Senior Machine Learning Engineer

March 26

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Treasure Data

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Treasure Data is a leading customer data platform (CDP) that empowers some of the world's largest and most innovative companies to unify, manage, and activate customer data. Utilizing a blend of batch and real-time data, Treasure Data enhances personalized customer journeys by integrating AI-driven insights which help boost conversions and optimize costs. The platform is extensively used across various industries including automotive, retail, and financial services to improve customer acquisition, retention, and lifetime value by delivering impactful and personalized experiences. With AI capabilities and scalable solutions, Treasure Data provides deep insights into customer behavior and preferences, making it a favorite choice for companies like Yum! Brands.

Data Acquisition • Data processing • Advanced Analytics • Customer Data • Customer Data Platform

501 - 1000 employees

Founded 2011

🤖 Artificial Intelligence

☁️ SaaS

🏢 Enterprise

📋 Description

• As a Senior Machine Learning Engineer at Treasure Data, you will be instrumental in developing cutting-edge AI and machine learning solutions that drive personalized customer experiences. • You will collaborate closely with cross-functional teams to innovate and implement scalable ML models and algorithms, leveraging our robust data platform to extract meaningful insights and deliver impactful business outcomes. • Design, develop, and deploy scalable machine learning models and algorithms that enhance customer data insights and personalization capabilities. • Collaborate with data scientists, product managers, and engineers to understand business requirements and translate them into technical solutions. • Optimize and tune machine learning models for performance and scalability in a high-volume, real-time data environment. • Implement best practices in machine learning engineering, including data preprocessing, feature engineering, model evaluation, and deployment. • Work with large-scale datasets using distributed computing frameworks to extract, transform, and load data for machine learning workflows. • Research and prototype new machine learning algorithms and techniques to improve model accuracy and efficiency. • Stay current with advancements in machine learning, AI, and related technologies to drive innovation and competitive advantage.

🎯 Requirements

• Bachelor's or advanced degree in Computer Science, Engineering, Mathematics, or a related field. • Proven experience (6-8 years) in designing, developing, and deploying machine learning models in production environments. • Strong programming skills in Python and proficiency with machine learning libraries such as TensorFlow, PyTorch, or scikit-learn. • Experience with distributed computing frameworks (e.g., Spark) and big data technologies (e.g., Hadoop, Hive, Kafka). • Solid understanding of machine learning concepts including supervised and unsupervised learning, deep learning, natural language processing, etc. • Ability to work independently and collaboratively in a dynamic, fast-paced environment.