Automotive CAN Signal Capturing Dataset Generation

About OpenTrain

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About AI training and this kind of work

AI training (also called data labeling or annotation) is the human side of building machine intelligence. People prepare and review examples that models learn from — in this project, that means mapping natural-language use case descriptions to the vehicle CAN signals needed to satisfy them.

This role directly shapes how automotive systems and analytics interpret vehicle data: accurate mappings help teams collect the right signals for diagnostics, telematics, insurance models, and remote support.

The role

You will use our internal proprietary tooling to generate a training dataset that maps natural-language use-case descriptions to lists of CAN signals required to address those use cases.

Example mapping: "I need to understand how driver uses the sunroof" -> [signal_for_sunroof, signal_for_driving, signal_for_speed, signal_for_ac]. The dataset is document-based and labeled as DATA_COLLECTION.

• Employment type: Contractor (remote, worldwide).
• Data type: DOCUMENT; Label type: DATA_COLLECTION.
• Labeling software: INTERNAL_PROPRIETARY_TOOLING.

What you'll do

Follow project guidelines to convert natural-language use cases into comprehensive lists of CAN signals using the supplied internal tooling. Work carefully to ensure mappings are complete and relevant to each use case.

• Interpret each use-case description and identify the CAN signals required to address it.
• Enter mappings into the internal proprietary tooling and follow the project's metadata and formatting rules.
• Apply automotive domain knowledge to include signals that matter for diagnostics, telematics, driver behavior, and related analyses.

Requirements

A subject-matter expert in automotive CAN signaling is mandatory. This is an intermediate-level role that expects hands-on familiarity with vehicle signals and their meanings.

• Subject-matter: automotive, CAN signal — subject expert is mandatory.
• Experience level: Intermediate.
• Employment: Contractor, remote (worldwide).

Preferred experience

The following prior experiences are preferred and will help you be successful on this project. They are not listed as strict requirements but will be valuable.

• Worked on vehicle diagnostics that identify which signals are important for troubleshooting vehicle problems.
• Worked on vehicle insurance projects that use driving data to determine insurance premiums.
• Worked on remote support that remotely reads vehicle signal data to diagnose problems.
• Worked with data science teams at OEMs (original equipment manufacturers).

Compensation, timeline, and how to apply

This is a fixed-price contract: USD 2,000 total for the agreed scope of work. Detailed schedule, milestones, and delivery instructions will be provided during onboarding.

To apply, create a free OpenTrain account and submit your application; the process usually takes only a few minutes. If selected, you'll receive project guidelines and access to the internal tooling.

• Payment type: FIXED_PRICE — USD 2000.
• Worldwide applicants accepted; no specific language or country restrictions listed.
• Application: apply via OpenTrain (account creation is free).