logcat.ai is the AI engineering layer for the operating-system stack. We build agentic systems for Linux and Android devices: we ingest the diagnostic artifacts a device emits (bugreports, logcat, dmesg, modem traces, ramdumps, perfetto, pcap, CAN) and help device makers diagnose, remediate, test, and author features across their OS stack, grounded in their own BSP. Think Datadog and Cursor combined for the OS layer. A VC-backed startup with a small, senior team — you work directly with the founders.

Build the automotive substrate adapter. Modern cockpit dumps are dual-OS on one SoC, and nobody diagnoses across that boundary well. You'll change that.

This is a scoped, deliverable-based contract with a path to a full-time Member of Technical Staff role.

Location: Remote (4h overlap with Pacific or IST; Seattle or Bengaluru on-site preferred). Contract.

The bar

• 10+ years of relevant engineering experience.

• Automotive embedded experience.

• AUTOSAR (Classic and/or Adaptive).

• CAN / CAN-FD and DBC-level fluency.

• UDS / DoIP diagnostics, SOME/IP, and ECU or domain/zonal architecture.

• DLT, with QNX a strong plus.

• Proficient in day-to-day work with CLI-based AI coding tools like Claude Code (or an equivalent). It's how the team operates, not a nice-to-have.

What you'll do

• Parse and correlate dual-OS cockpit dumps: QNX SLOG2, DLT traces, Android IVI logs, perfetto, pcap, CAN.

• Solve cross-clock-domain correlation across DLT ECU time, QNX monotonic, kernel monotonic, and Android boottime/realtime.

• Build the automotive diagnostic path: UDS, DoIP, DTCs.

Bonus

• AAOS or AAOS SDV, including VHAL.

• Hypervisor-based cockpits (QNX Hypervisor).

• Functional safety (ISO 26262) literacy.

• Vector toolchain (CANoe, CANalyzer).

In your application, tell us what you'd own here, and the hardest thing you've shipped in this domain.