PlusAI
Physical AI
MachineLearningEngineerIntern-ScenarioSimulation
Neural analysis suggests this role is
optimal for Entry candidates.
“Machine Learning Engineer Intern- Scenario Simulation at PlusAI. Skills: Machine Learning, Computer Vision, Python, PyTorch. Develop translation layer. Optimize simulation throughput”
Industry & Context.
Bridge the Sim-to-Real Gap; Optimize Simulation Throughput; Enable Closed-Loop Training
What They're Looking For.
Must Have
deep learning, computer vision, machine learning, Python, PyTorch, BEV / E2E Autonomous Driving Architectures, Sim-to-Real Gap in autonomous systems
Nice to Have
C++, End-to-End (E2E) driving models, CARLA, IsaacSim, Gazebo, curating datasets, synthetic data generation, Kubeflow, MLFlow, Weights & Biases, simulation / scenario-based testing
What You'll Do.
Develop translation layer
Optimize simulation throughput
Enable closed-loop training
Support reinforcement learning
Create infrastructure for RL
Full Job Description
## Description PlusAI is a Physical AI company pioneering AI-based virtual driver software for factory-built autonomous trucks. Headquartered in Silicon Valley with operations in the United States and Europe, Plus was named by Fast Company as one of the World’s Most Innovative Companies. Partners including TRATON GROUP’s Scania, MAN, and International brands, Hyundai Motor Company, Iveco Group, Bosch, and DSV are working with Plus to accelerate the deployment of next-generation autonomous trucks. If you’re ready to make a huge impact and drive the future of autonomy, Plus is looking for talented individuals to join its fast-growing teams. ## Responsibilities Bridge the Sim-to-Real Gap: Develop and implement a translation layer that converts idealized simulator Ground Truth (GT) into realistic, "noisy" Bird’s-Eye View (BEV) embeddings.Optimize Simulation Throughput: Research and implement a "shortcut" pipeline that bypasses slow image/LiDAR rendering to generate BEV features directly from state data.Enable Closed-Loop Training: Integrate the translated BEV embeddings into a training pipeline to make synthetic data directly usable for planning models.Support Reinforcement Learning: Create the infrastructure necessary for planning models to undergo self-play RL fine-tuning within the bridged BEV feature space. ## Required Skills Strong foundation in deep learning, computer vision, and machine learning. Proficiency in Python and deep learning frameworks (PyTorch) Prior experience with BEV / E2E Autonomous Driving Architectures (understanding of BEV generation, sensor fusion, spatial transformation). Prior experience in addressing the Sim-to-Real Gap in autonomous systems such as robotics and autonomous driving ## Preferred Skills Nice-to-have: Experience with C++ Experience with End-to-End (E2E) driving models. Experience with working with simulators (CARLA, IsaacSim, Gazebo) Experience with curating datasets and synthetic data generation (SDG), Experience with ML Infra
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