and licensing controls
- Document data sheets and model cards for governance
Skills & expertise
Statistical rigor meets ML and operations
Trainers who can author guidelines, manage rater operations, and quantify model quality through structured evaluations and bias audits.
- Data labeling strategy
- Annotation guideline design
- RLHF & DPO data collection
- LLM & multimodal datasets
- Evaluation frameworks
- Data quality auditing
- Bias & fairness analysis
- Statistics & sampling
- Python & SQL
- Active learning
- Drift detection
- Responsible AI documentation
AI tools & stack we operate in
Hands-on across annotation platforms, evaluation tooling, ML platforms, and data infrastructure.
Annotation Platforms
- Label Studio
- Scale AI
- SuperAnnotate
- Prodigy
- V7
- Labelbox
Eval & Experimentation
- LangSmith
- Langfuse
- Promptfoo
- DeepEval
- Ragas
- Custom harnesses
ML Platforms
- Hugging Face
- Weights & Biases
- MLflow
- Vertex AI
- Azure ML
- AWS SageMaker
Data & Storage
- Snowflake
- Databricks
- BigQuery
- S3
- DVC
- LakeFS
LLM Providers
- OpenAI
- Anthropic
- Google Gemini
- Mistral
- Llama
- Hugging Face Hub
Workflow & Reporting
- Airflow
- Prefect
- Notion
- Looker
- Metabase
- Jupyter
Engagement models
Hire on terms that match your stage
Dedicated Full-time
Senior AI Data Trainer embedded full-time to own dataset strategy and ongoing evaluation.
- 40 hrs / week
- Full ownership
- Long-term roadmap
Part-time / Fractional
20 hrs/week of strategic data and evaluation coverage for ongoing model improvement.
- Eval focus
- Vendor oversight
- Flexible cadence
Project / Outcome-based
Fixed-scope engagement to deliver a labeled dataset, RLHF batch, or eval suite.
- Defined deliverables
- Milestone billing
- Predictable timeline
How to hire
From first call to first sprint in under a week
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Shortlist in 48 hours
Receive 2–3 pre-vetted AI Data Trainers with relevant experience.
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Interview & evaluate
Run interviews and an optional paid trial on a sample annotation or eval task.
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Onboard in 7 days
Sign, kick off, and start delivery — backed by a 14-day risk-free trial.
FAQs
Hiring an AI Data Trainer — questions we hear most
What does an AI Data Trainer do?+
An AI Data Trainer designs the data and feedback systems that make models better — sourcing data, defining annotation guidelines, managing labeling teams, building evaluation harnesses, and running RLHF or DPO pipelines.
Do you handle labeling at scale?+
Yes. We work across in-house teams, vendor platforms (Scale, SuperAnnotate, Labelbox), and crowdsourced raters — designing guidelines, QA loops, and dashboards to ship quality at volume.
Can you support fine-tuning and RLHF?+
Absolutely. We build the preference data, rater rubrics, calibration processes, and review tooling needed for high-quality RLHF, DPO, and supervised fine-tuning.
How do you measure model improvement?+
We design golden sets, regression suites, and structured human evaluations tailored to your use case. Every iteration is benchmarked on accuracy, safety, latency, and business KPIs.
How do you handle bias and compliance?+
We audit datasets for bias, document representation gaps, enforce PII redaction and consent, and produce data sheets and model cards aligned with Responsible AI and regulatory standards.
How quickly can the trainer start?+
Most engagements start within 7 days of contract signing. Urgent needs can be accelerated to 72 hours.
Ready to hire your AI Data Trainer?
Share your model goals and we'll send a shortlist of pre-vetted AI Data Trainers within 48 hours — backed by a 14-day risk-free trial.
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