Computer Vision AI & ML Engineer at Skild AI

Job description

Company Overview At Skild AI, we are building the world's first general purpose robotic intelligence that is robust and adapts to unseen scenarios without failing. We believe massive scale through data-driven machine learning is the key to unlocking these capabilities for the widespread deployment of robots within society. Our team consists of individuals with varying levels of experience and backgrounds, from new graduates to domain experts. Relevant industry experience is important, but ultimately less so than your demonstrated abilities and attitude. We are looking for passionate individuals who are eager to explore uncharted waters and contribute to our innovative projects. Position Overview We are seeking a Computer Vision AI & ML Engineer to design, build, and deploy advanced perception systems for real-world robotics and automation. You will work across the full machine learning lifecycle—model development, data strategy, evaluation, and production integration—to deliver robust, high-performance vision capabilities. This role combines applied research with hands-on engineering and offers the opportunity to influence both architecture and roadmap decisions. Responsibilities Develop and optimize deep learning models for depth estimation, object detection, segmentation, tracking, and 3D scene understanding using multi-modal sensor data. Build scalable pipelines for data processing, training, evaluation, and deployment into real-world and real-time systems. Design labeling strategies and tooling for automated annotation, QA workflows, dataset management, augmentation, and versioning. Implement monitoring and reliability frameworks, including uncertainty estimation, failure detection, and automated performance reporting. Conduct proof-of-concept experiments to evaluate new algorithms and perception techniques; translate research insights into practical prototypes. Collaborate with robotics, systems, and simulation teams to integrate perception models into production pipelines and improve end-to-end performance. Preferred Qualifications Strong experience with deep learning frameworks (PyTorch, TensorFlow, or JAX). Background in computer vision tasks such as detection, depth estimation, 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 multi-sensor fusion (RGB-D, LiDAR, stereo). Experience with data annotation tools, dataset management, and augmentation techniques. Familiarity with robotics, simulation environments (Isaac Sim, Gazebo, Blender), or real-time systems. Understanding of uncertainty modeling, reliability engineering, or ML monitoring/MLOps practices.

Requirements

Must-haves

Company Overview
Develop and optimize deep learning models for depth estimation, object detection, segmentation, tracking, and 3D scene understanding using multi-modal sensor data.
Build scalable pipelines for data processing, training, evaluation, and deployment into real-world and real-time systems.
Design labeling strategies and tooling for automated annotation, QA workflows, dataset management, augmentation, and versioning.
Implement monitoring and reliability frameworks, including uncertainty estimation, failure detection, and automated performance reporting.
Conduct proof-of-concept experiments to evaluate new algorithms and perception techniques; translate research insights into practical prototypes.
Collaborate with robotics, systems, and simulation teams to integrate perception models into production pipelines and improve end-to-end performance.

Nice-to-haves

Strong experience with deep learning frameworks (PyTorch, TensorFlow, or JAX).
Background in computer vision tasks such as detection, depth estimation, 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 multi-sensor fusion (RGB-D, LiDAR, stereo).
Experience with data annotation tools, dataset management, and augmentation techniques.
Familiarity with robotics, simulation environments (Isaac Sim, Gazebo, Blender), or real-time systems.
Understanding of uncertainty modeling, reliability engineering, or ML monitoring/MLOps practices.

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Keywords to mirror

aimachine learningmlpython

Platform: greenhouse
Effort: quick

Benefits

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Vision