Applied AI Engineer

Responsibilities:

• Take models and methods from research and turn them into production-ready services.
• Build evaluation frameworks to determine whether a change genuinely improves performance.
• Optimize retrieval, prompting, and memory for accuracy and cost efficiency.
• Instrument production systems to ensure that regressions are identified before they impact customers.
• Work across the entire development cycle, collaborating with research teams and providing feedback based on issues and observations from production.

Requirements:

• A working understanding of modern machine learning and LLMs, including how they work, where they fail, and why.
• Strong software engineering fundamentals, with the ability to write maintainable code that other engineers can run, understand, and modify.
• 2+ years of experience shipping real-world software or production systems, whether ML-backed or otherwise.
• Strong proficiency in Python and familiarity with PyTorch or an equivalent ML framework.
• Strong engineering judgment and the ability to distinguish genuine improvements from changes that only make performance metrics look better.

Nice to Have:

• Hands-on experience with retrieval, embeddings, or vector search in production environments.
• Experience with evaluation frameworks, LLM-as-a-Judge setups, and an understanding of their limitations.
• Exposure to fine-tuning techniques, including LoRA or adapters.
• Experience owning both a production service and a machine learning model.