Applied AI Engineer I

dtna; Portland (OR); On-site; USD 71,000 - 91,000; Full time

Benefits offered by this job

401k with company match

Vacation & holidays

Paid parental leave

Healthcare plans

Tuition assistance

Job summary

DTNA is seeking an Engineering Quality, Safety and Compliance (EQSC) professional to advance data lifecycle practices and connect design risk assessment with manufacturing and field insights. You will help deploy AI-enabled workflows, configure AI agents, and ensure well-governed data solutions across vehicle programs.

The role emphasizes collaboration with Vehicle Engineering, Product Validation, Manufacturing, Service, and IT, with a focus on test, adoption, and documentation to support

Qualifications

• A bachelor's degree in engineering, computer science, data science, or a related technical field.
• 0-2 years of relevant experience through work, internships, co-ops, academic projects, or applied technical projects.
• Foundational understanding of AI/ML and GenAI concepts, including large language models, embeddings, retrieval, prompt patterns, and basic model evaluation.
• Awareness of responsible AI practices, including

Responsibilities

• Support governed data pipelines, including Snowflake-enabled datasets, by helping prepare, clean, validate, and connect requirements, specifications, validation records, vehicle compliance inputs, defect investigations, manufacturing data, service data, warranty information, and field-quality insights.
• Assist with SQL queries, data models, metadata fields, and data-quality checks that improve traceability, reliability, and readiness for analytics and AI-assisted workflows.
• Contribute to AI-agent implementation by helping configure workflows, retrieval patterns, prompt examples, test cases, and deployment-support materials under guidance from senior team members.
• Prepare approved standards, process guidance, historical examples, compliance references, investigation learnings, and engineering knowledge content for use in AI-assisted workflows and evaluation datasets.
• Help test, validate, and deploy AI-agent capabilities using approved enterprise platforms, Snowflake-enabled data assets, Microsoft 365 Copilot / Copilot Studio, APIs, and related tools.
• Capture data-quality issues, manual handoffs, duplicated steps, user pain points, pilot feedback, and improvement ideas in issue-tracking or backlog tools to support practical workflow improvements.
• Support analysis of connected engineering, compliance, investigation, manufacturing, service, warranty, and field data to help improve risk assessment, product-quality decisions, corrective-action follow-up, and service diagnostics.
• Help measure AI-agent output quality, efficiency, token usage, user feedback, and accuracy by supporting evaluation datasets, regression testing, grounding checks, stress testing, and hallucination-reduction reviews.
• Create and maintain implementation notes, prompt/configuration change logs, user guidance, training aids, data definitions, known limitations, and adoption content in Confluence, SharePoint, and similar enterprise knowledge platforms.
• Work with Vehicle Engineering, Product Engineering, Vehicle Compliance, Product Validation, Manufacturing, Service, Quality, IT, defect investigation teams, and regional/global stakeholders to support user acceptance testing, adoption, and well-governed AI and data solutions.

Skills

SQL

Data modeling

AI/ML basics

Data governance

Stakeholder collaboration

Education

Bachelor’s degree in engineering/computer science/data science or related field

Tools

Snowflake

Microsoft 365 Copilot

Confluence

SharePoint

APIs

Job description

Inside the Role

The Engineering Quality, Safety and Compliance (EQSC) team at DTNA is at the heart of product integrity, design risk assessment, and data-driven quality improvement across vehicle development programs. EQSC works with design engineering, product validation, manufacturing, service, warranty, and cross-functional teams to improve how engineering quality work products are created, connected, governed, and used to support stronger design decisions.

In this role, you will be a key contributor to the EQSC team with responsibility for improving data lifecycle practices and connecting design risk assessment with manufacturing and real-world field insights. You will support short- and long-term quality improvement strategies, translate defined AI-enabled EQSC concepts into usable workflows, and help deploy practical data and process solutions through reliable, well-governed data models and agents.

This role is highly collaborative and requires the ability to work across Vehicle Level Engineering, Product Engineering, Product Validation, Manufacturing, Service, Quality, IT, and business stakeholders. The role will help operationalize the AI-enabled strategy envisioned by the leadership team across EQSC by coordinating user feedback, adoption documentation, training support, configuration inputs, and implementation readiness while preserving engineering verification ownership within the expert teams.

Posting Information

We provide a scheduled posting end date to assist our candidates with their application planning. While this date reflects our latest plans, it is subject to change, and postings may be extended or removed earlier than expected.

We Take Care of Our Team

Position offers a starting salary range of $71,000 to $91,000 USD

Pay offered dependent on knowledge, skills, and experience

Benefits include 401k company contribution with company match up to 8% as well as non-elective company contribution of 3 - 7% depending on age; starting at 4 weeks paid vacation; 13+ calendar holidays; 8 weeks paid parental leave; employee assistance program; comprehensive healthcare plans and wellness programs; onsite fitness (at some locations); tuition assistance and volunteer paid time off; short-term and long-term disability plans.

What You Drive at DTNA

• Support governed data pipelines, including Snowflake-enabled datasets, by helping prepare, clean, validate, and connect requirements, specifications, validation records, vehicle compliance inputs, defect investigations, manufacturing data, service data, warranty information, and field-quality insights.
• Assist with SQL queries, data models, metadata fields, and data-quality checks that improve traceability, reliability, and readiness for analytics and AI-assisted workflows.
• Contribute to AI-agent implementation by helping configure workflows, retrieval patterns, prompt examples, test cases, and deployment-support materials under guidance from senior team members.
• Prepare approved standards, process guidance, historical examples, compliance references, investigation learnings, and engineering knowledge content for use in AI-assisted workflows and evaluation datasets.
• Help test, validate, and deploy AI-agent capabilities using approved enterprise platforms, Snowflake-enabled data assets, Microsoft 365 Copilot / Copilot Studio, APIs, and related tools.
• Capture data-quality issues, manual handoffs, duplicated steps, user pain points, pilot feedback, and improvement ideas in issue-tracking or backlog tools to support practical workflow improvements.
• Support analysis of connected engineering, compliance, investigation, manufacturing, service, warranty, and field data to help improve risk assessment, product-quality decisions, corrective-action follow-up, and service diagnostics.
• Help measure AI-agent output quality, efficiency, token usage, user feedback, and accuracy by supporting evaluation datasets, regression testing, grounding checks, stress testing, and hallucination-reduction reviews.
• Create and maintain implementation notes, prompt/configuration change logs, user guidance, training aids, data definitions, known limitations, and adoption content in Confluence, SharePoint, and similar enterprise knowledge platforms.
• Work with Vehicle Engineering, Product Engineering, Vehicle Compliance, Product Validation, Manufacturing, Service, Quality, IT, defect investigation teams, and regional/global stakeholders to support user acceptance testing, adoption, and well-governed AI and data solutions.

Knowledge You Should Bring

• A bachelor's degree in engineering, computer science, data science, or a related technical field.
• 0-2 years of relevant experience through work, internships, co-ops, academic projects, or applied technical projects.
• Foundational understanding of AI/ML and GenAI concepts, including large language models, embeddings, retrieval, prompt patterns, and basic model evaluation.
• Awareness of responsible AI practices, including