Senior AI Engineer
The responsibilities include building agent-driven enrollment and parent communication pipelines that scale significantly without proportional headcount growth; creating and managing parallel simulations of students testing curriculum to identify gaps and generate improvements; developing automated culture and community agents for engagement, onboarding, and retention at machine scale; constructing real-time operational dashboards to provide leadership with visibility into various business aspects such as enrollment, academic progress, parent satisfaction, and campus operations; designing AI-first workflows for guides, advisors, and operational staff to reduce administrative burdens and refocus on students; building systems called Brainlifts to capture and compound institutional knowledge over time; and integrating these capabilities into Alpha's broader AI ecosystem including EPHOR, Alpha GPTs, and Fleet/Swarm infrastructure.
AI QA Analyst
Review and annotate complex conversation traces to rate response quality based on metrics such as helpfulness, honesty, and harmlessness (HHH). Build and maintain high-quality "Golden Datasets" and benchmarks to stress-test the model across various domains and edge cases. Conduct pre-deployment testing and A/B model comparisons to identify performance regressions or improvements. Categorize model failures (hallucinations, logic errors, tone drift) to provide actionable feedback to the Engineering and Research teams. Help define and refine the rubric for "what a good response looks like" as the product evolves.
Expansion Account Executive
Debug and fix issues in the platform and ship pull requests with fixes. Build internal tools and copilots powered by generative AI to enhance the team. Rapidly prototype proof-of-concepts for customer use cases. Collaborate across Engineering, Product, and Solutions teams to unblock customers and advance AI adoption.
Full Stack Engineer
Build and maintain features for the web-based property management platform using TypeScript, React, Node.js, PostgreSQL, and AWS. Contribute to a monorepo architecture, working within two-week sprint cycles to deliver high-quality code. Implement integrations including DocuSign, Plaid, Stripe, and ownership group payout systems. Optimize platform performance and user experience by replacing legacy systems. Build and integrate AI agents using Claude and other AI APIs to automate organizational processes, developing API integrations and custom agents. Collaborate with the CEO on prioritizing automation opportunities. Take ownership of tasks, independently research and implement solutions to challenges, proactively identify and implement improvements, and contribute ideas to platform architecture and development priorities.
AI Evaluation Engineer
Design and implement evaluation pipelines to measure the performance and reliability of AI models, develop automated testing frameworks to assess model outputs at scale, analyze model performance using both traditional statistical metrics and AI-specific evaluation methods, evaluate AI systems built on modern architectures such as LLM-based applications and Retrieval-Augmented Generation (RAG), identify potential issues related to accuracy, hallucinations, bias, safety, and model drift, conduct adversarial testing to uncover vulnerabilities and ensure safe model behavior, collaborate with engineering and AI teams to improve prompt design, model outputs, and system performance, monitor model performance in production, and help define best practices for AI evaluation and observability.
Automotive Engineering & Python Expert - Freelance AI Trainer
Contributors may design graduate- and industry-level automotive engineering problems grounded in real practice; evaluate AI-generated solutions for correctness, assumptions, and engineering logic; validate analytical or numerical results using Python (NumPy, SciPy, Pandas); improve AI reasoning to align with first principles and accepted engineering standards; and apply structured scoring criteria to assess multi-step problem solving.
Machine Learning Developer (Freelance)
Design original computational STEM problems that simulate real scientific workflows, create problems requiring Python programming to solve, ensure problems are computationally intensive and cannot be solved manually within reasonable timeframes (days/weeks), develop problems requiring non-trivial reasoning chains and creative problem-solving approaches, verify solutions using Python with standard libraries such as numpy, pandas, scipy, sklearn, and document problem statements clearly while providing verified correct answers.
Data Scientist (Python & SQL) - Freelance AI Trainer
As a Data Science AI Trainer, you will design original computational data science problems that simulate real-world analytical workflows across various industries including telecom, finance, government, e-commerce, and healthcare. You will create Python programming problems using libraries such as pandas, numpy, scipy, sklearn, statsmodels, matplotlib, and seaborn, ensuring these problems are computationally intensive and cannot be solved manually within reasonable timeframes. You will develop problems requiring complex reasoning in data processing, statistical analysis, feature engineering, predictive modeling, and insight extraction, while avoiding stochastic elements or ensuring fixed random seeds for reproducibility. The problems will be based on real business challenges such as customer analytics, risk assessment, fraud detection, forecasting, optimization, and operational efficiency, covering end-to-end data science pipeline workflows including data ingestion, cleaning, exploratory data analysis, modeling, validation, and deployment considerations. Your tasks will also involve incorporating big data processing scenarios requiring scalable computational approaches, verifying solutions using Python and standard data science libraries, and documenting problem statements clearly with realistic business contexts along with verified correct answers.
Freelance AI Evaluation Engineer (Python/Full-Stack)
Create challenging coding test cases to push AI coding systems to their limits by reviewing and refining realistic coding tasks based on provided production codebases with realistic scope, requirements, and information sources. Write comprehensive functional tests that validate actual end-to-end behavior and edge-cases. Craft challenges that are fair but hard, where the AI has all the context it needs, requiring complex reasoning with information scattered across files and external sources. Analyze AI failures to understand the model's struggles and strengths. Iterate based on feedback from expert QA reviewers who score work on seven quality criteria.
Mechanical Engineer & Python Expert - Freelance AI Trainer
Participants design graduate- and industry-level mechanical engineering problems grounded in real practice, evaluate AI-generated solutions for correctness, assumptions, and engineering logic, validate analytical or numerical results using Python (NumPy, SciPy, Pandas), improve AI reasoning to align with first principles and accepted engineering standards, and apply structured scoring criteria to assess multi-step problem solving.
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