Embedded Edge AI Architect

Developing software, algorithms, and firmware for AI systems across embedded platforms (GPUs, ARM MCUs, DSPs, FPGAs) * Developing Edge AI software across devices using Core ML + Metal (Apple) and Android platforms * Deploying deterministic Edge AI pipelines on NVIDIA platforms

· 8-10+ years of experience in data engineering, telemetry systems, or applied data science within embedded, edge, or hardware-adjacent environments · Strong proficiency in Python for data processing, modeling, and pipeline development · Working knowledge of C/C++ to interface with low-level systems and firmware integration points · Proven experience designing and building telemetry pipelines, data models, and ingestion frameworks · Experience translating hardware/firmware signals into structured datasets for analytics, performance tuning, or ML use cases · Strong understanding of time-series data, signal processing, and real-time data constraints · Experience building firmware-to-application data interfaces and ingestion pathways · Ability to operate across system layers: device/edge * transport * host * data platform · Familiarity with GPU-based systems or high-performance computing environments · Strong cross-functional collaboration with firmware, hardware, and platform engineering teams · Experience designing performance characterization and optimization models that correlate telemetry, workload behavior, thermal constraints, and system utilization into actionable tuning policies, workload mappings, and system-level control decisions · Experience with edge AI / inference pipelines or GPU-accelerated workloads · Familiarity with RTOS concepts (Zephyr, FreeRTOS) - awareness vs deep specialization · Experience integrating platform management protocols (MCTP, PLDM) to enable structured telemetry, device visibility, and communication between embedded systems and host-side applications · Exposure to embedded Linux or hybrid edge/cloud architectures · Experience with data observability, data validation, and telemetry integrity frameworks · Background in hardware-adjacent environments (IoT, robotics, devices)