GPU Acceleration-Velox Product Manager

IBM

Quick summary

Work type
On-site
Location
San Jose, CA
Posted
60 days ago

Market check

Salary context

How this pay compares to similar roles

Similar $206k
$158k most similar roles pay here $254k

This listing doesn't post a salary. Most similar roles pay $177,250–$235,750.

Based on 240 similar postings.

Employer

About IBM

IBM is a US-based global technology company providing hybrid cloud, AI, consulting, enterprise software, and IT infrastructure products and services.

IBM currently has 743 open roles on FindRole.

Listed pay typically runs $1,000,000–$1,000,000 across 8 roles with salary data.

Most-posted roles

View all roles at IBM

At a glance

TL;DR · GPU Acceleration-Velox Product Manager

Apply Now Log in to save

We are seeking a Senior Product Manager to drive critical initiatives for the Velox execution engine in San Jose. This role involves shaping product strategy and collaborating with senior leadership to enhance performance, operator coverage, and hardware acceleration. Day-to-day responsibilities include defining operator contracts, memory management policies, and contributor APIs alongside C++ engineers, participating in design reviews, and tracking KPIs such as latency and throughput. The ideal candidate has deep expertise in query engines or data infrastructure, experience with open-source collaboration models, and hands-on technical skills in areas like C++, distributed systems, and vectorized execution. Familiarity with GPU-accelerated analytics, SIMD vectorization, and modern data platform architectures is essential, as well as contributions to projects such as Apache Arrow, Velox, Presto, or Spark.

Skills

C++ Velox Presto Spark Apache Arrow GPU SIMD Vectorized Execution Columnar Data Formats Arrow ORC Parquet CI/CD Docker Kubernetes AWS Google Cloud Platform GitHub JIRA Confluence

What you'll do

  • Drive Velox roadmap for operator coverage and hardware acceleration.
  • Define and track engine KPIs like latency, throughput, and memory efficiency.
  • Work with C++ engineers to establish operator contracts and APIs.
  • Support alignment between Velox and embedding engines like Presto and Spark.
  • Partner with ML platform teams to scope new Velox use cases beyond SQL analytics.
  • Participate in architectural discussions and design reviews for Velox.

What we're looking for

  • Deep domain expertise in query engines or data infrastructure.
  • Experience with open-source query engines and SQL-based data platforms.
  • Hands-on technical background in C++, distributed systems, and runtime/compilers.
  • Familiarity with vectorized execution and modern data platform architectures.
  • Ability to define and track engine KPIs like latency, throughput, and memory efficiency.
  • Experience contributing to projects such as Apache Arrow or Velox.
  • Knowledge of GPU-accelerated analytics and hardware-aware query execution.

Related searches

More GPU Acceleration jobs GPU Acceleration jobs in San Jose All jobs at IBM

More like this

Similar roles

GPU Acceleration-Velox Product Manager

IBM

Austin, TX 60 days ago
C++ Velox Presto Spark Apache Arrow GPU SIMD Vectorized Execution Columnar Data Formats Arrow ORC Parquet CI/CD Docker Kubernetes AWS Google Cloud Platform GitHub JIRA Confluence
Save

Accelerated Computing GPU Product Manager

Nvidia

Santa Clara, CA 148 days ago $168,000–$258,750
GPU Large Language Models Cloud Computing Infrastructure AI Applications Training and Inference Kubernetes Docker CI/CD AWS Azure Google Cloud Platform PostgreSQL MongoDB Git Jenkins Prometheus Grafana Terraform Python C++
Save

Senior Accelerated Computing GPU Product Manager

Nvidia

Santa Clara, CA 124 days ago $168,000–$258,750
GPU Large Language Models Training and Inference Cloud Computing Infrastructure CI/CD Kubernetes Docker Python PostgreSQL TensorFlow PyTorch AWS Azure Grafana Prometheus
Save

Implementation Methodology Engineer - GPU

Nvidia

Santa Clara, CA 18 days ago $136,000–$218,500
Synopsys Cadence DC FC Genus Innovus Python Tcl Make EDA VLSI logic optimization physical design implementation placement routing logic restructuring
Hybrid
Save