Machine Learning & Coding Expert

Purpose

We are seeking a highly experienced Coding / Machine Learning professional to serve as a consultant on AI training data projects for leading AI model builders and enterprises. Your focus will be to define success criteria, review outputs, and provide targeted guidance to improve quality and speed — directly contributing to the successful delivery of domain-specific annotated datasets that meet the highest technical standards. You will be engaged on specific projects with clearly defined deliverables, milestones, and end dates.

Components

Technical Standard Setting, Quality Control, and Process Improvement

• Define domain-specific quality success metrics.
• Develop project-specific SOPs, QA rubrics, and reference materials for the specific purpose of meeting client technical standards.
• Review project outputs (code annotations, model configurations) against technical standards, flagging and correcting defects before client delivery.
• Perform structured QA passes on daily/weekly deliverables; flag, track, and resolve defects quickly to hit delivery deadlines.
• Return work to contractors with precise remediation notes.
• Provide advisory input on tools, frameworks, workflows, and processes to meet quality benchmarks.
• Handle spec changes and edge-case scenarios e.g., evaluation of new model architectures, data formats, or API changes drafting acceptance criteria or technical workarounds.
• Curate example libraries of “gold standard” scripts, models, and dataset annotations for calibration and comparability to reference samples.

Talent Vetting & Output Improvement

• Participate in vetting and assessing technical contractor talent for specific projects, including code review tests and ML task evaluations.
• Review sample work from contractors and provide precise, actionable written feedback to improve outputs.
• Create targeted training or calibration resources — e.g., best practices for clean, maintainable code, hyperparameter tuning guidelines, dataset preparation standards.

Project Delivery Support

• Advise on technical scoping and requirements during project setup, including choice of programming languages, ML frameworks, and data preprocessing pipelines.
• Provide expert guidance for edge cases, technical exceptions, and specification changes during the project lifecycle.
• Contribute to post-project reviews to capture lessons learned and improve future standards.
• Identify and summarize client model observations and insights (e.g., model accuracy drift, overfitting patterns, data leakage issues).
• Build dashboards or trackers with defect categories and recurrence to surface production insights that improve project outcomes.
• Conduct post-mortems, analyze defect trends, and propose process tweaks or training refreshers.

Target Profile

• Deep technical expertise and 5+ years professional experience in software engineering, machine learning, or data science, with demonstrable industry impact.
• Mastery of one or more programming languages (e.g., Python, C++, Java) and experience with leading ML frameworks (e.g., PyTorch, TensorFlow, scikit-learn).
• Proven ability to set, enforce, and maintain high technical standards in software development and ML workflows.
• Strong communication skills for delivering clear technical guidance to both engineers and non-technical stakeholders.
• Experience producing technical documentation, quality rubrics, or training resources.
• Ability to work within fixed project timelines and scope.
• Strong attention to detail, documentation discipline, and commitment to accuracy and consistency.
• Fluency in spoken and written English, with clear and concise writing skills.

Example Data Annotation Potential Scope

Field of Study

• Machine Learning: Model training/explanation, bias detection, evaluation metric analysis
• Deep Learning: Neural net architecture design, backpropagation walkthroughs
• Natural Language Processing (NLP): Text classification, summarization, sentiment analysis
• Computer Vision: Image labeling, object detection, image captioning
• MLOps / Deployment: Model lifecycle support, pipeline design, monitoring/rollback flows
• Statistical Modeling: Hypothesis testing, regression diagnostics, p-value calculation
• Data Engineering: Data pipeline logic, cleaning steps, schema validation
• Feature Engineering: Variable selection, encoding/normalization strategies
• Time Series Analysis: Forecast modeling, anomaly detection
• Recommender Systems: User/item embedding design, collaborative filtering support
• Explainable AI (XAI): SHAP/LIME interpretation, fairness flagging
• Data Storytelling: Generating readable summaries from charts or model outputs

Tools

• TensorFlow / PyTorch / JAX / HuggingFace

Key skills/competency

• Machine Learning
• AI Training Data
• Quality Control
• Technical Standards
• Python Programming
• Model Evaluation
• Data Annotation
• SOP Development
• Code Review
• ML Frameworks