Applied AI Engineer I

Join DTNA’s Engineering Quality, Safety and Compliance (EQSC) team to support governed data practices and AI-agent workflows.

Responsibilities

• Support governed data pipelines using Snowflake-enabled datasets, including preparation, cleaning, validation, and connection of 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 to 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.
• Help prepare approved standards, process guidance, historical examples, compliance references, investigation learnings, and engineering knowledge content for use in AI-assisted workflows and evaluation datasets.
• Support testing, validation, and deployment of AI-agent capabilities using approved enterprise platforms and Snowflake-enabled data assets, including Microsoft 365 Copilot / Copilot Studio, APIs, and related tools.
• Track and capture data-quality issues, manual handoffs, duplicated steps, user pain points, pilot feedback, and improvement ideas in issue-tracking or backlog tools.
• Support analysis across connected engineering, compliance, investigation, manufacturing, service, warranty, and field data to improve risk assessment, product-quality decisions, corrective-action follow-up, and service diagnostics.
• Help measure AI-agent output quality using evaluation datasets and regression testing, including grounding checks, stress testing, and hallucination-reduction reviews (accuracy, efficiency, and token usage; user feedback).
• 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 platforms.
• Coordinate 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.

Requirements

• Bachelor’s degree in engineering, computer science, data science, or a related technical field.
• 0–2 years relevant experience via work, internships, co-ops, academic projects, or applied technical projects.
• Foundational understanding of AI/ML and GenAI, including large language models, embeddings, retrieval, prompt patterns, and basic model evaluation.
• Awareness of responsible AI, including grounding, hallucination reduction, privacy, access control, bias awareness, and human review for high-impact engineering decisions.
• Basic experience preparing, cleaning, validating, joining, and documenting datasets for analytics, automation, or AI-assisted workflows.
• Working knowledge of SQL, Python, REST APIs, and enterprise data-platform concepts, including Snowflake or similar environments.
• Familiarity with basic software-development practices such as version control, configuration tracking, code review, testing discipline, and clear technical documentation.
• Evaluation and regression-testing mindset: create test cases, compare expected vs. actual results, document limitations, and support issue resolution.
• Familiarity with collaboration/documentation/issue-tracking tools such as Jira, Azure DevOps, Confluence, SharePoint, or similar platforms.
• Basic awareness of automotive/engineering quality, product development, compliance, manufacturing, warranty, service, or field-quality workflows.
• Ability to communicate clearly, collaborate cross-functionally, learn quickly, ask good questions, and manage multiple tasks with guidance.

Technologies

• Snowflake
• SQL
• Python
• REST APIs
• Microsoft 365 Copilot
• Copilot Studio
• APIs
• Confluence
• SharePoint
• Jira
• Azure DevOps
• Power BI
• Excel
• Power Platform

Benefits

• 401k company contribution with company match up to 8%
• 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
• Volunteer paid time off
• Short-term and long-term disability plans

Additional Information

• Hybrid schedule: 4 days per week in-office / 1 day remote
• Location: Portland, OR (hybrid)
• Relocation assistance is not available
• Not open for Visa sponsorship or to existing Visa holders
• Must be legally authorized to work permanently in the country where the position is located at the time of application
• Final candidate must successfully complete a criminal background check
• Final candidate may be required to complete a pre-employment drug screen
• Contractors/professional services/contingent workers should confirm eligibility with their local agency for FTE positions
• EEO - Disabled/Veterans

Posting Information

DTNA provides a scheduled posting end date to support application planning. The date may change, and postings may be extended or removed earlier than expected.

What You Drive at DTNA

• Support governed data pipelines (Snowflake-enabled datasets) by preparing, cleaning, validating, and connecting 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 to improve traceability, reliability, and readiness for analytics and AI-assisted workflows.
• Help configure AI-agent workflows, retrieval patterns, prompt examples, test cases, and deployment-support materials under senior guidance.
• Prepare approved standards, process guidance, historical examples, compliance references, investigation learnings, and engineering knowledge content for AI-assisted workflows and evaluation datasets.
• Support testing, validation, and deployment of AI-agent capabilities using enterprise platforms, Snowflake-enabled data assets, Microsoft 365 Copilot / Copilot Studio, APIs, and related tools.
• Capture data-quality issues and improvement ideas in issue-tracking or backlog tools to support practical workflow changes.
• Support analysis across engineering, compliance, investigations, manufacturing, service, warranty, and field data for risk assessment, product-quality decisions, corrective-action follow-up, and service diagnostics.
• Help measure AI-agent output quality and efficiency using evaluation datasets, regression testing, grounding checks, stress testing, and hallucination-reduction reviews.
• Create and maintain implementation notes, configuration change logs, user guidance, training aids, data definitions, known limitations, and adoption content in Confluence, SharePoint, and similar platforms.
• Work with cross-functional teams to support user acceptance testing, adoption, and well-governed AI and data solutions.

Exceptional Candidates Might Have

• Applied project, internship, co-op, capstone, or portfolio experience producing a working prototype, workflow, dashboard, or documented solution.
• Hands-on exposure to AI-enabled workflows, custom AI agents, retrieval-augmented generation, vector search, embeddings, prompt engineering, or agent evaluation.
• Experience with enterprise data platforms or business systems such as Snowflake, Dataverse, SAP, SharePoint, Power Platform, or similar environments.
• Experience building Power BI, Excel, Python, or similar dashboards/reports for usage, quality, adoption, workflow status, or data-quality metrics.
• Exposure to automotive, manufacturing, warranty, service, aftermarket, vehicle compliance, defect investigation, or field-quality data and related signals for product-quality decisions.
• Experience documenting requirements, test results, defects, user feedback, known limitations, or adoption materials in Jira, Azure DevOps, Confluence, SharePoint, or similar tools.
• Exposure to structured problem-solving, quality improvement, or engineering root-cause analysis methods.

Compensation: USD 71,000 - 91,000 per year.