Senior/Principal AI Platform Engineer - Feature Store Infrastructure

As a Senior/Principal AI Platform Engineer at Roblox, your responsibilities will include designing and building the Knowledge Graph and Feature Store infrastructure. This involves developing large-scale ML data pipelines for both batch and real-time environments, ensuring low-latency access for AI inference, and collaborating with cross-functional AI teams to innovate AI-driven solutions. Your work will directly impact billions of daily interactions on the platform.

To qualify for the Senior/Principal AI Platform Engineer role at Roblox, candidates should possess a minimum of 5 years of experience in AI/ML data infrastructure, with a focus on knowledge graphs and feature stores. A strong background in software engineering, especially using distributed computing frameworks and real-time data systems, is essential. Additionally, familiarity with ML Feature Stores and knowledge graphs is critical for success in this role.

Roblox empowers its AI Platform Engineers by providing them with end-to-end ownership of significant projects. Engineers have the opportunity to shape the AI landscape by developing infrastructures that drive creativity and digital experiences for millions of users. Additionally, the company fosters a culture of innovation, allowing engineers to explore cutting-edge technologies and collaborate with diverse teams.

The expected salary range for the Senior/Principal AI Platform Engineer position at Roblox is between $238,520 and $391,590 per year. However, the actual base pay may vary based on individual qualifications and market demand. Roblox also offers a competitive compensation package that includes equity compensation and a range of benefits to support its employees.

The work culture at Roblox for AI Platform Engineers is one of collaboration, innovation, and empowerment. The team values diversity and encourages creativity, allowing engineers to tackle complex challenges while shaping AI applications that enhance user experiences. Additionally, with supportive workplace policies and perks like free catered lunches and an onsite fitness center, Roblox fosters a healthy, inclusive environment for its employees.

When answering this question, give specific examples of projects where you designed or enhanced data pipelines for ML applications. Discuss the technologies used, the challenges faced, and how you ensured scalability and efficiency. Highlight any success metrics to showcase the impact of your contributions.

In your response, define a Feature Store as a centralized repository that allows teams to store, manage, and serve features for ML models. Explain its significance in ensuring consistency, reducing redundancy, and democratizing access to features across different teams and projects, ultimately enhancing productivity and model performance.

When discussing designing a Knowledge Graph, emphasize the importance of understanding the relationships between entities. Talk about how you would define the schema, choose the right database technology, and ensure that the graph is scalable and capable of supporting various AI applications. Mention any past experiences to back your points.

Be specific about the distributed computing frameworks you've used, such as Spark or Flink. Explain the contexts in which you applied them, detailing any unique solutions you devised or performance improvements you achieved. Having a solid grasp of the benefits and challenges associated with these frameworks will showcase your expertise.

When answering this question, list the tools you're proficient in, like Kafka or Redis. Provide examples of how you managed real-time data streams, focusing on how you ensured low latency and data integrity. Highlight any projects that required real-time processing to emphasize your expertise.

Share a specific challenge you encountered, such as data bottlenecks or deployment failures. Detail your problem-solving approach and actions taken, including any collaboration with cross-functional teams. Highlight the outcome and what you learned, showcasing both your technical and teamwork skills.

Discuss strategies for ensuring model robustness during inference, such as implementing proper versioning and monitoring, validating inputs, or testing against various edge cases. Provide examples of how you handled these situations in previous projects, emphasizing the importance of maintaining performance and reliability.

Elaborate on the importance of collaboration when it comes to building AI infrastructures. Mention experiences where you worked with cross-functional teams, the benefits of peer feedback, and the need for collective problem-solving. Highlight how interdisciplinary collaboration can lead to innovative solutions.

Share the resources and methods you utilize to keep abreast of the latest AI/ML developments. This could include literature, attending conferences, engaging in online communities, or participating in workshops. Express a passion for continuous learning, and note how staying informed has benefited your career.

In your answer, stress the importance of being open to feedback and constructive criticism. Discuss how you would listen to the perspectives of others, evaluate their validity, and either adapt your approach or justify your decisions with data. Emphasize the value of adaptability and collaboration in project success.