Machine Learning Engineer
2 days ago
Are you passionate about applying machine learning to robotics to enable intelligent, autonomous behavior? Do you excel at building and optimizing algorithms that empower robots to learn, adapt, and perform complex tasks? If you’re ready to create the brains behind robotic systems, our client has the ideal role for you. We’re looking for a Machine Learning Engineer (Robotics) (aka The Intelligent Automation Specialist) to develop and implement machine learning models that advance the capabilities of robotic systems.
As a Machine Learning Engineer in Robotics at our client , you’ll collaborate with robotics, software, and data teams to design algorithms that enhance navigation, perception, and decision-making in real-world environments. Your work will be central in creating robots that learn from their environments, react to changing conditions, and continuously improve.
Key Responsibilities:
- Develop Machine Learning Models for Robotics Applications: Build and train models for object recognition, obstacle detection, navigation, and decision-making. You’ll develop algorithms that enable robots to interpret and respond to complex environments.
- Implement Reinforcement Learning and Computer Vision Algorithms: Utilize reinforcement learning and computer vision techniques to enable autonomous robotic behavior and perception. You’ll create adaptive systems that improve performance over time.
- Integrate ML Models into Robotic Systems: Work with software and hardware teams to deploy ML models onto embedded platforms and robotic hardware. You’ll ensure models are optimized for real-time performance and reliability.
- Optimize Algorithms for Real-Time Performance: Enhance model efficiency to run effectively on limited computational resources. You’ll refine and compress algorithms to achieve high performance within embedded or edge environments.
- Collect and Process Training Data: Gather and preprocess data from robotic sensors (e.g., cameras, LIDAR, IMUs) for training and testing. You’ll ensure that data is clean, labeled, and prepared for robust model training.
- Conduct Simulation and Testing of ML-Driven Behaviors: Use simulation environments and real-world tests to validate ML model performance. You’ll monitor, troubleshoot, and optimize models to handle edge cases and variability in real-time applications.
- Stay Updated on AI and Robotics Trends: Keep current with advancements in machine learning, computer vision, and AI as applied to robotics. You’ll integrate cutting-edge techniques to maintain a competitive edge in robotic intelligence.
Required Skills:
- 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 (e.g., object detection, segmentation) 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.
Educational Requirements:
- Bachelor’s or Master’s degree in Computer Science, Robotics, Machine Learning, or a related field. Equivalent experience in ML and robotics may be considered.
- Relevant certifications or coursework in machine learning, AI, or computer vision are advantageous.
Experience Requirements:
- 3+ years of experience in machine learning engineering, with a focus on robotics or autonomous systems.
- Experience with robotic operating systems like ROS (Robot Operating System) and familiarity with simulation environments (e.g., Gazebo) is beneficial.
- Familiarity with embedded ML deployment (e.g., TensorFlow Lite, NVIDIA Jetson) is a plus.
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