On-Device AI Engineer

2 weeks ago


kharagpur, India beBeeDevice Full time

Job Title: Device Intelligence Engineer Description: The role involves developing and optimizing AI models for on-device deployment, focusing on real-time speech, local understanding, and smooth synthesis while respecting battery and memory budgets. Responsibilities: Quantize, distill, and compile models (VAD/ASR/NLU/TTS/Small-LLMs) for CoreML/Metal with streaming pipelines. Build offline fallbacks and clever on-device first, cloud-when-necessary routing with clear consent gates. Instrument latency, accuracy, power, memory, and binary size; land targets and keep them there. Partner with iOS to ship low-friction voice UX (barge-in, barge-out, interruption repair). Collaborate with Orchestration to design tool contracts that exploit edge capabilities (wake-words, local context, cache). Maintain a reproducible toolchain (PyTorch → ONNX → CoreML) with deterministic builds and golden benchmarks. Key Performance Indicators (KPIs): 30 days: Baseline ASR+TTS pipeline running locally with streaming; p95 end-to-end voice turnaround 60 days: Add wake-word, VAD, and small-footprint NLU; offline short-task mode (notes, reminders, follow-ups); accuracy/latency dashboards shipped 90 days: >85% task-success on the on-device golden set; stable MOS for TTS; memory Skills and Qualifications: Shipping CoreML/Metal inference with real-time audio and profiling traces. Experience in quantization (INT8/INT4), distillation, or sparsity, with ability to explain trade-offs. Comfort with Swift/C++ bridging, AVAudioEngine, and low-latency I/O. Taste for graceful degradation and offline safety. Benefits: Opportunity to work on cutting-edge AI projects. Collaborative and dynamic work environment. Professional growth and development opportunities.


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