Edge AI Engineer

Bright Vision Technologies is hiring an Edge AI Engineer to design, optimize, and deploy machine learning models for resource-constrained edge devices. The role focuses on hardware-aware model optimization, cross-platform inference, secure and reliable deployment, and collaboration across hardware, firmware, and product teams.

Responsibilities:

• 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

Requirements:

• U.S. Citizens, Green Card Holders, EAD Holders, and H-1B transfer candidates
• We are unable to sponsor new H-1B visa petitions for this position
• Bachelor's or Master's degree in Computer Science, Computer Engineering, or a related field
• Eight or more years of experience in ML engineering, with significant work on edge or mobile AI
• Strong proficiency in Python and C++
• Hands-on experience with model compression, quantization, and pruning techniques
• Experience with at least one major edge inference framework
• Solid understanding of mobile and embedded hardware architectures
• Experience deploying ML models to production on mobile or embedded platforms
• Strong performance engineering and profiling skills
• Familiarity with on-device privacy and security considerations
• Strong communication and cross-functional collaboration skills
• Experience with custom NPU or DSP toolchains
• Familiarity with federated learning or on-device personalization
• Exposure to safety-critical or industrial edge deployments
• Open-source contributions to edge AI frameworks
• Experience optimizing LLMs for on-device inference