Machine Learning Engineer (Robotics) (The Intelligent Automation Specialist)

Unreal Gigs - San Francisco, CA

Full-time - Mid Level

San Francisco, CA

About the position

The Machine Learning Engineer (Robotics) role focuses on developing and implementing machine learning models to enhance the capabilities of robotic systems. This position involves collaboration with various teams to design algorithms that improve navigation, perception, and decision-making in real-world environments, ultimately creating intelligent, autonomous robots that can learn and adapt to their surroundings.

Responsibilities

• Develop Machine Learning Models for Robotics Applications: Build and train models for object recognition, obstacle detection, navigation, and decision-making.
• Implement Reinforcement Learning and Computer Vision Algorithms: Utilize reinforcement learning and computer vision techniques to enable autonomous robotic behavior and perception.
• Integrate ML Models into Robotic Systems: Work with software and hardware teams to deploy ML models onto embedded platforms and robotic hardware.
• Optimize Algorithms for Real-Time Performance: Enhance model efficiency to run effectively on limited computational resources.
• Collect and Process Training Data: Gather and preprocess data from robotic sensors for training and testing.
• Conduct Simulation and Testing of ML-Driven Behaviors: Use simulation environments and real-world tests to validate ML model performance.
• Stay Updated on AI and Robotics Trends: Keep current with advancements in machine learning, computer vision, and AI as applied to robotics.

Requirements

• Proficiency in Machine Learning and Deep Learning: Extensive experience with ML/DL frameworks like TensorFlow, PyTorch, or Keras, specifically applied to robotics or autonomous systems.
• Expertise in Computer Vision and Reinforcement Learning: Strong knowledge of computer vision techniques and reinforcement learning for enabling adaptive behaviors in robots.
• Programming Skills in Python and C++: Proficiency in Python for model development and C++ for integration into robotic systems and real-time applications.
• Data Processing and Model Optimization: Experience with data cleaning, preprocessing, and model optimization techniques to maximize performance on embedded hardware.
• Analytical and Problem-Solving Skills: Strong troubleshooting skills for debugging ML models, analyzing performance data, and implementing improvements in complex systems.

Nice-to-haves

• Familiarity with robotic operating systems like ROS (Robot Operating System) and simulation environments (e.g., Gazebo).
• Familiarity with embedded ML deployment (e.g., TensorFlow Lite, NVIDIA Jetson).
• Relevant certifications or coursework in machine learning, AI, or computer vision.