Machine Learning Engineer

The Data Science and Machine Learning (DSML) team owns the full lifecycle of Prodigal's ML systems, from data curation to model selection, fine-tuning, and post-training, through to deployment, evaluation, and ongoing monitoring. We sit close to real product and customer problems, and we're expected to move from experiment to production with both speed and rigor.

• Understand the problem domain. Work closely with product, engineering, and customer-facing teams to turn ambiguous, real-world problems into well-scoped ML problems.
• Curate datasets. Source, clean, and structure the data needed to train and evaluate models, often before any "off-the-shelf" dataset exists.
• Train models across the full spectrum. Build and train everything from simple classifiers to neural networks, transformers, and small/large language models, choosing the right level of complexity for the problem at hand.
• Deploy and scale for inference. Take models from notebook to production. optimizing for latency, cost, and reliability at scale.