Computer Vision AI & ML Engineer
At a glance
Mid-level Machine Learning Engineering role at Skild AI. San Mateo · no salary stated · we estimate $201,625–$333,295.
Skild AI has not published a salary for this role. The figure below is our estimate, not theirs.
aimlroles summary, based on the employer's posting.
What you'll do
- Build and tune vision models for depth, detection, segmentation, tracking and 3D scene interpretation from multiple sensor types
- Create pipelines that move data through processing, model training and evaluation into real-time deployments
- Set up annotation methods and tools, quality checks, dataset tracking and augmentation workflows
- Track model reliability with uncertainty estimates, failure detection and automated performance reports
- Test new perception methods, turn promising findings into prototypes and connect models with robotics, systems and simulation teams
What you bring
- Hands-on work with deep learning frameworks such as PyTorch, TensorFlow or JAX
- A background in computer vision, including detection, depth estimation or another listed task
- Python proficiency and experience creating model training, evaluation or deployment workflows
- Knowledge of 3D geometry, sensor processing or multi-sensor fusion, such as RGB-D or LiDAR
Who this fits
This mid-level role suits someone who combines applied research with practical model-building and production integration. The position is based in San Mateo.
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 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.