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Chief Scientist – Technical Artificial Intelligence
Location
United States
Posted
114 days ago
Salary
$138K - $230K / year
Seniority
Lead
Job Description
Chief Scientist – Technical Artificial Intelligence
Cummins Inc.
• Serve as the enterprise’s senior technical authority for generative and agentic AI, setting the standard for technical rigor and shaping how the Technical Function understands, evaluates, and applies advanced AI capabilities. • Define and evolve AI principles, reference architectures, and technical standards that guide safe, scalable, and robust AI adoption across complex industrial engineering environments and influence long-term technology strategy. • Translate frontier AI research into practical, actionable guidance, identifying emerging methods and design patterns and ensuring Cummins remains on the leading edge of innovation. • Drive and accelerate high-value engineering use cases by advising teams on applying LLMs, RAG, and agent-based systems, improving decision quality, and enabling new technical workflows. • Evaluate and validate AI model performance and risks through stress-testing, failure‑mode analysis, and domain-expert collaboration to ensure outputs are explainable, traceable, and decision-supportive. • Build organizational capability and ensure responsible scaling by coaching practitioners, raising AI literacy, and providing senior oversight for pilots and early deployments to clarify where AI adds value—and where human judgment remains essential.
Job Requirements
- Advanced academic or technical foundation in Computer Science, Engineering, Data Science, or a related field (or equivalent industry experience), enabling rigorous understanding of AI methods in industrial contexts.
- Hands-on mastery of modern AI toolchains and ecosystem technologies, including model evaluation techniques, long‑context handling, multi-step reasoning workflows, and infrastructure supporting agentic systems.
- Proficiency in designing AI validation, stress‑testing, and risk‑assessment frameworks, with the ability to assess hallucination risks, misuse scenarios, and model reliability in engineering‑critical environments.
- Strong ability to collaborate with diverse domain experts—engineering, product, software, operations—to ensure AI outputs are grounded, explainable, and aligned with real-world technical constraints.
- Experience developing new AI-driven products, applications, or workflows, including scoping requirements, co-innovating with vendors, and contributing to enterprise-wide tools, kits, and system roadmaps.
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