Machine Learning Engineer

Summary

Join Flip.shop, a rapidly growing social commerce company, as a Machine Learning Engineer. You will design, develop, and optimize machine learning models for personalized recommendation systems. This role requires expertise in machine learning algorithms, data analysis, and programming languages like Python, TensorFlow, and PyTorch. You will collaborate with cross-functional teams and contribute to the company's AI-driven platform. Flip.shop offers a dynamic startup environment and opportunities for innovation. The compensation package includes a base salary, equity, bonuses, long-term incentives, PTO, and other benefits.

Requirements

• In depth experience working on AI applications, ideally working on social networking or e-commerce platforms
• Strong proficiency in machine learning algorithms, data analysis, and programming languages such as Python, TensorFlow, and PyTorch
• Experience developing recommendation systems for feeds and/or ads ranking, with a focus on user engagement and monetization
• Ability to analyze and interpret complex data, and use insights to drive AI innovation
• Excellent collaboration skills, with the ability to work effectively in a fast-paced, team-oriented environment
• Strong verbal and written communication skills, with the ability to convey complex AI concepts to non-technical stakeholders
• A genuine passion for leveraging AI to enhance user experiences and drive business growth in a dynamic startup environment

Responsibilities

• Design, develop, and optimize machine learning algorithms for our feed and shopping recommendation systems
• Analyze large datasets to extract meaningful insights and improve model performance
• Deploy and monitor machine learning models in a production environment, ensuring their scalability and reliability
• Work closely with cross-functional teams, including product, engineering, and marketing, to integrate AI solutions seamlessly into our platform
• Stay up-to-date with the latest advancements in AI and machine learning, applying new techniques to enhance our recommendation systems
• Address complex challenges related to recommendation algorithms, user engagement, and monetization strategies

Benefits

• Equity
• Bonuses
• Long term incentives
• A PTO policy
• Other progressive benefits