DATA SCIENTIST, AI SYSTEMS: MAKE THE SCHEDULING SMARTER.

At Doodle, we’re building the future of scheduling and productivity, tackling complex challenges at the intersection of human collaboration and digital efficiency. We believe in crafting elegant solutions to intricate problems, and we’re looking for passionate Data Scientists who share this drive.
You’re more than an analyst; you’re a builder, a problem-solver, and a continuous learner who thrives on applying AI to real-world impact. You’ll shape and refine our core AI systems, playing a pivotal role in designing and training models that are accurate, scalable, and resilient. If you’re excited by the intricacies of machine learning pipelines, the power of generative AI, and the precision required when working with human time and behavior, you’ll find a stimulating environment here.

You will be the architect of our intelligent systems, from a data and modeling perspective. You will design, explore, and prototype solutions for complex problems.

• Explore and implement Retrieval-Augmented Generation (RAG) or Graph RAG approaches to improve the quality of information retrieval and reasoning in LLM workflows. This includes graph construction, entity linking, and hybrid scoring strategies.
• Contribute to prompt design and continuously evaluate prompt results to ensure model outputs are reliable and aligned with user goals.
• Design and implement solutions using various neural network architectures beyond LLMs, such as Convolutional Neural Networks (CNNs) for image tasks or Recurrent Neural Networks (RNNs) for sequential data, based on the specific problem.
• Develop evaluation frameworks for testing model output quality, reliability, and alignment with user goals (e.g., hallucination detection, prompt regression, safety scoring).
• Contribute to our AI strategy and roadmap, helping shape how we scale the use of models responsibly across the platform. This includes thinking beyond LLMs to select the right neural network architecture for a given task.
• Prototype and evaluate AI features on platforms like Claude or Amazon Bedrock, with a focus on production readiness.
• Quantize large models into smaller, more efficient models to enable edge intelligence and on-device processing.