Founding Machine Learning Engineer, Recommendations, GenAI

This is a fully remote position, open to applicants in United States.

📋 Description

• Design, develop, and enhance machine learning systems for recommendations, ranking, personalization, retrieval, and generative AI workflows;

• Transform product objectives into tangible machine learning challenges, evaluation strategies, experiments, and deployed features;

• Analyze behavioral, transactional, contextual, and unstructured data to uncover signals and enhance model performance;

• Create offline evaluation frameworks and conduct online experiments to assess relevance, quality, latency, cost, and business impact;

• Enhance generative AI agent performance through improved retrieval, context management, prompting, tool utilization, orchestration, and evaluation;

• Investigate failure modes, perform error analysis, and make pragmatic trade-offs between quality, reliability, speed, and complexity;

• Collaborate closely with platform and backend engineers to deploy, monitor, and refine models in a production environment;

• Help shape the company's approach to machine learning: metrics, experimentation discipline, technical standards, and long-term strategy;

• Utilize real-time behavioral and transactional signals to enhance recommendations, personalization, and intelligent product functionalities;

• Contribute to predictive and insight-driven machine learning applications including segmentation, churn prediction, recommendation evaluation, and opportunity ranking;

• Write clean, testable Python code and contribute reusable machine learning components and shared libraries utilized across the platform.

⛳️ Requirements

• Solid foundation in machine learning, statistics, computer science, or a related quantitative field;

• Proven experience in building and deploying machine learning systems or intelligent product features in production or near-production settings;

• Proficient in Python, with the ability to work across data, modeling, evaluation, and collaborative production processes;

• Strong understanding of experimentation, model evaluation, feature engineering, data quality, and error analysis;

• Excellent communication skills and the ability to navigate complex and ambiguous product challenges;

• High ownership, self-motivation, and a strong inclination towards proactive action;

• Over 5 years of experience in building and deploying machine learning systems or intelligent product features in production;

• Comprehensive understanding of model evaluation, cross-validation, feature engineering, and data quality issues in real-world scenarios;

• Experience with large-scale behavioral, transactional, or contextual data;

• Strong software engineering practices, including writing clean, testable, and maintainable Python code.

• Experience with recommendation systems, ranking, search, personalization, or optimization of marketplace/feed;

• Familiarity with large language model applications, retrieval-augmented generation, generative AI agents, prompt iteration, or evaluation of generative AI systems;

• Experience conducting A/B tests or online experiments;

• Proven ability to work closely with product teams, translating user challenges into machine learning solutions;

• Knowledge of real-time machine learning, streaming features, low-latency inference, or online learning;

• Experience with causal inference, uplift modeling, multi-armed bandits, or other decision-optimization techniques;

• Familiarity with cloud-based machine learning infrastructure, containerized deployment, and MLOps workflows;

• Background in iGaming, fintech, e-commerce, or another domain with extensive transactional and behavioral data;

• Experience with predictive analytics applications such as segmentation, churn prevention, lifetime value modeling, or opportunity prioritization.

🏝️ Benefits

• Competitive salary and performance-based bonuses;

• Comprehensive health benefits including medical, dental, and vision coverage;

• Flexible working hours and remote work options;

• Opportunities for professional development and continuous learning;

• Collaborative and innovative work environment;

• Generous vacation policy and paid time off.