About the role
Partners with appropriate stakeholders to determine user
requirements for one or more complex scenarios. Provides technical leadership for the identification of dependencies and the development of design documents for a product, application, service, or platform. Leads by example and mentors' others to produce extensible and maintainable code used across the company. Leverages deep subject-matter expertise of cross-product features with appropriate stakeholders (e.g., project managers) to lead multiple product's project plans, release plans, and work items. Holds accountability as a Designated Responsible Individual (DRI), mentoring engineers across products/solutions, working on call to monitor system/product/service for degradation, downtime, or interruptions. Proactively seeks new knowledge and adapts to new trends, technical solutions, and patterns that will improve the availability, reliability, efficiency, observability, and performance of products while also driving consistency in monitoring and operations at scale and shares knowledge with other engineers. Bachelor's Degree in Computer Science or related technical field AND 6+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python. Proven ability to define long-term ML infrastructure strategy and drive cross-org alignment across engineering, product, and research. Hands-on experience working or building robust, ML systems with high reliability, low latency, and seamless platform integration. Bachelor's Degree in Computer Science or related technical field AND 12+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript or Python. OR Master's Degree in Computer Science or related technical field AND 10+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python. OR equivalent experience. Experience architecting ML inference pipelines for LLMs. Experience building local model integrations in system or app level components. Demonstrated mastery in ML compiler design, hardware-aware optimizations, and scalable infrastructure across heterogeneous platforms.
