Robotics Vision & ML Engineer: Real-Time Perception

Skild; San Mateo (CA); On-site; USD 140,000 - 210,000; Full time

Job summary

Skild is seeking a Computer Vision AI & ML Engineer to design, build, and deploy 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.

Qualifications

• Experience with deep learning and computer vision tasks.
• Proficiency in ML lifecycle: data strategy, evaluation, deployment.
• Familiarity with 3D sensors and multi-sensor fusion is a plus.

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.

Skills

Deep learning

Computer vision

Python

ML deployment

ML research

Tools

PyTorch

TensorFlow

JAX

Python

C++

CUDA

Job description

Skild is seeking a Computer Vision AI & ML Engineer to design, build, and deploy 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.