GPU Acceleration-Velox Product Manager

Introduction At IBM Software, we transform client challenges into solutions. Building the world’s leading AI-powered, cloud-native products that shape the future of business and society. Our legacy of innovation creates endless opportunities for IBMers to learn, grow, and make an impact on a global scale. Working in Software means joining a team fueled by curiosity and collaboration. You’ll work with diverse technologies, partners, and industries to design, develop, and deliver solutions that power digital transformation. With a culture that values innovation, growth, and continuous learning, IBM Software places you at the heart of IBM’s product and technology landscape. Here, you’ll have the tools and opportunities to advance your career while creating software that changes the world. We are looking for a Product Manager to drive critical work across the Velox execution engine. This role sits at the intersection of engineering, open-source development, and product strategy, working alongside senior product leadership to shape how Velox evolves across performance, operator coverage, and hardware acceleration. You will work closely with C++ engineers and open-source contributors, participate in architectural discussions, and represent product priorities within the broader Velox community. We are particularly interested in candidates with deep domain expertise in query engines or data infrastructure, including individuals who may have held solutions engineering, sales engineering, or technical architecture roles in the data platform ecosystem before transitioning into product. Your role and responsibilities As a Technical Product Manager, you will drive product strategy, leveraging your deep domain expertise to identify target markets and opportunities. Your primary responsibilities will include: Roadmap: Drive the Velox roadmap across operator coverage, adaptive execution, codegen integration, and SIMD/GPU acceleration. Engineering Partnership: Work directly with C++ engineers to define operator contracts, memory management policies, and contributor APIs. Participate in design reviews, RFC discussions, and architectural planning. Performance: Define and track engine KPIs including latency, throughput, and memory efficiency. Translate benchmark results and profiling insights into clear product priorities. Cross-Engine Coordination: Support alignment between Velox and engines that embed it (e.g., Presto, Spark). Contribute to integration requirements and migration sequencing. New Use Cases: Partner with ML platform and data infrastructure teams to scope emerging Velox use cases beyond SQL analytics, including ML and AI data pipelines. Required technical and professional expertise Domain Experience: 10+ years of experience in the data infrastructure ecosystem, which may include roles in product management, solutions engineering, sales engineering, developer advocacy, or technical architecture within query engines, lakehouse platforms, or distributed data systems. Product Thinking: Demonstrated ability to translate customer needs, system constraints, and performance insights into clear product requirements and roadmap priorities. Systems Background: Hands‑on technical background in areas such as C++, query execution engines, distributed systems, or runtime/compilers. Comfortable interpreting flame graphs, execution plans, and memory profiling results. Open Source & SQL Platforms: Experience working with open‑source query engines or SQL‑based data platforms and familiarity with open‑source collaboration models. Data Infrastructure Knowledge: Familiarity with vectorized execution, columnar data formats (Arrow, ORC, Parquet), and modern data platform architectures. Communication: Ability to write clear product briefs and technical narratives that resonate with both engineers and leadership. Education: Bachelor’s degree in Computer Science or related field; advanced degree preferred. Preferred technical and professional experience Open Source Contributions: Experience contributing to projects such as Apache Arrow, Velox, Presto, Spark, or similar native query runtimes. Hardware Acceleration: Experience with GPU‑accelerated analytics, SIMD vectorization, or hardware‑aware query execution. ML Data Infrastructure: Background working with ML training data pipelines or data processing frameworks used in AI workloads. Data Platform Ecosystem Experience: Prior experience working with companies in the modern data stack (e.g., lakehouse platforms, distributed query engines, or analytics infrastructure). IBM is committed to creating a diverse environment and is proud to be an equal‑opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, gender, gender identity or expression, sexual orientation, national origin, caste, genetics, pregnancy, disability, neurodivergence, age, veteran status or other characteristics. IBM is also committed to compliance with all fair employment practices regarding citizenship and immigration status. #J-18808-Ljbffr IBM Computing