Evaluations Team Lead

Accountabilities:

Build and maintain a shared evaluation platform that supports research, engineering, and Applied AI teams, enabling independent benchmarking and investigation of model performance.
Develop and continuously improve benchmark suites, curated datasets, and model comparisons to assess predictive quality across a broad range of business use cases.
Establish rigorous evaluation standards covering statistical metrics, validation strategies, data splits, leakage prevention, calibration, uncertainty estimation, and benchmark contamination.
Design reproducible evaluation pipelines with version-controlled inputs, datasets, and artifacts, integrating automated regression checks into research and release workflows.
Benchmark models against competing approaches using fair and consistent protocols for hyperparameter tuning, data access, computational resources, and end-to-end latency measurement.
Evaluate predictive performance, inference latency, and cost efficiency across deployment configurations, task types, and dataset characteristics.
Support customer proof-of-concept evaluations by providing reliable tooling, methodological guidance, and data-driven analysis to help demonstrate model value.
Translate evaluation findings into actionable research priorities, release recommendations, and evidence-based improvement plans for customer use cases.
Recruit, lead, and develop a small team of evaluation specialists, setting priorities, providing technical coaching, and delivering constructive performance feedback.
Remain hands-on with Python development, experimental design, statistical analysis, and evaluation methodology while fostering collaboration across research, engineering, and customer-facing teams.
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.