Senior AI Engineer (Edge Dialog Systems)

Senior AI Engineer – Edge Dialog Systems

BrightAI is a high-growth Physical AI company transforming how businesses interact with the physical world through intelligent automation. Our AI platform processes visual, spatial, and temporal data from billions of real-world events—captured across edge devices, mobile sensors, and cloud infrastructure—to enable intelligent decision-making at scale.

We are now hiring a Sr. AI Engineer – Edge Dialog Systems to own and evolve the on-device conversational AI that powers our industrial safety wearable. The assistant guides field technicians through safety-critical procedures by voice, and it runs on the device itself, under hard latency, memory, and thermal budgets, with deterministic safeguards that take precedence over model output.

This is a systems role rather than a prompt-and-retrieve role. The competencies of a strong LLM and RAG engineer are needed for the position, but the work itself is dialog systems engineering at the edge, where most conversational turns are resolved by deterministic and embedding-based methods, and the language model is the last resort rather than the first move.

You will work at the intersection of natural language understanding (NLU), small language models (SLM), and embedded software, building a system in which a wrong answer is a safety concern and not merely a quality issue.

Responsibilities

Own the on-device dialog pipeline end to end: intent routing, hybrid intent classification (pattern matching combined with embedding similarity and out-of-domain detection), text normalization for noisy speech input, and the multi-step guided-procedure engine.

Maintain and extend the deterministic safety layer that wraps the language model—confirmation and echo-back gating, criticality tagging, negation handling—so that a misheard answer on a safety-critical step cannot pass silently.

Run SLM inference on-device under memory, computational complexity, and latency budgets, and reduce the per-turn inference cost through model selection, quantization, and runtime optimization.

Preserve and extend the zero-shot configuration model, in which new device commands and customer procedures are authored as data rather than released as code, so that a new customer can be onboarded in hours rather than weeks.

Coordinate the device deployment pipeline with the edge team 

Maintain the API contract with on-device voice pipeline & its speech-to-text (STT) stack.

Define and run on-device benchmarks: latency, accuracy, and false-accept/reject rates on safety-critical steps; use measurements to drive engineering decisions.

Build and maintain golden datasets and a non-regression suite, and use them as the release gate as the command and procedure catalogs grow.

Lead the migration from zero-shot to fine-tuned on-device models in order to reduce latency, without reintroducing a per-customer retraining burden.

Collaborate with product, firmware, and cloud teams, and bring new capabilities online, including additional languages, device commands, and  guided workflows.

Educational Background

Trained in AI, Machine Learning, Electrical/Computer Engineering, or a related field, with specialization in NLP, speech, or deep learning; or equivalent industry experience delivering production conversational AI systems.

Applied background in NLU, dialog systems, or on-device machine learning.

Required Skills & Expertise

LLM and retrieval foundation — the baseline for this role

5+ years of experience in ML/AI, with a strong focus on NLP, LLMs, or conversational AI.

Strong applied experience with LLMs: prompting, structured output, tool and function calling, evaluation, and retrieval-augmented generation (RAG) – together with the judgment to recognize when a model should not be used at all.

Solid command of embeddings and semantic similarity (e.g., cosine similarity, centroid versus maximum-similarity strategies, threshold tuning, and out-of-domain detec …