Senior Deep Learning Engineer

About

Mentee Robotics is redefining humanoid automation with an AI-first approach, integrating cutting-edge perception, reasoning, and dexterous manipulation into a fully autonomous humanoid robot that continuously adapts and learns. Our flagship product, Menteebot v3, is designed to seamlessly integrate into industrial, logistics, and retail environments, performing complex tasks with human-like adaptability.

We are looking for an experienced Deep Learning Engineer to build the foundational infrastructure for training, simulation integration, deployment, and optimization of deep learning models that power Menteebot's intelligence.

What you will do

As a Senior Deep Learning Engineer, you will be responsible for scalable deep learning systems in a robotics context. You will own the entire deep learning infrastructure — from simulator integration to training, deployment and optimization. You will have access to peta-bytes of data and enough compute to train state of the art VLA (vision-language-action) models.

• Build robust, modular pipelines for training and deploying deep learning models at scale.
• Design infrastructure to support high-performance training (multi-node/GPU/cloud).
• Integrate training and evaluation pipelines with robotics simulators.
• Optimize models and runtime performance for real-time deployment on edge devices.
• Collaborate closely with AI researchers to bring models into production.
• Maintain high code quality, observability, and reproducibility standards.

Requirements

• B.Sc. in Computer Science, Engineering, or a related field.
• 5+ years of hands-on experience in software engineering or machine learning infrastructure.
• Proven experience building and scaling deep learning pipelines — beyond just using existing ones.
• Comfortable working in cloud and distributed environments (e.g., AWS, SLURM).
• Proficient with PyTorch and widely-used ML tools and frameworks.
• Demonstrated systems thinking and a strong builder mindset.

Advantages

• Experience integrating simulators (e.g., Isaac Gym, MuJoCo, or similar) into training loops.
• Familiarity with robotics environments or edge deployment.
• Previous experience as the first hire in a domain and setting foundations for others.
• Experience with model optimization techniques (quantization, pruning, distillation, etc.).