Senior/Staff Machine Learning Engineer for AI Architecture & Code Review (possible long term)

## Description We are building a next-generation AI platform involving sophisticated machine learning models, production AI infrastructure, and proprietary algorithms. This is **not** a chatbot or simple LLM integration project. We are looking for a **Senior, Staff, or Principal Machine Learning Engineer** to perform an independent technical review of our codebase, architecture, and modeling approach. The initial engagement is a **paid one-day review**. If the fit is excellent, this can evolve into an ongoing fractional technical advisor or technical leadership role. ### What You'll Review You will independently evaluate: * Overall system architecture * Machine learning model design * Training methodology * Validation strategy * Data quality and feature engineering * Risk of data leakage * Generalization and overfitting * Evaluation metrics and benchmark design * Production readiness * MLOps architecture * Scalability * Inference performance and cost optimization * Code quality and maintainability * Security considerations * Technical debt * Future extensibility We're looking for someone who can identify both hidden risks and opportunities to substantially improve the platform. ### Deliverables At the end of the engagement, we expect: * A prioritized list of the 10 highest-impact improvements * Critical architectural concerns * Assessment of model quality and engineering approach * Evaluation of production readiness * Recommendations for scaling * Technical roadmap for the next development phase * Written report and 60–90 minute review meeting ### Required Experience Please apply only if you have significant experience with several of the following: * Production machine learning systems * PyTorch * TensorFlow (optional) * Python * Distributed training * Feature engineering * Model evaluation * Time-series, prediction, classification, or ranking models * Production inference * MLOps * Kubernetes * Docker * AWS/GCP/Azure * CI/CD * Vector databases (if applicable) * LLMs and agentic systems (helpful but not sufficient by themselves) Experience designing systems from scratch is strongly preferred. ### Ideal Background We are especially interested in candidates who have worked at organizations such as: * OpenAI * Anthropic * Google DeepMind * Google * Meta * Microsoft * Amazon * NVIDIA * Apple * Tesla * Scale AI * Databricks * Snowflake * Leading AI startups Equivalent experience at smaller companies is absolutely welcome if you've built sophisticated production ML systems. ### Required Application Questions Please answer each question thoughtfully. 1. Describe the most technically sophisticated machine learning system you personally designed. 2. Describe a model that worked well during development but failed or degraded in production. What happened? 3. How do you detect data leakage, overfitting, and unreliable validation? 4. During the first two hours of reviewing an unfamiliar ML repository, what would you examine first? 5. Please provide links to GitHub repositories, technical papers, architecture documents, conference talks, blog posts, or other examples of your work. 6. Explain the difference between machine learning research, ML engineering, software engineering, and MLOps. 7. Are you willing to complete a paid two-hour technical review before the full engagement? ### To Confirm You Read This Posting Begin your proposal with the words: **MODEL REVIEW** Then answer this question: **Describe one situation where a simpler machine learning model is a better choice than a more sophisticated one, and explain why.** ### Budget This initial engagement is fixed-price for approximately one full day of work. Exceptional performance may lead to an ongoing advisory role, recurring architecture reviews, or a larger technical leadership position.