Senior Software Engineer, Machine Learning Platform

MongoDB - New York, NY

Full-time - Mid Level

New York, NY

Professional, Scientific, and Technical Services

About the position

MongoDB is at the forefront of the data management software market, which is projected to grow significantly in the coming years. As a Senior Software Engineer for the Machine Learning Platform team, you will play a pivotal role in shaping the future of machine learning initiatives at MongoDB. This position involves designing and building a scalable platform that facilitates the development, management, and deployment of machine learning models. Your contributions will directly impact MongoDB's growth and innovation, empowering developers to create applications that enhance user experiences. In this role, you will be responsible for tackling complex platform challenges, ensuring that the machine learning ecosystem is reliable, scalable, and robust. You will collaborate with other engineers, data scientists, and key stakeholders to create a seamless integration of machine learning models into MongoDB's systems. Your expertise will also be utilized in mentoring and training fellow engineers, fostering a culture of continuous improvement and knowledge sharing within the team. The ideal candidate will possess strong programming skills in languages such as Go, Python, or Java, and have extensive experience in designing and implementing end-to-end machine learning infrastructure. A deep understanding of machine learning best practices, including model training, serving, and optimization, is essential. You will also be expected to have experience with architectural patterns of large-scale systems, well-designed APIs, and high-volume data pipelines. Your role will involve not only technical contributions but also enhancing the team's development processes and documentation practices, ensuring that best practices are shared and adopted across the organization.

Responsibilities

• Build production-ready services to deploy machine learning models and integrate them into various MongoDB systems.
• Perform code reviews with peers and recommend improvements to code and software development processes.
• Design machine learning system architecture that automates the critical machine learning product lifecycle, including training, model management, tracking, and deployment.
• Continuously optimize and tune critical infrastructure and services to ensure efficient operation of the machine learning platform and services.
• Collaborate with software engineers, data scientists, and key stakeholders, seizing learning and leadership opportunities.
• Further improve the team's testing and development processes.
• Document and educate the larger team on best practices.
• Drive optimization, testing, and tooling to enhance ML platform quality.

Requirements

• Strong problem-solving skills with a sense of ownership and accountability.
• Proficient in programming languages such as Go, Python, Java, or equivalent.
• Extensive experience in designing and implementing end-to-end machine learning infrastructure.
• Deep understanding of machine learning best practices in model training, serving, optimization, and experimentation.
• Experience with architectural patterns of large, high-scale systems, including well-designed APIs and high-volume data pipelines.
• Ability to communicate and collaborate effectively with engineering and business partners.
• Familiarity with test-driven development and incremental delivery processes.
• Passion for developing reliable and high-quality software.

Nice-to-haves

• Experience with large-scale data using distributed processing platforms like Spark, Ray, and Trino.
• Familiarity with data infrastructure tools such as Presto, Hive, Spark, and BigQuery.
• Experience with containerization and orchestration platforms like Docker and Kubernetes.
• Working knowledge of cloud platforms and services.
• Familiarity with operational tools for machine learning services.
• A security-first mindset with attention to security best practices.

Benefits

• Equity participation in the employee stock purchase program.
• Flexible paid time off.
• 20 weeks fully-paid gender-neutral parental leave.
• Fertility and adoption assistance.
• 401(k) plan with company contributions.
• Mental health counseling services.
• Access to transgender-inclusive health insurance coverage.
• Comprehensive health benefits offerings.