Requirements:

• Proven experience owning the evaluation of tabular machine learning systems used in production environments or high-impact customer decision-making.
• Strong statistical expertise, including selecting appropriate metrics and validation schemes, estimating uncertainty, comparing models across datasets, and accounting for repeated experimentation.
• Practical experience identifying and preventing data leakage involving preprocessing, feature engineering, joins, temporal dependencies, and related entities across training and evaluation splits.
• Strong proficiency in Python and SQL, with familiarity with scikit-learn, gradient-boosted decision trees, and the development of reliable ML tools or platforms used by multiple teams.
• Demonstrated experience designing fair and reproducible model comparisons, including hyperparameter optimization, resource allocation, and end-to-end inference latency measurement.
• Previous people management experience, including hiring, technical mentoring, team development, and performance management, combined with a willingness to remain actively involved in technical work.
• Excellent written and verbal communication skills, with the ability to collaborate effectively with researchers, software engineers, and customer data scientists.
• A strong analytical mindset and the confidence to challenge unsupported conclusions, communicate uncertainty, and ensure that technical decisions are grounded in reliable evidence.
• Experience evaluating tabular foundation models or automated machine learning (AutoML) systems is an advantage.
• Familiarity with measuring the impact of model optimizations on predictive performance and inference efficiency is desirable.
• Experience working with relational or multi-table datasets and data platforms such as Snowflake or Databricks is a plus.
• Knowledge of automated or agent-driven machine learning workflows, including identifying failure modes that aggregate metrics may conceal, is beneficial.

Benefits:

• Competitive compensation package combining salary and equity.
• Comprehensive health coverage for employees and their dependents.
• Paid parental leave for all new parents, including adoptive and surrogacy journeys.
• Relocation assistance for employees moving to join the team at one of the company's office locations.
• Remote working opportunities across eligible locations in Europe and Israel.
• The opportunity to establish evaluation standards and build a new function from the ground up, with substantial technical ownership and influence.
• A mission-driven, collaborative, and low-ego culture that values diverse perspectives, individual ownership, and proactive decision-making.
• The opportunity to work on advanced AI research and help improve the reliability, predictive performance, and efficiency of enterprise AI systems.

How Jobgether works:

We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.