Machine learning Engineer

Company: Mercari

Website: Visit Website

LinkedIn: Visit LinkedIn

Business Type: Startup

Company Type: Product

Business Model: C2C

Funding Stage: Series D+

Industry: Information Technology

Job Description

At Mercari, we leverage advanced machine learning to create a safer, more reliable, and engaging marketplace. As a Machine Learning Engineer, you will design, build, and operate scalable ML systems in cloud-native environments, contributing directly to trust, safety, and user experience.

Key Responsibilities

• We are looking for people who are interested in our services, mission, and values, and want to work where engineers can go bold, use the latest technology, make autonomous decisions, and take on challenges at a rapid pace.
• Collaborate with cross-functional teams and product stakeholders to gather requirements, design solutions, and implement features that improve user engagement
• Conduct data analysis and experimentation with large-scale data sets to identify patterns, trends, and insights that drive the refinement of trust and Safety algorithms
• Utilize machine learning frameworks and libraries to deploy scalable and efficient content moderation, fraud and financial security solutions.
• Monitor system performance and conduct A/B testing to evaluate the effectiveness of features.
• Continuously research and stay updated on advancements in AI/machine learning techniques and recommend innovative approaches to enhance the trust and safety capabilities.
  
  

Minimum Requirements

• Over 2-6 years of professional experience in end-to-end development of large-scale ML systems in production
• Strong experience demonstrating development and delivery of end-to-end machine learning solutions starting from experimentation to deploying models, including backend engineering and MLOps, in large scale production systems.
• Experience using common machine learning frameworks (e.g., TensorFlow, PyTorch) and libraries (e.g., scikit-learn, NumPy, pandas)
• Deep understanding of machine learning and software engineering fundamentals
• Basic knowledge and skills related to monitoring system, logging, and common operations in production environment
• Communication skills to carry out projects in collaboration with multiple teams and stakeholders
  
  

Preferred Skills

• Experience developing AI-based anomaly detection and content moderation systems is preferred
• Functional development and bug fixing skills necessary to improve system performance and reliability
• Experience with technology such as Docker and Kubernetes
• Experience with cloud platforms (AWS, GCP, Microsoft Azure, etc.)
• Microservice development and operation experience with Docker and Kubernetes
• Utilizing deep learning models/LLMs in production
• Experience in publications at top-tier peer-reviewed conferences or journals