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.
• Evaluate and close the loop. Design evaluation frameworks that actually measure what matters, and build learning loops.

• Strong fundamentals in ML/DL, backed by hands-on experience training and shipping models, not just coursework or competitions.
• High agency, you take an ambiguous problem and run with it rather than waiting for it to be fully specified.
• A first-principles approach to problem-solving – you reason from the ground up rather than defaulting to whatever's fashionable or familiar.
• Genuine curiosity and the comfort to operate at the edge of what you already know.

• Real problems, real models, real data. No toy datasets, no academic benchmarks — you'll work on problems that directly affect customers and the business.
• Frontier technology, applied. Learn to harness the latest in AI and put it to work on problems that matter.
• Smart people, fast shipping. Work alongside a sharp team, ship quickly, and grow faster than you would elsewhere.

• 2-5 years of hands-on engineering experience, with good exposure to building or working with ML or AI systems in production.
• Solid Python fundamentals - you are comfortable writing clean, maintainable code and debugging production issues.
• Some experience working with LLMs: prompt engineering, API integrations, or building simple pipelines or agents.
• High bias for action - you don't wait to be told exactly what to do, and you push yourself to ship rather than over-engineer.
• Eager to learn in a fast-moving environment, take ownership of your work, and ask good questions.

Health insurance for you and your family, meals at office on us, travel reimbursement, unlimited leaves, gym membership, unlimited learning & development, flexible work schedule and a world-class team to learn and grow with!