Edge AI Engineer

Bright Vision Technologies 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.

As we continue to grow, we’re looking for a skilled Edge AI Engineer to join our dynamic team and contribute to our mission of transforming business processes through technology.

This is a fantastic opportunity to join an established and well‑respected organization offering tremendous career growth potential.

Job Title: Edge AI Engineer

Location: 100% Remote (Continental United States)

Position Type: In‑house Bright Vision Technologies SOW engagement (no third‑party client or vendor)

Experience: 6+ years

Sponsorship: No new H1B sponsorship available. H1B transfers welcomed for qualified candidates.

Employment Type: Full‑time, direct W2 with Bright Vision Technologies (no C2C, no 1099, no third‑party)

Engagement: Long‑term, multi‑year, aligned to the Bright Vision SOW delivery roadmap

Compensation: Competitive base salary commensurate with experience, plus benefits.

Employment Terms & Visa Policy

This is a 100% remote, full‑time, direct W2 position with Bright Vision Technologies. This role is part of Bright Vision Technologies’ in‑house Statement of Work (SOW) engagement. The client, end customer, and employer for this position is Bright Vision Technologies — there is no third‑party client, vendor, or implementation partner involved. We do not engage in C2C, 1099, or third‑party arrangements for this role.

BUT STRICTLY NO C2C/1099/3RD PARTY COMPANIES. ALL OUR ROLES ARE W2 AND NO 3RD PARTY BROKERING PLEASE. Candidates must be willing to work directly as a full‑time W2 employee of Bright Vision Technologies and contribute to our in‑house SOW deliverables. No new H1B sponsorship is available for this role. However, candidates who are currently on a valid H1B visa and require a transfer are welcome to apply.

We will support H1B transfers for qualified candidates.

For every role, a technical coding assessment is mandatory. Please apply only if you are confident in your technical abilities and hands‑on experience.

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.

• 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 Tensor Flow 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…