Member of Technical Staff, Applied AI

🕒 July 27

🇮🇳 India – Remote

⏰ Full Time

🔴 Lead

🖥 Software Engineer

👻 Ghost score 50%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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logcat.ai

WebsiteLinkedInAll Job Openings

2 - 10 employees

🤖 Artificial Intelligence

🤝 B2B

🚘 Automotive

Artificial Intelligence • B2B • Automotive

logcat. ai is an AI-driven engineering platform that automates cross-layer root-cause analysis and remediation for device operating stacks (Android, Yocto/Linux and other embedded OSes). Its Diagnose product ingests bugreports, logcat, dmesg, modem traces, CAN captures and other device signals, correlates events across kernel, framework, modem and bus, and produces cited, auditable investigations and proposed patches (remediation in research preview). The company targets device engineering teams at mobile OEMs/ODMs, Tier‑1 suppliers, chipset/modem vendors and embedded-silicon customers, and offers cloud, dedicated-instance, and self-hosted BYOC deployments with strict data controls and auditability. Key capabilities include native parsers for many signal formats, multi-file delta comparisons, hypothesis-driven reasoning with line-level citations, and enterprise compliance features.

📋 Description

• Instrument the product so every investigation, especially human-corrected ones, becomes structured training data. • Build the eval harness for diagnostic accuracy. Root-cause correctness has to be measured, not eyeballed. • Distill expensive deep-investigation runs into small, fine-tuned models that hold the quality bar at a fraction of the cost. • Deploy inference, including on-prem and airgapped SLMs for customers whose logs cannot leave their environment.

🎯 Requirements

• 10+ years of relevant engineering experience. • Production ML systems experience end to end: data pipelines, fine-tuning, eval, deployment. • Hands-on fine-tuning and distillation with open-weight models (Qwen, Llama-class). • Eval harness design for correctness-sensitive tasks, including trajectory-level evals for agentic and tool-use systems. • Inference deployment and scaling (vLLM or equivalent) on managed platforms. • Proficient in day-to-day work with CLI-based AI coding tools like Claude Code (or an equivalent).

🏖️ Benefits

• Remote (4h overlap with Pacific or IST; Seattle or Bengaluru on-site preferred)