Senior Software Engineer, Machine Learning Platform

The worldwide data management software market is massive (According to IDC, the worldwide database software market, which it refers to as the database management systems software market, was forecasted to be approximately $82 billion in 2023 growing to approximately $137 billion in 2027. This represents a 14% compound annual growth rate). At MongoDB we are transforming industries and empowering developers to build amazing apps that people use every day. We are the leading developer data platform and the first database provider to IPO in over 20 years. Join our team and be at the forefront of innovation and creativity.

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. You will tackle complex platform problems with the goal of making our platform more scalable, reliable, and robust.

Who you are:

• 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

Bonus Points:

• You have experience with working with large-scale data using distributed processing platform and query engine such as Spark, Ray and Trino
• You are familiar with data infrastructure and toolings such as Presto, Hive, Spark and BigQuery
• You have a working experience with containerization and orchestration platform such as Docker and Kubernetes
• You have experience with or working knowledge of cloud platforms and services
• You are familiar with operational toolings for machine learning services
• You have a security-first mindset and keen eyes on security best practices to ensure that our services adhere the security best practices

What you will do:

• 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

To drive the personal growth and business impact of our employees, we’re committed to developing a supportive and enriching culture for everyone. From employee affinity groups, to fertility assistance and a generous parental leave policy, we value our employees’ wellbeing and want to support them along every step of their professional and personal journeys. Learn more about what it’s like to work at MongoDB, and help us make an impact on the world!

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