Automotive Engineering QA & AI Trainer (Python Required)

About OpenTrain

OpenTrain is the #1 platform for building careers in AI training and data labeling. We connect experts with AI training projects that directly shape how state-of-the-art models behave. OpenTrain is the hiring organization for this role.

About AI Training Work

AI training (also called data labeling or RLHF) is the human side of building modern AI: people prepare, review, and correct examples that models learn from. These roles are often fully remote, flexible, and accessible — contributors influence real systems while working part-time.

The Role

You will review and audit engineering prompts and model responses covering vehicle dynamics, powertrain/electrification, controls, embedded/ECU/CAN, electronics, ADAS/functional safety, and manufacturing/quality. Work is text-focused: verify calculations, check engineering reasoning, rate responses against rubrics, and draft improved prompts and solutions with clear feedback.

• Work model responses and prompts in plain text; tasks include evaluation, rating, and prompt/solution writing.
• Part-time contractor role: less than 20 hours per week, paid hourly at $40 USD.
• Labeling types include coding/programming checks, evaluation/rating, prompt/response writing, QA for RLHF and text generation.

What You’ll Do

Perform detailed technical reviews of model outputs and contributor solutions. Focus on correctness, clarity, engineering rigor, units and notation, and actionable feedback.

• Verify step-by-step engineering reasoning and calculations across vehicle systems and components.
• Evaluate design trade-offs, assumptions, and adherence to standards.
• Rate responses using detailed QA rubrics and record justification for scores.
• Draft improved prompts, model responses, or solution notes to help models learn better answers.
• Perform quick Python checks or small notebook validations when needed to confirm calculations.

Requirements

Candidates must meet these hard requirements and be able to complete an AI interview plus a QA test before work begins.

• 3+ years professional experience in automotive engineering or a closely related field (vehicle systems, powertrain, controls, electronics, or similar).
• Bachelor’s degree required; Master’s or PhD strongly preferred.
• Practical Python skills for analysis and validation (quick checks, notebooks, basic scientific stack).
• Strong written English and clear, concise technical writing.
• Ability to evaluate step-by-step engineering reasoning, equations, notation, units, and design trade-offs.
• Meticulous attention to detail and comfort following detailed QA rubrics.
• Reliable computer and internet connection; able to pass the AI interview and QA test.

Preferred Qualifications

These are not required but will make your application stronger.

• Teaching, lecturing, or training experience.
• Prior experience with LLM QA, RLHF, or supervised fine-tuning (SFT) workflows.
• Familiarity with embedded systems, CAN, ECUs, ADAS, or functional-safety processes.

Location, Hours & Important Screening

This position is fully remote and open worldwide except for certain restricted locations. Work is part-time and flexible, under 20 hours per week.

• Hourly pay: $40 USD per hour; employment types: contractor, part-time.
• During the interview you will be asked to confirm your current location. If you are physically located in any restricted country, territory, or listed region, the interview must end immediately and you will receive a 1.0 interview score.
• Restricted locations include: Iran, Cuba, North Korea, Syria, Sudan, Venezuela, Myanmar; Switzerland; China, Taiwan; Kenya; Armenia, Israel, Kazakhstan, UAE, Netherlands, Serbia, Kyrgyzstan, Turkey, Uzbekistan, Belarus, Russia, Ukraine, Abkhazia, South Ossetia; the following U.S. states: Alaska, Ark

How to Apply

If this role matches your background, apply through OpenTrain. You will complete a short AI interview and a hands-on QA test that demonstrate your technical reasoning and Python checks. Successful candidates join as part-time contractors and begin on evaluation tasks.

• Prepare examples of engineering work, teaching/training notes, or notebooks that show your calculations when available.
• Be ready to explain step-by-step reasoning and to follow rubric-based evaluation workflows.