High-quality feedback requires understanding of a student's work, insights into what concepts would help them improve, and language that matches the preferences of the specific teaching team. While Large Language Models (LLMs) can generate coherent feedback, adapting these responses to align with specific teacher preferences remains an open challenge. We present a method for aligning LLM-generated feedback with teacher preferences using Direct Preference Optimization (DPO). We integrate preference data collection into the grading process. This creates a self-improving pipeline keeping the teacher-in-the-loop to ensure feedback quality and maintain teacher autonomy. To evaluate effectiveness, we conducted a blind controlled study where expert evaluators compared feedback from multiple models on anonymized student submissions. Evaluators consistently preferred feedback from our DPO model over GPT-4o. We deployed the system in two offerings of a large university course, with nearly 300 students and over 10 teaching assistants per term, demonstrating its feasibility in real classroom settings. We share strategies for automated performance monitoring using critic models. We explore methods for examining fairness across protected demographics. [For the complete proceedings, see ED675583.]