Contract Engineer, Automotive

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.

We recently raised a $2.55M pre-seed. The product is scaling and the customer list is growing. The team is still small, so you'll own what you build.

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.

A note on location

Remote, with at least four hours of overlap with Pacific or IST. We don't track hours. We ask for the overlap because a lot of this work is debugging together in real time, and that doesn't work across a twelve-hour gap. If you're in Seattle or Bengaluru, we'd like to meet in person now and then. It isn't a requirement.

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).

What we offer you

• A front seat as we scale. Customer calls, the roadmap, the pipeline, how the company is actually doing.
• The AI tools you need. We use AI across writing code, reviewing it, research, ops, and internal tooling. If something would help you work better, we'll get it.
• Early ownership, and full visibility. We move fast, and you'll help decide where.
• A small team that's easy to work with. People here like solving hard problems and helping each other out.

What to expect after you apply

• Screening call with Head of People & Business Operations
• Technical conversation with Co-founder/ Head of Engineering
• Technical exercise with Co-founder/ Head of Engineering
• Final conversation with CEO
• References, then offer

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

Originally posted on Himalayas