ATG AI Integration Engineer

Daimler Truck•Portland, OR

1d•Hybrid

About The Position

As a Software Tooling and AI Integration Engineer at Daimler Truck North America, you will shape how engineering teams create, standardize, and validate specifications by embedding AI driven capabilities directly into the software tooling ecosystem. This role within the Autonomous Technology Group will work at the intersection of software engineering, test automation, and artificial intelligence, leveraging modern AI tools and Large Language Models (LLMs) to improve how specifications are authored, expanded, reviewed, and transformed into executable test artifacts. In this role, you will partner closely with system engineers, test teams, and software developers to ensure specifications are not only clear and consistent, but also machine interpretable and automation ready. Your work will directly influence how Hardware in the Loop (HiL) and vehicle level test cases are generated and executed, accelerating validation cycles while improving quality, traceability, and developer experience.

Requirements

Bachelor’s degree in Computer Science, Software Engineering, or a related engineering discipline and 2+ years of relevant experience is required
Strong software engineering fundamentals, including experience building and maintaining internal tools or developer‑facing applications
Experience working with or integrating AI systems, such as LLMs, agent frameworks, or AI‑assisted development tools
Familiarity with test automation concepts, including HiL, SiL, or vehicle‑level testing environments
Ability to translate loosely defined engineering needs into structured, scalable software solutions
Demonstrated ability to work effectively across multidisciplinary engineering teams
Strong sense of ownership, curiosity, and initiative within your technical domain
Ability to communicate complex technical concepts clearly to both technical and non‑technical audiences
Commitment to continuous learning and staying current with evolving physical autonomy, AI, and software development practices
An attached resume is required

Nice To Haves

Experience with design of vehicle autonomy systems and the associated development, test, and deployment processes
Experience using LLMs for code generation, documentation generation, or automated review workflows
Familiarity with requirements management, specification authoring, or standards‑based engineering workflows
Hands‑on experience generating automated test cases from specifications or models
Exposure to automotive or embedded software development and validation processes
Experience designing tools with a strong focus on usability and developer experience (UX)
Knowledge of traceability concepts across requirements, tests, and implementation
Experience evaluating AI limitations such as hallucinations, determinism, explainability, and validation in engineering contexts

Responsibilities

Apply AI‑based tools and agent‑driven workflows to optimize how engineering specifications are authored, reviewed, and standardized
Design and implement mechanisms to expand standardized specifications into automatically generated test cases for HiL and vehicle‑level testing
Integrate AI‑assisted test generation into existing software tooling developed by the team, ensuring compatibility with established validation pipelines
Incorporate Large Language Models (LLMs) into the software tool development lifecycle to improve documentation quality, code review effectiveness, and overall user experience
Collaborate with system, software, and test engineering teams to ensure AI‑enabled tooling aligns with real‑world engineering workflows and validation needs
Define and maintain technical concepts, architectures, and roadmaps for AI integration within internal software tools
Validate and verify AI‑enabled tooling through reviews, experimentation, and targeted testing to ensure reliability, traceability, and scalability
Identify technical risks and limitations related to AI usage and provide clear reporting, recommendations, and mitigation strategies
Define the data and measurable indicators needed to evaluate virtual driver systems on DTNA vehicles against DTNA performance standards, including ODD expansion rate, incident reporting, remote assistance activity, system capability, and in-service performance.
Establish apples-to-apples assessment criteria for VD partners by benchmarking autonomous performance against Cascadia durability, maintenance intervals, fuel efficiency, and vehicle uptime to ensure solutions deliver incremental customer value on DTNA platforms.
Contribute lessons learned and best practices to influence future tooling standards and development approaches across the organization