Automotive AI Rater & Evaluator

Overview

LILT is building a global network of domain experts to support high-quality AI evaluation across training, benchmarking, red-teaming, and ongoing model monitoring. We are seeking automotive professionals to contribute expert judgment to human-in-the-loop AI evaluation workflows used by leading enterprises and hyperscalers.

This role is designed for professionals who understand how automotive systems, vehicles, manufacturing processes, and mobility technologies operate in real-world environments and who can apply that expertise to evaluate, assess, and improve multilingual AI systems.

Your contribution of expertise will directly influence multilingual AI model quality, safety, and deployment readiness.

This role includes two distinct expert tracks, based on experience level and scope of responsibility.

Track A: Automotive AI Rater

Raters execute structured evaluation tasks using clearly defined rubrics and instructions.

Responsibilities

• Evaluate AI outputs related to automotive engineering, vehicles, and mobility topics

• Perform structured scoring, comparison, classification, and judgment tasks

• Assess technical accuracy, safety considerations, clarity, and alignment with automotive standards

• Identify hallucinations, unsafe guidance, incorrect specifications, or misleading automotive information

• Apply domain-specific automotive guidelines consistently across tasks

Ideal Background

• Automotive engineers, manufacturing specialists, vehicle systems professionals, or mobility practitioners

• Experience with vehicle systems, automotive manufacturing, maintenance, or mobility technologies

• Strong attention to detail and comfort working with structured evaluation criteria
  

Track B: Automotive AI Evaluator (Senior Track)

Evaluators provide higher-level domain oversight and help shape how evaluation is performed.

Responsibilities

• Validate and refine evaluation rubrics and edge-case handling

• Perform adjudication where raters disagree

• Conduct error analysis and qualitative reviews of model behavior

• Partner with LILT research, product, and customer teams on evaluation design

• Support red-teaming, safety analysis, and model readiness assessments

Ideal Background

• Senior automotive engineers, technical leads, or automotive subject matter experts

• Experience defining standards, reviewing complex edge cases, or advising on automotive safety and compliance

• Ability to clearly explain nuanced technical and safety-related tradeoffs

Evaluation Focus & Requirements

Types of AI Evaluation Work
Depending on project demands, work may include:

• Automotive engineering and mobility content evaluation

• Vehicle systems reasoning and safety assessment

• Benchmarking and comparative model analysis

• Safety-focused red-teaming and risk evaluation

• Ongoing model monitoring and regression testing

What We Look For

• Deep domain expertise in automotive engineering, manufacturing, or mobility

• Strong judgment and ability to apply criteria consistently

• Comfort working with structured evaluation workflows

• Ability to explain reasoning clearly, especially in safety-critical scenarios

• Reliability, professionalism, and respect for quality standards

Engagement Model

• Contract-based, flexible participation

• Project-based work with clear expectations and timelines

• Opportunities for recurring work based on performance and demand

• Compensation communicated upfront per project or task type
  

Why This Work Matters

Your expertise helps ensure that AI systems:

• Provide accurate and safe automotive-related information

• Align with industry standards and safety expectations

• Are trustworthy and reliable across languages

Language Requirements

• Native or professional fluency in one or more supported languages is required

• Supported languages span 30+ global languages

• Language-specific nuance is assessed through screening and task-based evaluation, not separate job descriptions

• English fluency is required for guidelines, feedback, and collaboration

AI is changing how the world communicates — and LILT is leading that transformation.

LILT's mission is to make the world's information available to everyone, no matter the language they speak. Join our global community who thrive on innovation and excellence. Our collective knowledge, uniqueness, and skills deliver multilingual AI and human-verified services to Enterprises, Governments, and AI Developers around the world.

Earn money. Have fun. Advance human knowledge. Work on diverse projects from anywhere, any time you want. Get paid quickly and fairly, and build your professional network in a supportive community—all through a streamlined application process tailored to your expertise.

Information collected and processed as part of your application process, including any job applications you choose to submit, is subject to LILT's Privacy Policy at https://lilt.com/legal/privacy.

At LILT, we are committed to a fair, inclusive, and transparent hiring process. As part of our recruitment efforts, we may use artificial intelligence (AI) and automated tools to assist in the evaluation of applications, including résumé screening, assessment scoring, and interview analysis. These tools are designed to support human decision-making and help us identify qualified candidates efficiently and objectively. All final hiring decisions are made by people. If you have any concerns, require accommodations, or would like to opt-out of the use of AI in our hiring process, please let us know at recruiting@lilt.com.

LILT is an equal opportunity employer. We extend equal opportunity to all individuals without regard to an individual’s race, religion, color, national origin, ancestry, sex, sexual orientation, gender identity, age, physical or mental disability, medical condition, genetic characteristics, veteran or marital status, pregnancy, or any other classification protected by applicable local, state or federal laws. We are committed to the principles of fair employment and the elimination of all discriminatory practices.