Senior Software Engineer, MongoDB

• You have a strong problem solving skills and take pride in the strong sense of ownership and accountability
• You have strong programming (Go, Python, Java or equivalent) skills and appetite of pursuing engineering best practices
• You have an extensive experience in designing and implementing end-to-end machine learning infrastructure and productionizing machine learning models in large-scale industry settings
• You have a deep understanding of machine learning best practices in areas such as model training, serving, optimization, experimentation and more
• You have an extensive exposure to architectural patterns of large, high-scale systems with well-designed APIs, high volume data pipelines and robust monitoring
• You are a team player who can effectively communicate and collaborate with key engineering and business partners
• You are familiar with test-driven development, incremental delivery and deployment processes
• You’re passionate about developing reliable and high quality software
• You’re curious, collaborative and intellectually honest

Desirable

• MongoDB is growing rapidly and seeking a Senior Software Engineer for the Machine Learning Platform team to be a key contributor to the critical data science and machine learning initiatives at MongoDB
• The ML Platform team focuses on building a scalable, reliable, and flexible machine learning ecosystem encompassing key components such as model training, deployment, instrumentation and performance tracking in order to empower critical MongoDB product and business initiatives
• As a Software Engineer, you will design and build a scalable platform to effectively develop, manage and deploy machine learning models to help drive MongoDB’s growth as a product and as a company, while also lending your technical expertise to other engineers as a mentor and trainer
• Build production-ready services to deploy machine learning models and integrate to a variety of MongoDB systems
• Perform code reviews with peers and make recommendations on how to improve our code and software development processes
• Design machine learning system architecture that can abstract and automate critical machine learning product lifecycle including training, model management, tracking and deployment
• Continuously optimize and tune critical infrastructure and services to ensure that the machine learning platform and services are operating efficiently
• Collaborate with other software engineers, data scientists, and key stakeholders, taking learning and leadership opportunities that will arise every single day
• Further improve the team’s testing and development processes
• Document and educate the larger team on best practices
• Help drive optimization, testing, and tooling to improve ML platform quality