Senior Director of Engineering, Inference Platform

The Senior Director of Engineering, Inference Platform at MongoDB is responsible for leading the Voyage Inference Engineering Team, ensuring the design, development, and scaling of the inference platform. This includes maintaining high performance, reliability, and efficiency while collaborating with research engineers to integrate AI models into production and developing scalable APIs. The role also involves recruiting top engineering talent and defining the product and engineering strategy for the platform.

To be considered for the Senior Director of Engineering, Inference Platform position at MongoDB, candidates should have over 10 years of engineering leadership experience, particularly with managing distributed systems. Essential qualifications include a proven track record of building ML inference platforms in production, expertise in cloud-native architectures, and proficiency in API development. Strong leadership skills and a solid understanding of ML frameworks are also critical.

The Senior Director of Engineering, Inference Platform will significantly impact MongoDB's AI strategy by leading the development of core AI/ML services. This role is crucial in shaping the technical vision and long-term architecture of the inference platform, thus enhancing MongoDB's capabilities in AI-driven search and ensuring that the company remains a leader in industry innovations.

In the Senior Director of Engineering, Inference Platform role at MongoDB, you will handle high-scale, complex projects related to AI/ML technologies. You’ll architect and optimize large-scale inference pipelines, work on integrating AI models into the MongoDB Atlas, and develop real-time API solutions that support robust inference operations, ultimately driving the AI capabilities of MongoDB's systems.

MongoDB prides itself on fostering a collaborative and innovative culture, especially for the Senior Director of Engineering, Inference Platform. The company supports employee growth through various initiatives, including mentoring programs and professional development opportunities, while emphasizing work-life balance. The hybrid working model allows flexibility, encouraging productivity and a dynamic work environment.

When answering this question, detail specific projects where you've built or managed inference platforms, highlighting the technologies used and the challenges you overcame. Describe how your leadership skills helped in driving team success and collaboration, mentioning any measurable outcomes to back your claims.

Discuss specific strategies you've implemented to scale distributed systems, such as using microservices architecture, container orchestration with Kubernetes, and efficient load balancing. Provide real examples that demonstrate the impact of these strategies on system performance and reliability.

In your response, describe the entire process from model selection and validation to deployment and monitoring. Emphasize the importance of collaboration with cross-functional teams and how you ensure smooth integration, along with any tools or frameworks you've utilized in production.

Here, outline your methods for ensuring operational excellence, such as implementing monitoring systems for performance tracking, establishing service level objectives (SLOs), and developing incident response protocols. Share your experiences in leading initiatives that have improved reliability or reduced downtime.

Discuss your background in developing APIs specifically for real-time inference applications. Share details about the technologies you've used, how you manage API scalability, and any best practices you follow to ensure performance and security.

Explain your philosophy on hiring, including what qualities you value most in candidates. Describe how you create an inclusive and mentoring culture to develop talent within your teams, as well as any metrics you use to measure hiring success and team growth.

Narrate a particular scenario that posed challenges, focusing on your problem-solving approach, how you led your team through this obstacle, and the positive outcomes achieved. This is a great opportunity to showcase your resilience and leadership.

Here, share your insights on current AI/ML trends, pulling from your own research and experience. Discuss how certain trends, such as advancements in model training, need for real-time inference capabilities, or innovations in hardware can impact MongoDB's product strategy.

Detail the methods you use to validate and monitor the performance of ML models after deployment, including techniques like A/B testing, continuous integration/continuous deployment (CI/CD) practices, and performance tracking. Share how these practices enhance service reliability.

Discuss the essential elements you evaluate when defining strategy, such as market trends, user feedback, technology advancements, and resource availability. Highlight your approach to aligning product vision with engineering capabilities and overall business goals.