ddn - Ai Pursuit Engineer
Requirements
• Bachelor's degree in Computer Science, Computer Engineering, Software Engineering, or a related technical discipline; Master's degree preferred. • 12+ years of software engineering experience with demonstrated technical leadership in large-scale distributed systems. • Proven experience designing and delivering AI-assisted software development platforms or developer productivity tooling. • Demonstrated success using AI within correctness-critical software systems, including directing coding agents, automated quality engineering agents, and validating AI-generated outputs through rigorous evaluation frameworks. • Deep expertise in machine learning infrastructure, LLM inference serving, and distributed AI systems. • Strong 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. • Strong understanding of software architecture, CI/CD pipelines, automated testing frameworks, and production software lifecycle management. • Proven experience mentoring engineers and leading highly technical engineering initiatives. • 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. • AI-Assisted Software Development • Agentic Engineering Systems • Loop Engineering • Developer Platforms & Tooling • LLM Inference Serving • Inference Orchestration • GPU Infrastructure & Cluster Operations • Distributed Systems Architecture • CI/CD Automation • Automated Quality Engineering • Software Validation & Evaluation Frameworks • Cloud & Container Technologies • Production Platform Engineering • A passion for building engineering platforms that fundamentally improve how software is developed. • Exceptional architectural thinking with the ability to balance innovation, scalability, and operational excellence. • Strong technical judgment in evaluating AI-generated software within correctness-critical environments. • A collaborative leadership style with the ability to mentor engineers and influence technical direction across organizations. • A product mindset focused on transforming internal innovation into customer-ready capabilities.
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 significantly improve developer productivity while maintaining rigorous 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 that govern AI-assisted software development. • Partner closely 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 once validated through internal adoption. • 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, raising technical standards through coaching, architectural guidance, and technical leadership. • Serve as a key technical interviewer and hiring bar-raiser for engineering talent. • Collaborate cross-functionally with software engineering, platform engineering, AI research, quality engineering, and product management teams to deliver scalable engineering capabilities.
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