Computer Vision AI & ML Engineer

Rethink recruit; California (MO); On-site; USD 120,000 - 180,000; Full time

Job summary

Skild AI is 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—from model development and data strategy to evaluation and production integration—shaping architecture and roadmap decisions.

You will collaborate with robotics, systems, and simulation teams to deliver robust, high-performance vision capabilities and translate research insights into

Qualifications

• Experience with deep learning frameworks for perception tasks.
• Strong foundation in computer vision including detection, depth, segmentation, or 3D understanding.
• Proficiency in Python and software development for ML pipelines.

Responsibilities

• Develop and optimize deep learning models for depth estimation, object detection, segmentation, tracking, and 3D scene understanding.
• Build scalable data processing, training, evaluation, and deployment pipelines.
• Design labeling strategies and tooling for automated annotation and dataset management.
• Implement monitoring and reliability frameworks including uncertainty estimation and performance reporting.
• Collaborate with robotics and simulation teams to integrate perception models into production pipelines.

Skills

PyTorch

TensorFlow

JAX

Python

Tools

Isaac Sim

Gazebo

Blender

Job description

About Skild AI

Skild AI is building the world's first general purpose robotic intelligence --- systems that are robust and adapt to unseen scenarios without failing. The core belief is that massive scale through data-driven machine learning is the key to unlocking these capabilities and enabling the widespread deployment of robots in the real world.

The team spans new graduates and domain experts, and values demonstrated ability and attitude over credentials. Skild is looking for people who are eager to explore uncharted territory and contribute to work that has not been done before.

The Opportunity

Skild AI is looking for 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 --- delivering robust, high-performance vision capabilities. This role combines applied research with hands-on engineering and offers real influence over architecture and roadmap decisions.

What You'll Do

• 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

You Should Have

• Strong experience with deep learning frameworks such as 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 including RGB‑D, LiDAR, or stereo

Nice to Have

• Experience with data annotation tools, dataset management, and augmentation techniques
• Familiarity with robotics, simulation environments such as Isaac Sim, Gazebo, or Blender, or real-time systems
• Understanding of uncertainty modeling, reliability engineering, or ML monitoring and MLOps practices