Computer Vision AI & ML Engineer
At a glance
Mid-level Machine Learning Engineering role at Skild AI. Bengaluru.
Pay not stated ML Engineer salary
aimlroles summary, based on the employer's posting.
What you'll do
- Build and tune vision models for detecting and tracking objects, segmenting scenes, and interpreting 3D environments from sensor inputs
- Create workflows that move data through preparation, model training, testing and deployment into real-time systems
- Set up annotation and dataset processes, including automated labeling, quality checks, augmentation and version tracking
- Test new perception methods, turn promising results into prototypes, and connect models with robotics and simulation teams
What you bring
- Hands-on experience with computer vision methods such as object detection, segmentation, tracking or 3D scene interpretation
- Strong command of a deep learning framework such as PyTorch, TensorFlow or JAX, plus Python proficiency
- Experience creating model training and evaluation workflows and deploying machine-learning systems
- Knowledge of 3D geometry, sensor processing or combining inputs such as RGB-D, LiDAR or stereo
Who this fits
This role suits an engineer who can pair applied research with practical model development and production integration. You’ll work with robotics, systems and simulation teams, and the role is based in Bengaluru.
From the employer
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 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, 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.