Director of Engineering
ML Platform
Confirmed live in the last 24 hours

5,001-10,000 employees

Modern, general-purpose database platform
Company Overview
MongoDB empowers innovators to create, transform, and disrupt industries by unleashing the power of software and data
Data & Analytics

Company Stage

Series F

Total Funding





New York, New York

Growth & Insights

6 month growth


1 year growth


2 year growth

Remote in USA • New York, NY, USA
Experience Level
Desired Skills
Data Science
AI & Machine Learning
  • Experience building systems that support robust, large-scale data pipelines and machine learning model training and hosting, designed for scalability, repeatability, automation, and CI/CD/CT
  • Strong understanding of the development and operational patterns of data scientists
  • Proven success designing, building, testing, debugging, and performance tuning of distributed and/or highly concurrent software systems
  • Ability to think through requirements and choose the right architecture and tooling to incrementally build systems while addressing the needs of the job at hand
  • Proficient with Python
  • Experience reviewing code from junior developers
  • Excellent verbal and written technical communication skills and desire to collaborate with colleagues, mentor fellow engineers and assume project ownership and accountability
  • Team player who lives MongoDB's values and thrives in a diverse and inclusive work culture
  • Lead a team responsible for designing, building, and maintaining MongoDB's ML Platform, supporting real-time data pipelines, data observability, as well as training, orchestration, and productization of machine learning and large language models
  • Accelerate the productization of AI/ML innovations into production code via infrastructure that can perform at scale and is resilient
  • Work closely with data science, product management, product engineering, infrastructure engineering, as well as other teams within the company, to define the first iteration and future evolution of the ML platform
  • Collaborate with internal Data Platform leadership to minimise duplication of effort
  • Shape architecture, development practices, and escalation policies as the teams and platform usage grow
  • Address platform requirements so that support engineers can quickly detect and correct issues in production
  • Develop and maintain tools and processes supporting automated platform testing and live upgrades of platform software
  • Stay in regular communication with data science, product and product engineering teams to understand how the platform can best assist in other teams' objectives
  • Lead the process, in collaboration with MongoDB recruiting, to hire the right mix of skills and experience for the team, considering both internal and external candidates
  • Participate in code review and contribute to technical standards
  • Mentor junior developers