Edge AI Engineer

Bright Vision Technologies•Hoboken, NJ

3d•$100,000 - $155,000•Remote

About The Position

We are looking for an Edge AI Engineer to design, optimize, and deploy machine learning models that run efficiently on resource-constrained edge devices, including mobile platforms, embedded systems, and specialized accelerators. The role requires deep expertise in model compression, quantization, and hardware-aware optimization, along with strong systems engineering skills to ship reliable AI capabilities outside the data center. The ideal candidate has shipped edge AI in production environments where compute, memory, energy, and connectivity constraints fundamentally shape the engineering trade-offs.

Requirements

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 skills.
Familiarity with on-device privacy and security considerations.
Strong communication and cross-functional collaboration skills.

Nice To Haves

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.

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.