[ref. u80492116] San Francisco - Machine Learning Engineer (Robotics) (The Intelligent Automation Specialist)

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:

1. 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.

Requirements

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.

• Health and Wellness: Comprehensive medical, dental, and vision insurance plans with low co-pays and premiums.
• Paid Time Off: Competitive vacation, sick leave, and 20 paid holidays per year.
• Work-Life Balance: Flexible work schedules and telecommuting options.
• Professional Development: Opportunities for training, certification reimbursement, and career advancement programs.
• Wellness Programs: Access to wellness programs, including gym memberships, health screenings, and mental health resources.
• Life and Disability Insurance: Life insurance and short-term/long-term disability coverage.
• Employee Assistance Program (EAP): Confidential counseling and support services for personal and professional challenges.
• Tuition Reimbursement: Financial assistance for continuing education and professional development.
• Community Engagement: Opportunities to participate in community service and volunteer activities.
• Recognition Programs: Employee recognition programs to celebrate achievements and milestones.