Machine Learning Engineer

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About Skylark Labs

At Skylark Labs, we pioneer embodied artificial intelligence that seamlessly integrates into every physical device, evolving toward true general intelligence. Our mission is to transform the world by creating adaptive AI systems that empower innovation, enhance safety, and redefine connectivity for a smarter, more sustainable future.

We are building scalable AI-driven solutions to power next-generation visual intelligence systems. Join our team of passionate engineers and researchers to solve real-world computer vision problems across domains like robotics, smart cities, manufacturing, surveillance, and public safety - all while contributing to the future of embodied AI.

Key Responsibilities

• Design, develop, and deploy robust computer vision models for object detection, classification, segmentation, pose estimation, tracking, and OCR tasks
• Handle end-to-end data management, including dataset curation, preprocessing, augmentation, and annotation for large-scale image and video data
• Implement optimization techniques such as model quantization, pruning, and knowledge distillation for efficient edge deployment
• Optimize model inference performance on both cloud and edge computing platforms
• Develop and fine-tune deep learning architectures using modern frameworks
• Integrate computer vision models into production pipelines and edge-based inference systems
• Collaborate with MLOps/DevOps and product teams to deploy CV solutions at scale
• Research and evaluate emerging techniques to continuously improve system accuracy and efficiency

Required Skills

• 2+ years of hands-on experience in computer vision and deep learning development
• Proficiency in Python with computer vision libraries and data processing tools
• Strong expertise in deep learning frameworks for training custom models and leveraging pretrained architectures
• Experience with deployment and optimization on edge computing devices
• Familiarity with hardware acceleration and model optimization tools
• Understanding of classical computer vision techniques, including image processing fundamentals
• Experience in data pipeline management for vision applications
• Proficiency with version control systems and collaborative development workflows
• Basic experience working with video streams from cameras, drones, or embedded platforms

Nice-to-Have

• Experience with model serving frameworks and inference optimization tools
• Familiarity with MLOps practices and CI/CD pipeline integration
• Knowledge of Vision-Language Models (VLMs) and multimodal AI systems for advanced visual understanding
• Knowledge of cloud AI services and edge computing platforms
• Experience with real-time object tracking algorithms
• Domain experience in smart surveillance, OCR, safety detection, or embedded CV applications