Machine Learning Engineer

Position Overview

• Location Type: Remote
• Employment Type: Full-Time
• Salary: Not provided

Hang is building the future of loyalty for brands. Hang is the next generation brand loyalty & membership platform. By harnessing the power of personalization, gamification, and its integrations ecosystem, Hang provides brands with a radically new type of loyalty experience for their customers. Today, they work with a variety of major brands (such as Ulta Beauty, Budweiser, Flipkart, and more), as well as multiple well-known, up-and-coming restaurant chains (Boba Guys, Roam Artisan Burger, and Williamsburg Pizza, among several others). Hang draws from years of deep expertise in loyalty, game design, and finance with employees from leading companies like Google, Amazon, Apple, Meta, LinkedIn, Coinbase, Square, and Goldman Sachs. Hang raised a $16 million Series A led by Paradigm last summer, with participation from Tiger Global, Howard Schultz, Kevin Durant, Mr. Beast, and the founders of Warby Parker, Allbirds, and Bombas, among others.

Requirements

• Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, or a related field. A Ph.D. is a plus.
• 5+ years of experience in machine learning and data related roles.
• Proven experience as a Machine Learning Engineer or in a similar role.
• Strong programming skills in Python.
• Familiarity with ML frameworks (like TensorFlow or PyTorch).
• Experience with data processing and data analytics.
• Experience with vector databases, demonstrating proficiency in managing and querying high-dimensional data.
• Familiarity with popular vector databases like Redis, Milvus, Pinecone, Weaviate, Chroma or Faiss is required.
• Knowledge of machine learning techniques and algorithms, including fine tuning processes and methodology.

Responsibilities

• Model Development: Design, build, and deploy machine learning models to improve various aspects of our platform, including customer personalization, predictive analytics, and automated decision-making.
• Data Analysis: Analyze large datasets to identify trends and patterns, and use this information to inform model development and business strategies.
• Algorithm Optimization: Continuously test and refine algorithms to improve accuracy and efficiency.
• Collaborative Development: Work closely with software engineers, data scientists, and product managers to integrate ML models into our platform and ensure seamless deployment.
• Research and Innovation: Stay up-to-date with the latest developments in machine learning and AI, and explore new techniques and technologies that could benefit Hang.
• Technical Leadership: Provide insights and guidance on best practices in machine learning, and contribute to the strategic direction of our technology.

Application Instructions

• Not provided