Edge AI Engineer

Job summary

Bri*** is a forward-thinking software development company dedicated to building innovative solutions that help businesses automate and optimize their operations. We leverage cutting-edge technologies to create scalable, secure, and user-friendly applications.

Benefits

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Computer Engineering, or a related field
• Six 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
• 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

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

Skills

Degrees

Industry

Company size