Jobs and Bounties

We’re building an AI system that automatically recognizes and maps CAN signals in cars to speed up porting vehicles to openpilot-like autonomy stacks.

Instead of manually reverse-engineering every car, we collect CAN logs, train models, and let AI suggest signals (speed, steering, brake, gas, etc.) that engineers only confirm.

This is not a CRUD web app.
This is a real data/ML system handling large logs, background jobs, inference, and user workflows.

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What you’ll build

You will design and implement the backend for:

• uploading and storing large CAN logs
• parsing and preprocessing data
• background processing pipelines
• model inference APIs (serving predictions)
• labeling workflows (human-in-the-loop training)
• dataset/version management
• integration with our online Cabana-like web tool
• exporting signal definitions (DBC/openDBC style)

In short:
You build the data + API + ML infrastructure layer.

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Tech stack (flexible but preferred)

• Python (FastAPI) or Node.js (NestJS)
• PostgreSQL
• S3/MinIO object storage
• background jobs (Celery / Redis / queues)
• Docker
• Linux

Nice to have:

• WebSockets
• ClickHouse / time-series DB
• basic MLOps knowledge
• experience with large file processing
• experience with data pipelines

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Requirements

Must have:

• solid backend fundamentals
• API design skills
• database knowledge (SQL, schema design)
• async/background processing
• Docker
• ability to work independently

Good fit if you:

• like systems engineering more than frontend/UI
• enjoy working with real hardware/data
• want to build something used in real vehicles
• prefer practical engineering over “AI hype”

Not required:

• automotive knowledge
• CAN knowledge
• deep ML knowledge

You will learn those on the job.

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What makes this interesting

This is NOT:

• a typical web shop or dashboard

This IS:

• automotive reverse engineering
• real CAN buses and hardware
• ML + data engineering
• building tools used to port real cars
• solving non-trivial technical problems

You’ll work closely with:

• ML engineer
• CAN expert
• embedded/hardware team

Very hands-on, very practical.

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We offer

• part-time or full-time
• student-friendly
• flexible hours
• remote or hybrid
• real ownership (not tickets from Jira hell)
• direct impact on product
• modern stack, no legacy corporate nonsense

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Nice bonus if you have

• built any backend projects yourself
• worked with data pipelines
• played with Raspberry Pi / hardware / robotics
• contributed to open source
• built tools, not just websites

Part-time / Student / Junior–Mid | Automotive CAN | Reverse Engineering | ADAS

We are building an AI-assisted system that automatically recognizes CAN signals and significantly reduces the time required to add new vehicles to openpilot.

As part of this project, we are developing an online version of Cabana and training machine learning models on real-world CAN logs.
Your work will directly define the ground truth that the AI learns from.

This is a hands-on reverse engineering role focused on real vehicles and real data — not simulations or theory.

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What you will do

• Capture and analyze raw CAN logs from vehicles
• Reverse engineer CAN messages and signals (speed, steering, brake, gear, blinkers, cruise, etc.)
• Identify counters, checksums, multiplexed signals and message structures
• Create and maintain DBC definitions
• Port new vehicles to openpilot
• Prepare labeled datasets for ML training
• Design heuristics and validation rules for automatic signal detection
• Work closely with the ML and backend engineers

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Requirements

• Practical knowledge of CAN bus (analysis, not only theory)
• Experience with CAN sniffing/logging tools (Cabana, SavvyCAN, or similar)
• Ability to read raw frames and manually extract signals
• Basic Python for scripting and data analysis
• Strong analytical and problem-solving skills
• Comfortable working with messy real-world data

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Nice to have (not required)

• Experience with openpilot or similar ADAS systems
• UDS / ISO-TP diagnostics
• Embedded or automotive electronics projects
• Reverse engineering or car hacking experience
• Linux environment

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Who this role is for

• Electronics / robotics / embedded / automotive students
• Engineers who enjoy reverse engineering and low-level systems
• Hobbyists who like analyzing vehicle networks

If you like figuring out what unknown bytes mean and discovering how cars really communicate, this role fits you.

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What we offer

• Work with real vehicles and real CAN data
• Build tools that are immediately used in production
• Direct impact on an AI system that automates vehicle porting
• Flexible hours (student-friendly)
• Small engineering team, no corporate overhead