Principal Machine Learning Engineer

• You will set technical strategy for the data graph, entity resolution, buying intent, and agent context, deciding which problems to take on, in what order, and how the pieces connect
• You will frame and ship problems nobody has framed yet, such as extraction for companies with no public footprint or semantic intent at web scale, deciding what not to build as deliberately as what to build
• You will define evaluation standards that other teams adopt, including sampling methodology, human labels, validated LLM judges, and release gates, and know where each one breaks
• You will define the data contracts between the data graph, intent signals, and the agents that consume them, including agent memory, keeping the system cost-effective to run across the full dataset
• You will turn good choices into defaults across the group: the baseline every model must beat, how uncertainty is reported, and which metrics get retired when they mislead
• You will lead design reviews, mentor senior engineers, shape hiring, and represent the team’s technical judgment to product and leadership

Benefits

• Benefits for the Best You - We want our employees and their families to thrive. In addition to comprehensive benefits we offer holistic mind, body and lifestyle programs designed for overall well-being
• Base Pay - We attract and retain the best talent with competitive compensation that aligns with industry benchmarks and merit programs yearly for promotions and salary increases based on performance, time in role, and position to market
• Bonus - When you crush it, we like to celebrate! Bonuses are awarded annually in connection with company and individual performance
• Equity - We offer each employee a generous equity award in the form of RSUs, so they can directly feel the results and success of their hard work
• Vacation & Time Off - We know everyone’s needs are different so we offer flexible paid time off plans including an unlimited vacation option for many employees
• Hybrid Working Model - We offer flexible hybrid remote working models to promote equity, employee wellness, and work life balance

• 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