Senior Computer Vision/AI Engineer

BrightAI is a high-growth Physical-AI company transforming how businesses interact with the physical world through intelligent automation. We are building a cutting-edge AI platform that processes visual, spatial, and temporal data across billions of real-world events—from edge devices and mobile sensors to large-scale cloud systems.

Our team includes world-class engineers and researchers from companies like Microsoft, Amazon, Tesla, and Meta. We are now hiring a Senior Computer Vision / AI Engineer to help shape the future of real-time visual intelligence and deploy next-generation AI systems at scale.

This is an incredible opportunity to work on cutting-edge AI systems that are redefining physical infrastructure industries. You will work on applied research and production-level ML/AI systems that power real-time decision-making across devices and services. This role requires deep expertise in computer vision, machine learning, and AI system design, with a strong focus on solving real-world problems. You'll work alongside world-class engineers and product leaders to define, build, and ship state-of-the-art AI features that are embedded into our platform.

Responsibilities:

Lead the full lifecycle of Computer Vision and ML model development — from data collection and labeling through deployment and monitoring in production environments.
Research and implement deep learning models for computer vision tasks including detection, segmentation, classification, tracking, and real-time inference.
Drive CV projects from prototyping to production, in alignment with product and platform goals.
Collaborate with product, hardware, and cloud teams to design end-to-end intelligent features across the stack.
Architect technically robust, scalable, and reliable AI systems in collaboration with cross-functional teams.
Solve complex physical-world challenges through structured experimentation and performance optimization.
Prioritize and manage multiple initiatives to ensure model performance, reliability, and compliance.
Stay at the forefront of ML/AI and foundational models, integrate key innovations into the product roadmap.
Evaluate emerging AI/ML trends and apply them to enable transformative infrastructure automation solutions.

Required Skills & Expertise:

7+ years of experience in computer vision and ML, with deep expertise in applied deep learning.
Proven ability to deliver production-grade AI/ML solutions in fast-paced, real-world environments.
Full-stack ML development lifecycle experience: data labeling and curation, model training, evaluation, optimization, and deployment.
Hands-on technical skills with DL frameworks (PyTorch or TensorFlow) and string programming skills in Python (C++ is a plus).
Hands-on experience with CNNs, YOLO, Vision Transformers, model compression, and real-time inference optimization.
Hands-on experience deploying models in cloud platforms (AWS/GCP/Azure) and edge devices using TensorRT, ONNX, or TFLite.
Strong problem solving and analytical skills, with the ability to convert ambiguity into actionable insights.
Excellent team work and collaboration skills; ability to work cross-functionally with software, hardware, and product teams.
Effective communicator, capable of conveying complex technical concepts to both technical and non-technical stakeholders.
Self-motivated, proactive, and thrives in a dynamic, fast-paced environment.

Educational Background:

PhD in Electrical Eng., Computer Science, or a related field with a focus on ML/AI for Computer Vision.
Demonstrated research experience in CV with a strong record of publications and/or patents.
Experience applying vision-based AI to real-time IoT systems or edge intelligence platforms.

Bonus Qualifications:

Applied experience in CV for surveillance, physical-world perception problems, remote sensing, or structural health monitoring.
Proficiency in Linux/Ubuntu environments; scripting and tooling around data and deployment workflows
Familiarity with Agile development practices and tools such JIRA, Git, Confluence.
Experience with embedded systems, Docker containers, or Linux-based deployment pipelines
Prior experience in startup or high-growth environments building zero-to-one AI solutions