Responsibilities
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Execute the PCP metrics pipeline, assessing how hardware updates across various sensors impact PCP performance sensitivity in 3D scene understanding, live mapping and localization, especially in adverse weather and winter conditions.
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Design and run structured A/B testing experiments, including side-by-side custom mount runs and tandem vehicle data collection, to clear sensors for the winter ODD.
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Collaborate with AHE to correlate sensor-specific lab tests (e.g., heater/defroster impacts on camera lenses, snow accumulation on radomes) to downstream PCP performance.
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Root cause detection regressions to understand how early fusion models compensate for modality degradation in challenging environments.
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Design & develop performant and efficient multi-sensor fusion model to leverage the complementary advantage of on-board (low cost) sensors to improve Perception performance for highway and adverse weather.
Qualifications
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Strong C++ and Python for running and modifying PCP metrics pipelines, automated testing, and re-simulations.
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Experience with A/B testing methodology (RBV - Risk Balanced Verification) for evaluating hardware/software changes in varied environments.
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Familiarity with diverse perception outputs and how environmental factors (like snow or fog) degrade bounding box precision/recall, point clouds, and 3D occupancy.
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Ability to define complex data collection plans for winter ODDs (e.g., specific snow types, ice, low temperatures).
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Experience with designing, developing and deploying multi-modality fusion based Perception models for 3D object detection, 3D segmentation & occupancy, Mapping and Localization.
Bonus Qualifications
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Experience in performance optimization to fit complex ML stack to low-power low cost edge compute (e.g., Nvidia Thor, TensorRT optimization).
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Experience with designing better sensor or better ML model for an anomaly or rare event/objects detection
