Senior Software Engineer - Machine Learning

Summary

Join our team building the next generation product discovery platform! We're creating a platform to enable truly personalized product discovery for e-commerce companies globally. You will specialize in designing, implementing, and optimizing machine learning models and algorithms to enhance search capabilities and user experiences. This role involves analyzing large datasets, developing predictive models, and leveraging large language models and generative AI. You'll collaborate with cross-functional teams to improve search performance and iterate on AI/ML solutions. We're backed by strong investors and poised to revolutionize e-commerce discovery.

Requirements

• Possess a strong understanding of networking protocols, communication patterns, & data serialization formats in distributed systems
• Have proven experience in designing & developing distributed systems, with a solid understanding of distributed computing concepts & principles
• Have experience in one or more programming languages such as Java, Python, or C++
• Have experience working with Postgres, DynamoDB, Temporal, & Thrift
• Demonstrate an interest in staying up-to-date with industry trends, emerging technologies, & advancements in distributed systems
• Have experience working with platforms such as Amazon AWS & Docker Kubernetes
• Have senior-level (6+ years) experience in Computer Science, Software Engineering, or a related field

Responsibilities

• Develop machine learning models and algorithms to solve specific business problems
• Collect, preprocess, and clean data for analysis and modeling
• Explore and visualize data to gain insights and inform modeling decisions
• Select appropriate machine learning techniques and algorithms for a given task
• Train, fine-tune, and validate machine learning models using relevant frameworks and tools
• Optimize models for performance, scalability, and efficiency
• Collaborate with cross-functional teams (e.g., data scientists, software engineers, product managers) to integrate machine learning solutions into products or systems
• Deploy machine learning models into production environments, ensuring scalability, reliability, and maintainability
• Monitor and evaluate model performance in production, and implement necessary updates or improvements
• Stay updated on the latest advancements in machine learning techniques, tools, and technologies, and incorporate them into practice when appropriate