Edge AI Engineer
Bright Vision Technologies is a technology consulting and software development company delivering cloud, AI, data, and enterprise solutions across the United States. This is a fantastic opportunity to join an established and well-respected organization offering tremendous career growth potential.
Location: 100% Remote (U.S.)
Position Type: Full-time, Direct W2
Salary Range: $70,000–$100,000 Annually
Sponsorship: U.S. Citizens, Green Card Holders, EAD Holders, and H-1B transfer candidates are encouraged to apply.
About the role
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.
Responsibilities
- Design, optimize, and deploy machine learning models for edge devices
- Apply model compression, quantization, and pruning techniques
- Deploy ML models to production on mobile or embedded platforms
- Perform performance engineering and profiling
- Address on-device privacy and security considerations
- Collaborate cross-functionally to ship reliable AI capabilities
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
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
Pay
$70,000–$100,000 Annually