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Applied AI Engineer I

Daimler Truck North America Portland, OR

Portland, OR

Today

$71k - $91k/yr Full time Analytics API APIs

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Job Description

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.

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