Please submit your CV in English and indicate your level of English proficiency. Mindrift connects specialists with project-based AI opportunities for leading tech companies, focused on testing, evaluating, and improving AI systems. Participation is project-based, not permanent employment. What this opportunity involves While each project involves unique tasks, contributors may:  - Design original computational physics problems that simulate real physics research workflows; - Create problems requiring Python programming to solve (using Numpy, SciPy, Sympy); - Ensure problems are computationally intensive and cannot be solved manually within reasonable timeframes (days/weeks); - Develop problems requiring non-trivial reasoning chains in mechanics, electromagnetism, thermodynamics, and quantum mechanics; - Base problems on real research challenges or practical applications from physics practice; - Verify solutions using Python with standard physics simulation libraries; - Document problem statements clearly and provide verified correct answers. What we look for This opportunity is a good fit for physicians with an experience in python open to part-time, non-permanent projects. Ideally, contributors will have:   - Degree in Physics (Theoretical, Experimental, or Computational) or related fields; - Python proficiency for numerical validation. MATLAB, R, C, SQL, Numpy, Pandas, SciPy, domain-specific libraries, Stata or knowledge of any programming language can be equivalent; - 2+ years of professional experience: applied, research, or teaching experience is applicable; - Experience with numerical simulation methods; - Ability to design problems that mirror real physics research workflows; - Creative thinking in problem design across diverse physics areas; - Familiarity with physics modeling and approximation techniques; - Strong written English (C1+). How it works Apply → Pass qualification(s) → Join a project → Complete tasks → Get paid Project time expectations For this project, tasks are estimated to require around 10–20 hours per week during active phases, based on project requirements. This is an estimate, not a guaranteed workload, and applies only while the project is active. Compensation On this project, contributors can earn up to $76 per hour equivalent, depending on their level and pace of contribution. Compensation varies across projects depending on scope, complexity, and required expertise. Please note that other projects on the platform may offer different earning levels based on their requirements.

• Improve our existing machine learning systems using your core coding skills and ML knowledge • Identify new opportunities to apply machine learning to different parts of the Quora product • Work with other machine learning engineers to implement algorithms and systems efficiently • Take end-to-end ownership of machine learning systems - data pipelines, candidate extraction, feature engineering, model training, as well as integration into our production systems

• Construir la infraestructura de Machine Learning y MLops que permite a Data Scientists desarrollar, desplegar y mantener modelos de Machine Learning en producción siguiendo buenas prácticas industriales. • Responsable de crear herramientas, SDKs y pipelines automatizados que garantizan reproducibilidad, observabilidad y gobernanza de modelos, con enfoque en self-service y autonomía de equipos de ML. • Incluye productización y mejora constante de modelos complejos (bayesianos, ensembles) con estándares de calidad enterprise. • Construir CLI tools para deployment automatizado (ml-deploy, ml-rollback). • Implementar workflows automatizados MLflow (promotion dev → staging → prod). • Crear templates repositories con best practices (model template, API template). • Establecer buenas prácticas para productización de modelos bayesianos (sampling, inference, monitoring). • Documentar APIs y SDKs (Sphinx/MkDocs, tutoriales, examples). • Training sessions Data Scientists (certificación uso plataforma). • Desarrollar herramientas self-service para Data Scientists (80%+ autonomy target). • Implementar automated testing suites (model validation, performance checks). • Optimizar performance modelos bayesianos en producción (MCMC efficiency, inference speed). • Optimizar MLflow performance (caching, query optimization). • Construir monitoring dashboards para model health (accuracy, latency, convergence diagnostics). • Governance automation (approval gates, compliance checks). • Incident response & troubleshooting support. • Mantener y evolucionar SDKs/tooling basado en feedback DS. • Mejorar arquitectura serving modelos complejos (bayesianos, ensembles). • Documentación continua (runbooks, troubleshooting guides). • Office hours semanales Data Scientists (support + knowledge sharing).

• Build and optimize data pipelines and backend services to process device and behavioral data in real time. • Develop and deploy ML models for fraud detection, ensuring they run reliably and efficiently in production. • Turn raw data into production-ready features that feed our fraud detection systems. • Collaborate with platform and backend engineers to integrate models seamlessly. • Maintain high standards of security, privacy, and compliance. • Champion best practices in testing, documentation, and observability.