4 months ago
Bengaluru, IndiaMid Level
Responsibilities
- Develop and optimize deep learning models for object detection, segmentation, tracking, and 3D scene understanding.
- Build scalable data processing, training, evaluation, and deployment pipelines for real-time systems.
- Design annotation strategies and tooling for automated labeling, QA workflows, dataset management, augmentation, and versioning.
- Implement uncertainty estimation, failure detection, monitoring, reliability, and automated performance reporting frameworks.
- Conduct proof-of-concept experiments and translate research findings into practical perception prototypes.
- Collaborate with robotics, systems, and simulation teams to integrate perception models into production pipelines and improve end-to-end performance.
Requirements
- Strong experience with PyTorch, TensorFlow, or JAX.
- Background in computer vision tasks including detection, segmentation, tracking, or 3D scene understanding.
- Proficiency in Python; familiarity with C++ is a plus.
- Experience building training pipelines, evaluation frameworks, and ML deployment workflows.
- Knowledge of 3D geometry, sensor processing, or multimodal sensor fusion involving RGB-D, LiDAR, or stereo data.
- Experience with data annotation tools, dataset management, and augmentation techniques.
- Familiarity with robotics, simulation environments, or real-time systems.
- Understanding of uncertainty modeling, reliability engineering, or ML monitoring and MLOps practices.
Categories
ML EngineeringRobotics
About Skild AI
Skild AI builds a general-purpose robotic intelligence and software stack to train and deploy robots that adapt to varied, unseen tasks for commercial use. It offers a platform and models for robot manufacturers and enterprises automating physical work, with support for integration across manipulators and mobile systems. Founded in 2023 and headquartered in Pittsburgh, the privately held company raised a Series C in 2026.