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DataDirect Networks

High-performance storage for unstructured data

AI Pursuit Engineer

AI Application EngineeringConfirmed live in the last 24 hours
Full-timeNo salary listedRemote

About the Job

Requirements

  • A bachelor's degree in Computer Science, Computer Engineering, Software Engineering, or a related technical discipline is required; a master's degree is preferred.
  • At least 12 years of software engineering experience with technical leadership in large-scale distributed systems.
  • Experience designing and delivering AI-assisted software development platforms or developer productivity tooling.
  • Experience using AI within correctness-critical software systems, directing coding agents and automated quality engineering agents, and validating AI-generated outputs through rigorous evaluation frameworks.
  • Expertise in machine learning infrastructure, large language model inference serving, and distributed AI systems.
  • Experience building developer platforms, engineering tooling, and software automation frameworks.
  • Experience with agentic systems, inference orchestration, and AI workflow automation.
  • Hands-on experience managing GPU clusters, AI compute infrastructure, or large-scale distributed computing environments.
  • Understanding of software architecture, continuous integration and continuous delivery pipelines, automated testing frameworks, and production software lifecycle management.
  • Experience mentoring engineers and leading highly technical engineering initiatives.

Responsibilities

  • Lead the architecture, technical direction, and execution of the Harness & Loop Engineering platform.
  • Design and build AI-powered development environments, agentic engineering sandboxes, automated quality engineering systems, and inference orchestration platforms.
  • Develop engineering platforms that improve developer productivity while maintaining standards for correctness, reliability, and security.
  • Own technical architecture and provide final design and code review authority for AI-generated software changes.
  • Define evaluation frameworks, automated testing strategies, validation pipelines, and engineering guardrails for AI-assisted software development.
  • Partner with the Foundation SDK team to co-evolve platform capabilities and integrate emerging technologies into production-ready solutions.
  • Lead the evolution of internal engineering platforms into scalable, externally consumable products after internal validation.
  • Oversee internal GPU infrastructure, inference services, and Infinia cluster operations supporting AI development and testing environments.
  • Establish engineering best practices for Loop Engineering, agentic development workflows, and AI-assisted software delivery.
  • Mentor and develop engineers across the organization through coaching, architectural guidance, and technical leadership.
  • Serve as a technical interviewer and hiring bar-raiser for engineering talent.
  • Collaborate with software engineering, platform engineering, AI research, quality engineering, and product management teams to deliver scalable engineering capabilities.

Desired Qualifications

  • Experience productizing internal engineering platforms for external customers.
  • Knowledge of AI infrastructure management, model serving frameworks, and high-performance computing environments.
  • Experience with large-scale observability, platform reliability, and engineering operations.
  • Familiarity with storage systems, distributed data platforms, or enterprise AI infrastructure.
  • Contributions to open-source developer platforms, AI tooling, or infrastructure projects.

About the Company

DataDirect Networks

High-performance storage for unstructured data

DDN provides high-performance data storage systems and software for data-intensive environments, including unstructured data, HPC, and AI workloads. Its products combine hardware and software to manage the data lifecycle, with A³I for GPU-optimized AI, EXAScaler as a parallel file system, IntelliFlash hybrid arrays, and Infinia for fast inference. The company differentiates itself through a dedicated focus on HPC/AI storage, close collaboration with NVIDIA, and a strategy of expanding software-defined storage and file systems via acquisitions. Its goal is to help customers scale data storage and AI workloads efficiently across enterprises, government, academia, and cloud providers.

Company size
1,001-5,000
Company stage
Growth Equity (Venture Capital)
Total funding
$309.9M
Headquarters
Los Angeles, CA
Founded
1998

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