Sr. Machine Learning Engineer - Recommendation

We're looking for a Senior Machine Learning Engineer to build and improve Mercari's recommendation systems, helping users discover items they'll love. You'll own the full ML lifecycle, from experimentation and modeling to deploying and monitoring large-scale production systems on the cloud and Kubernetes. Strong skills in ML frameworks (TensorFlow, PyTorch, scikit-learn), backend engineering, MLOps, and A/B testing are key, along with solid ML and software engineering fundamentals. You'll partner closely with cross-functional teams, so clear communication and a bold, autonomous, fast-moving mindset matter as much as technical depth.

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.
• Develop and optimize machine learning algorithms and models to enhance the recommendation system to improve the discovery experience of users
• 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 recommendation algorithms
• Utilize machine learning frameworks and libraries to deploy scalable and efficient recommendation 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 recommendation capabilities.

Requirements:

• Over 5+ 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 systems, logging, and common operations in production environments.
• Communication skills to carry out projects in collaboration with multiple teams and stakeholders.

Preferred skills:

• Experience developing recommender systems utilizing large-scale data sets.
• Basic knowledge of enterprise search systems and related stacks (e. g., ELK).
• 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 operations experience with Docker and Kubernetes.
• Utilizing deep learning models/LLMs in production.
• Experience in publications at top-tier peer-reviewed conferences or journals.