AI Vision Engineer

Nexxa.AI•Sunnyvale, CA

About The Position

Nexxa.ai is building artificial super intelligence for heavy industries — enabling machines, systems and operations to think, decide and act autonomously across manufacturing, large-scale infrastructure, logistics and legacy environments. Our mission is to translate deep technical breakthroughs into operational reality, solving some of the hardest systems-level problems in industry. We are looking for an AI Vision Engineer to help design, build, and deploy next-generation computer vision systems across a diverse set of real-world industrial applications. This role is ideal for someone with a strong foundation in Computer Vision and Machine Learning who is excited about working across the full vision stack, including classical and deep-learning-based CV, vision-language models (VLMs), multimodal reasoning, and real-time inference at the edge and in the cloud. You will work closely with our AI and engineering teams to develop production-ready vision solutions, improve model performance, and help shape the next generation of intelligent visual systems.

Requirements

Bachelor's degree in Computer Science, Computer Engineering, Electrical Engineering, Mathematics, Statistics, Artificial Intelligence, or a related technical field, or equivalent practical experience.
3+ years of industry experience in Computer Vision, Machine Learning, Applied AI, or related fields.
Demonstrated experience independently owning and delivering computer vision projects from concept to production.
Strong programming skills in Python.
Hands-on experience with PyTorch and modern deep learning workflows.
Experience developing and deploying computer vision models (detection, segmentation, classification, OCR) in production environments.
Experience working with image and video processing libraries such as OpenCV.
Experience with common CV/detection frameworks (e.g., YOLO, Detectron2, MMDetection, or similar).
Experience working with VLMs, multimodal models, or Generative AI applications.
Strong understanding of machine learning fundamentals, model evaluation, experimentation, and model deployment.
Experience working with Hugging Face Transformers and open-source AI ecosystems.
Familiarity with data annotation workflows, dataset curation, hyperparameter optimization, and inference optimization.
Experience building production-grade software and AI systems.
Strong analytical and problem-solving skills.
Excellent communication and collaboration skills.

Nice To Haves

Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Computer Vision, or a related field.
Experience with OCR, document understanding, or visual reasoning systems.
Experience with Vision-Language Models (VLMs) and multimodal AI applications.
Familiarity with 3D vision, SLAM, or sensor fusion (e.g., camera + LiDAR) for industrial or robotics applications.
Experience with real-time inference optimization (TensorRT, ONNX Runtime, quantization, pruning).
Experience deploying models on edge hardware (e.g., NVIDIA Jetson, embedded systems).
Experience with LangChain, LangGraph, or agentic AI frameworks combining vision and language models.
Experience with vector databases and visual/semantic retrieval systems.
Experience deploying AI systems on AWS, GCP, or other cloud platforms.
Experience with Docker, Kubernetes, and MLOps workflows.
Experience with PostgreSQL and large-scale data systems.
Experience with model serving, distributed training, and inference optimization at scale.
Contributions to open-source projects, technical blogs, research publications, Kaggle competitions, or other demonstrable CV/AI work.
Experience working in startup or high-growth environments.

Responsibilities

Design, train, evaluate, and deploy computer vision models for real-world industrial applications.
Build and optimize CV pipelines for tasks such as object detection, segmentation, classification, OCR, tracking, and visual understanding.
Develop and fine-tune vision-language models (VLMs) for multimodal reasoning, visual question answering, and document understanding.
Design and optimize real-time inference pipelines for deployment on edge devices and in the cloud.
Build scalable data pipelines for image and video collection, annotation, augmentation, training, and evaluation.
Fine-tune and evaluate open-source vision and multimodal foundation models using modern training and inference frameworks.
Develop robust evaluation frameworks and benchmarks to measure model accuracy, robustness, latency, and business impact.
Optimize models for production constraints, including quantization, pruning, and hardware-accelerated inference.
Collaborate with product, engineering, and research teams to translate business requirements into technical vision solutions.
Contribute to architecture decisions, technical design reviews, and AI/CV best practices.
Stay current with the latest advancements in computer vision, multimodal AI, and autonomous systems.