job openings

Machine Learning Engineer(s)

Vimaan is looking to onboard multiple Machine Leaning Engineers in Bengaluru, India to drive the development of computer vision and machine learning algorithms to power our cutting-edge wall-to- wall warehouse inventory tracking and verification platform. This is a unique opportunity to exploit a treasure cove of unseen real-world data coming from a multi-camera perception system and develop
large scale computer vision and deep learning models to build a product that creates a disproportionate value for warehouse industry. The role involves hands on CV/ML software development and deployment – from understanding the product requirements, defining Computer Vision functional specs to designing, developing, and deploying CV/ML models in production at scale.

Machine Learning Engineer Qualifications

The ideal candidate has the following attributes:
• MS in computer vision, machine learning, AI, applied mathematics, data science, or related technical fields or BS with 3+ years  experience in Computer Vision/Machine Learning
• Hands on experience in developing new learning algorithms for one or more of computer vision tasks such as object detection, object tracking, instance segmentation, activity detection, depthestimation, optical flow, multi-view geometry, domain adaptation, adversarial and generative models etc., and representational learning with a varied amount of data – from a few samples to a very large dataset.
• Knowledge of current DL literature and the mathematical foundations of machine learning
• Experience with popular object detection frameworks such as YOLO (You Only Look Once), SSD (Single Shot MultiBox Detector), or Faster R-CNN (Region-based Convolutional Neural Networks) is a plus
• Ability to train and debug deep learning systems – from defining datasets and evaluation metrics, model training, deployment, failure characterization, and iterative improvement
• Deep insights into data characteristics and ability to map those to appropriate model architectures
• Experience working with inputs coming from multiple cameras and input modes is a plus.
• Experience in AI Infrastructure, Machine Learning Accelerators, On-Device Optimization is a plus
• Experience with training and deploying deep learning models on GPU-accelerated platforms.
• Familiarity with relevant tools and libraries for data pre-processing, augmentation, and visualization.
• Strong programming skills and development experience with python and ML/DL frameworks such as Tensorflow, Pytorch etc.
• Prior experience in deploying machine learning models in production environments and working with cloud platforms (e.g., AWS, Azure, Google Cloud).
• Knowledge of software development best practices and experience with version control systems (e.g., Git).
• Highly motivated and passionate individual with a very strong work ethic, ability to work in a team and work independently under supervision and guidance in a matrix management environment.
• Ability to work in a fast paced, high pressure startup environment and adapt to rapidly changing requirements.
• Excellent problem-solving skills and ability to work independently as well as part of a team.
• Effective communication skills and ability to convey complex technical concepts to nontechnical stakeholders.
• Strong attention to detail and a passion for staying at the forefront of technology advancements in machine learning and computer vision.

Machine Learning Engineer Key Responsibilities

• Research, design, and develop machine learning algorithms and models for various task of
detection, recognition and classification for warehouse inventory management.
• Implement and optimize deep learning architectures for efficiency and accuracy.
• Explore and experiment with various techniques such as transfer learning, data augmentation, and ensemble learning to improve model performance.
• Guide annotation team to accurately annotate data for various model training.
• Curate and pre-process annotated datasets for training and evaluation purposes.
• Collaborate with MLOps to integrate machine learning models into production systems and ensure scalability and reliability.
• Conduct thorough performance analysis and evaluation of models using appropriate metrics and tools.
• Stay up-to-date with the latest advancements in machine learning, computer vision research and integrate relevant findings into our solutions.