Computer Vision AI & ML Engineer

Menlo Ventures; San Mateo (CA); On-site; USD 90,000 - 130,000; Full time

Job summary

Menlo Ventures is looking for a Computer Vision AI & ML Engineer in California to design and deploy cutting-edge perception systems for robotics. You will work throughout the machine learning lifecycle, from model development to production integration.

The successful candidate will leverage deep learning techniques and collaborate with simulation and robotics teams. Strong experience in deep learning frameworks and a passion for innovation in robotics are essential.

Qualifications

• Strong background in deep learning for robotics.
• Experience with data processing and model deployment.
• Familiar with monitoring and reliability engineering.

Responsibilities

• Develop deep learning models for robotics perception.
• Build scalable data processing and training pipelines.
• Collaborate with teams to integrate perception models.

Skills

Deep learning frameworks (PyTorch, TensorFlow, or JAX)

Computer vision tasks (detection, depth estimation, tracking)

Python (C++ is a plus)

Building training pipelines

Data annotation tools and techniques

Tools

Robotics simulation environments (Isaac Sim, Gazebo)

Multi-sensor fusion techniques

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.