Machine Learning Engineer Intern - Scenario Simulation

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{
  "lists": [
    {
      "text": "Responsibilities: ",
      "content": "<div><span data-sheets-root=\"1\">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.<br><br>Optimize Simulation Throughput: Research and implement a \"shortcut\" pipeline that bypasses slow image/LiDAR rendering to generate BEV features directly from state data.<br><br>Enable Closed-Loop Training: Integrate the translated BEV embeddings into a training pipeline to make synthetic data directly usable for planning models.<br><br>Support Reinforcement Learning: Create the infrastructure necessary for planning models to undergo self-play RL fine-tuning within the bridged BEV feature space.</span></div>"
    },
    {
      "text": "Required Skills:",
      "content": "\n<li><span data-sheets-root=\"1\">Strong foundation in deep learning, computer vision, and machine learning.</span></li>\n<li><span data-sheets-root=\"1\">Proficiency in Python and deep learning frameworks (PyTorch)</span></li>\n<li><span data-sheets-root=\"1\">Prior experience with BEV / E2E Autonomous Driving Architectures (understanding of BEV generation, sensor fusion, spatial transformation).</span></li>\n<li><span data-sheets-root=\"1\">Prior experience in addressing the Sim-to-Real Gap in autonomous systems such as robotics and autonomous driving&nbsp;<br><br></span></li>\n"
    },
    {
      "text": "Preferred Skills:",
      "content": "<div><span data-sheets-root=\"1\">Nice-to-have:</span></div>\n<div>&nbsp;</div>\n\n<li><span data-sheets-root=\"1\">Experience with C++</span></li>\n<li><span data-sheets-root=\"1\">Experience with End-to-End (E2E) driving models.<br></span></li>\n<li><span data-sheets-root=\"1\">Experience with working with simulators (CARLA, IsaacSim, Gazebo)<br></span></li>\n<li><span data-sheets-root=\"1\">Experience with curating datasets and synthetic data generation (SDG),&nbsp;<br></span></li>\n<li><span data-sheets-root=\"1\">Experience with ML Infras (Kubeflow, MLFlow, Weights &amp; Biases, etc)&nbsp;<br></span></li>\n<li><span data-sheets-root=\"1\">Basic understanding of simulation / scenario-based testing for autonomous driving systems</span></li>\n"
    }
  ],
  "country": "US",
  "createdAt": 1779312458524,
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    "team": "Planning",
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