Posted today · be early
Member of Technical Staff, Applied AI
logcat.ai
Canada, India, United StatesremotePosted today
Skill Required
Machine-Learning-EngineerApplied-AI-EngineerAI-EngineerTechnical-StaffSenior-Applied-AI-EngineerStaff-Applied-ScientistStaff-Applied-AI-EngineerApplied-AI-ScientistApplied-AI-ML-ScientistApplied-AI-ML-EngineerApplied-AI-Research-ScientistApplied-AI-ResearcherTechnical LeadAIEngineeringdebuggingAndroidDatadogdesignLinuxandMachine LearningFulltime
Key highlights
- Top-of-market salary
- 10+ years of relevant engineering experience
- Comprehensive medical insurance for you and your family
- Remote with at least four hours of overlap with Pacific or IST
- Hands-on fine-tuning and distillation with open-weight models (Qwen, Llama-class)
- Inference deployment and scaling (vLLM or equivalent) on managed platforms
Role overview
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. Own the engine that turns every investigation into a compounding asset: the more the platform runs, the sharper it gets. This is applied ML systems, not research. If your goal is training foundation models from scratch and publishing, this is not the seat.
Responsibilities
- 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). It's how the team operates, not a nice-to-have.
Nice to have
- Agent or tool-use systems.
- Preference optimization (DPO/GRPO-style) and structured-output / function-calling fine-tunes.
- On-prem or airgapped model deployment.
- Retrieval over large heterogeneous corpora.
- Systems or infra background.
Benefits
- A front seat as we scale. Customer calls, the roadmap, the pipeline, how the company is actually doing.
- Top-of-market salary and founding equity. You'll own a piece of what you build.
- 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.
- Comprehensive medical insurance for you and your family.
- Start-up perks and flexible time off, the kind people actually take.
- A small team that's easy to work with. People here like solving hard problems and helping each other out.
Additional details
- 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.
- 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.
- 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.
