Edge AI Engineer at Bright Vision Technologies

Frisco, Texas, United States -

Full Time

Start Date

Immediate

Expiry Date

15 Aug, 26

Salary

0.0

Posted On

17 May, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Yes

Skills

Edge AI, Model Compression, Quantization, Pruning, Python, C++, TensorFlow Lite, ONNX Runtime, Core ML, NPU Optimization, Embedded Systems, Performance Profiling, Systems Engineering, Hardware-Aware Optimization, Model Distillation, Cross-platform Inference

Industry

Information Technology & Services

Description

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. Edge AI Engineer 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. Job Summary 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. Key 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. Required 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 skills. Familiarity with on-device privacy and security considerations. Strong communication and cross-functional collaboration skills. Preferred Qualifications 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. How to Apply Would you like to know more about this opportunity? For immediate consideration, please send your resume to harry@bvteck.com or contact us at (908) 676-4399. Learn more about Bright Vision Technologies at www.bvteck.com. We recognize that our people are our strength, and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants’ and employees’ religious practices and beliefs, as well as mental health or physical disability needs. Bright Vision Technologies is an Equal Opportunity Employer, including Disability/Veterans. Position offered by “No Fee Agency.” Equal Employment Opportunity (EEO) Statement Bright Vision Technologies (BV Teck) is committed to equal employment opportunity (EEO) for all employees and applicants without regard to race, color, religion, sex, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, veteran status, or any other protected status as defined by applicable federal, state, or local laws. This commitment extends to all aspects of employment, including recruitment, hiring, training, compensation, promotion, transfer, leaves of absence, termination, layoffs, and recall. BV Teck expressly prohibits any form of workplace harassment or discrimination. Any improper interference with employees' ability to perform their job duties may result in disciplinary action up to and including termination of employment.

Responsibilities

Design and deploy optimized machine learning models for resource-constrained edge devices and embedded systems. Implement model compression techniques and build cross-platform inference runtimes to ensure efficiency in latency and energy consumption.