Edge AI Engineer

Requirements

• Design and implement edge AI solutions optimized for diverse hardware including mobile SoCs, NPUs, and embedded accelerators.
• Apply quantization, pruning, distillation, and architectural optimization to fit models within edge constraints.
• Tune model performance for latency, energy efficiency, and memory footprint on target hardware.
• Build cross-platform inference runtimes leveraging frameworks such as TensorFlow Lite, ONNX Runtime, and Core ML.
• Optimize models for specific accelerator backends including DSPs, NPUs, and mobile GPUs.
• Implement on-device model update, versioning, and rollback workflows that allow safe staged rollouts to large device populations and rapid recovery if a model release behaves unexpectedly in the field.
• Design hybrid edge-cloud architectures that gracefully degrade based on connectivity and device capability.
• Build telemetry pipelines that respect privacy while enabling continuous improvement.
• Collaborate with hardware, firmware, and product teams to align AI capabilities with device constraints.
• Implement secure execution paths, model protection, and integrity verification on edge devices.
• Develop benchmarking suites that characterize accuracy, latency, and energy trade-offs across devices.
• Drive responsible AI considerations including on-device privacy and bias evaluation.
• Maintain comprehensive, current technical documentation — including architecture diagrams, design decisions, configuration references, runbooks, and operational procedures — so that the system remains supportable, auditable, and easy to onboard new engineers onto over time.
• Stay current with edge AI hardware and software developments, regularly review release notes and community discussions, and translate noteworthy advances into concrete recommendations and adoption proposals for the team.

Benefits

• Generous Paid Time Off
• 401k Matching
• Retirement Plan