Responsibilities :

- Develop, train, and optimize AI/ML models that enhance Flippy's perception,decision-making, and real-time operational performance.

- Conduct experiments and research to improve model accuracy, robustness, and generalization across diverse kitchen environments.

- Collaborate with software, hardware, and robotics engineers to integrate AI models into the robotic stack, ensuring seamless on-device performance.

- Build and maintain data pipelines for continuous model training, evaluation, and deployment.

- Optimize models for real-time inference on embedded GPU hardware (e.g., NVIDIA Jetson).

- Improve the system's ability to handle challenging conditions such as heat, steam, occlusion, variable lighting, and rapid operator movements.

- Develop ML systems to support tasks such as activity recognition, anomaly detection, quality assessment, and robotic behavior prediction.

- Implement monitoring and feedback loops to identify model drift and drive continuous improvement.

- Stay current on advancements in computer vision, robotics ML, edge inference, and GPU acceleration, and recommend new methods for productization.

Qualifications :

- Strong background in AI/ML with applied experience in computer vision, robotics ML, or real-time edge intelligence.

- Hands-on experience with NVIDIA GPU acceleration, CUDA, TensorRT, and deep learning frameworks (e.g., PyTorch, TensorFlow).

- Proficiency in Python, Linux, and modern development workflows (Git, CI/CD, etc.).

- Experience building, training, and deploying ML models in production or on embedded systems.

- Familiarity with robotic perception concepts such as object detection, segmentation, tracking, sensor fusion, or pose estimation.

- Ability to analyze performance bottlenecks and optimize models for latency, throughput, and reliability on embedded hardware.

- Strong cross-functional collaboration skills with software, hardware, and robotics teams.

Desired Multipliers :

- Experience applying AI/ML to industrial automation, robotic manipulation, or kitchen automation.

- Experience with NVIDIA tools and frameworks for robotics and edge AI (e.g., Isaac,tensorRT, Jetson ecosystem).

- Familiarity with ROS (Robot Operating System).

- Experience with perception algorithms, sensor fusion, motion planning, or behavior modeling.

- Knowledge of reinforcement learning, imitation learning, or behavior cloning for robotic applications.

- Experience working in noisy, dynamic, or unstructured real-world environments.

- Experience with agile tools such as JIRA.

- Strong attention to detail and a passion for improving real-world robotic performance.