Progressive Robotics is a deep-tech startup building next-generation robotic intelligence for logistics and warehouse automation. We focus on closing the gap between cutting-edge machine learning research and real-time, production-ready robotic systems.
Building a model in a notebook is not enough. True robotic intelligence requires perception and decision-making systems that are real-time, robust, and optimized for the hardware they run on. We’re looking for an ML Engineer who is excited to work at the intersection of deep learning, GPU acceleration, and scalable deployment.
What You’ll Do
- Build and maintain high-performance training pipelines for supervised and reinforcement learning.
- Design, train, and optimize deep learning models for robotic perception and autonomous decision-making.
- Optimize models for hardware-accelerated execution on GPUs and edge devices.
- Deploy ML models into production using modern MLOps practices.
- Run experiments, evaluate performance, and iterate using rigorous metrics and validation frameworks. Collaborate closely with backend and systems engineers to integrate ML solutions into customer-facing products.
Requirements
- BSc or MSc in Computer Science, Computer Engineering, or a related field.
- Strong experience with deep learning frameworks such as PyTorch or TensorFlow.
- Proficiency in Python and C++.
- Hands-on experience with GPUs, CUDA, or distributed training (e.g. TensorRT, Triton).
- Solid understanding of modern ML architectures (CNNs, Transformers) and Reinforcement Learning. Experience with model evaluation, hyperparameter tuning, and experiment tracking.
Nice to Have
- Background in Robotics.
- Experience with Docker and containerized deployments.
- Familiarity with ROS2.
Tags & focus areas
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