Principal Machine Learning Engineer

Join our team as a Principal Machine Learning Engineer, where you will set the technical strategy for key areas such as data graph, entity resolution, buying intent, and agent context. You will frame and ship complex problems, define evaluation standards, and establish data contracts. Additionally, you will lead design reviews, mentor senior engineers, shape hiring, and represent the team's technical judgment to product and leadership. Enjoy competitive compensation, comprehensive benefits, and a flexible hybrid working model.

Missions

• Set technical strategy for data graph, entity resolution, buying intent, and agent context.
• Define evaluation standards that other teams adopt, including sampling methodology, human labels, validated LLM judges.
• Lead design reviews, mentor senior engineers, shape hiring, and represent the team's technical judgment to product and leadership.

Profil recherché

- You have defined and driven adoption of evaluation standards beyond your own team for systems with no single right answer, including LLM judges validated against human labels and results reported with their limits

- You are hands-on in production Python and strong SQL with distributed data processing experience, and you use AI coding tools daily with rigorous review of their output

- You have set technical direction as an individual contributor for a problem area spanning more than one team, with the resulting machine learning systems in production and owned after launch - depth of impact matters more than years or past titles

- You bring breadth across classical machine learning on large, messy tabular data, applied statistics, language processing at scale, and LLM agents or multi-step systems, with recognized depth in at least one

- You have made cost and capacity decisions for model training and serving, including when to self-host, distill, or call a hosted model, with cost tracked per unit of work

- You have a track record of changing how a group builds or measures - for example, introducing a baseline or review practice that other teams now use

- You have experience with ranking and retrieval, including embeddings, learned re-ranking, and metrics such as recall@k, MRR, and nDCG

- You bring entity resolution, knowledge graph, or web-scale information extraction experience on messy, real-world data

- You have trained and served open-weight models in PyTorch or an equivalent framework, including GPU capacity planning

- You have built user memory or context systems for agents, or defended LLM systems against adversarial inputs and prompt injection