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$18.00/hrExpert1+

Key Skills

Software

Internal/Proprietary Tooling

Top Subject Matter

No subject matter listed

Top Data Types

TextText
ImageImage
VideoVideo

Top Task Types

Bounding BoxBounding Box
ClassificationClassification
Text GenerationText Generation
Object DetectionObject Detection
RLHFRLHF

Company Overview

Steelhead Digital is an AI‑driven data and workforce platform focused on scalable microtask operations, model training, and quality assurance. We design systems that connect skilled human annotators with structured AI workflows, enabling efficient data labeling, evaluation, and productivity tracking across diverse projects. Our mission is to bridge human insight and machine learning through transparent, high‑quality task management. Steelhead Digital provides tools for workforce coordination, time tracking, automated audits, and skill‑based task assignment — ensuring accuracy, accountability, and measurable performance at every level. Built for reliability and innovation, Steelhead Digital supports clients seeking precision data pipelines and ethical AI development. We combine intelligent automation with human expertise to deliver consistent, verifiable results that strengthen model performance and accelerate AI advancement.

ExpertEnglish

Security

Security Overview

Steelhead Digital uses a security‑first framework to protect client data, worker activity, and all project‑related information. All data is transmitted over encrypted channels (HTTPS/TLS), stored in access‑controlled environments, and restricted through role‑based permissions. Worker access is limited to assigned tasks only, and all administrative actions are logged for auditability and compliance. We maintain strict data‑handling protocols, enforce confidentiality requirements, and monitor submissions for integrity, accuracy, and adherence to project guidelines. Internal systems include automated quality checks, audit logs, and anomaly detection to identify unauthorized behavior or low‑quality work. We prioritize privacy by minimizing data exposure, applying least‑privilege access, and ensuring that all workers operate within secure, isolated task environments. Steelhead Digital is committed to maintaining a secure, transparent, and reliable environment for AI model training, evaluation, and human‑in‑the‑loop operations.

Labeling Experience

AI Model Trainer and QM

Internal Proprietary ToolingComputer Code ProgrammingEvaluation RatingRLHF
I have four years of experience as a Quality Manager at Outlier, where I oversaw large‑scale AI training and evaluation workflows across NLP, computer vision, and code‑generation tasks. My work included reviewing and scoring model outputs, evaluating computer programming code for correctness and efficiency, and contributing to fine‑tuning pipelines through structured feedback and high‑integrity annotations. I also performed detailed bounding‑box and image‑labeling tasks, RLHF evaluations, and multi‑stage quality checks to ensure consistent, guideline‑aligned results. In my QM role, I managed evaluator performance, handled escalations, audited submissions for accuracy, and maintained project‑level quality standards. I trained new annotators, refined task instructions, and collaborated with project leads to improve clarity, reduce ambiguity, and strengthen data reliability. This combination of technical evaluation experience and quality‑management oversight gives me a strong foundation in AI model training, human‑in‑the‑loop systems, and scalable microtask operations.

I have four years of experience as a Quality Manager at Outlier, where I oversaw large‑scale AI training and evaluation workflows across NLP, computer vision, and code‑generation tasks. My work included reviewing and scoring model outputs, evaluating computer programming code for correctness and efficiency, and contributing to fine‑tuning pipelines through structured feedback and high‑integrity annotations. I also performed detailed bounding‑box and image‑labeling tasks, RLHF evaluations, and multi‑stage quality checks to ensure consistent, guideline‑aligned results. In my QM role, I managed evaluator performance, handled escalations, audited submissions for accuracy, and maintained project‑level quality standards. I trained new annotators, refined task instructions, and collaborated with project leads to improve clarity, reduce ambiguity, and strengthen data reliability. This combination of technical evaluation experience and quality‑management oversight gives me a strong foundation in AI model training, human‑in‑the‑loop systems, and scalable microtask operations.

2022 - Present