Evaluations Team Lead

Jobgether SRL; Schweiz; On-site; CHF 140,000 - 210,000; Full time

Benefits offered by this job

Competitive equity

Health coverage for employees and deps

Parental leave

Relocation assistance

Remote work across Europe & Israel

Job summary

Jobgether SRL in Switzerland is seeking an Evaluations Team Lead to shape how AI models are evaluated, validated, and improved in production. You will lead a shared evaluation platform across research, engineering, and customer-facing teams, establishing benchmarking standards and reproducibility.

You will manage a small team while remaining hands-on in code, experiments, and methodology. The role emphasizes predictive quality, latency, and cost efficiency to guide research and product releases,

Qualifications

• Experience owning production evaluation of ML systems.
• Strong statistical methods and uncertainty estimation.
• Experience preventing data leakage across splits.
• Ability to lead and grow a team.
• Excellent communication with researchers and engineers.

Responsibilities

• Build and maintain a shared evaluation platform for multiple teams.
• Develop benchmark suites and model comparisons.
• Establish evaluation standards for metrics, splits, and calibration.
• Design reproducible pipelines with versioned data.
• Lead a small team of evaluation specialists.
• Stay hands-on with Python, experiments, and methodology.

Skills

Python

SQL

Statistical analysis

Experiment design

Leadership

Team management

Snowflake

Databricks

scikit-learn

Tools

ML platforms

Job description

Our partner is looking for an Evaluations Team Lead based in Switzerland.

As an Evaluations Team Lead, you will shape how advanced AI models are evaluated, validated, and improved in production environments. You will lead the development of a shared evaluation platform that enables research, engineering, and customer-facing teams to measure model performance with confidence. Your work will establish rigorous standards for benchmarking, statistical validation, data quality, and reproducibility. Combining hands-on technical leadership with people management, you will build a high-performing team while remaining closely involved in code, experiments, and methodology. You will assess predictive quality, inference latency, and cost efficiency to guide research priorities and product releases. This is an opportunity to define evaluation practices from the ground up and directly influence the reliability and impact of next-generation enterprise AI.

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.