When optimising the placement of sensors on an Autonomous Vehicle (AV), research often uses evolutionary algorithms, offering a flexible way to explore complex solution spaces with multiple candidate configurations. However, this approach limits the ability to optimise one particular configuration directly. Sensor placement optimisation methods generally aim to optimise sensor-related aspects, such as visibility or coverage. When sensor placement is optimised based on the performance of a downstream task, this task performance is generally implicit, using surrogate metrics such as important task elements or probabilistic-related metrics. To address this, we propose to use gradient-descent-based optimisation in combination with a differentiable renderer. Making rendering differentiable allows gradients to flow between the initial rendering settings and the output rendered image, enabling gradient-based optimisation methods to optimise parameters based on the gradients of the objective function. This combination allows us to more directly optimise the placement of sensors on an associated downstream task. For this approach, we optimise the position of cameras on an AV based on 2D object detection performance. We create triangle mesh representations of traffic scenes from an AV simulator to use with the differentiable renderer. We use an objective function that combines optimisation losses related to in-frame rotational differences and visibility based on the detected object area of non-ego vehicles. This optimisation also considers constraints such as a minimum distance between each camera pair and camera positions that do not extend across the positioning bounds. The objective formulation results in an improved mean Average Precision (mAP) compared to a random sampling strategy and an intuitive baseline represented by cameras around the highest point on the ego vehicle.