---
title: 'Evaluations Team Lead at Fundamental'
canonical: 'https://feeny.ai/job/evaluations-team-lead-fundamental-europe-1ny9rqh4eead'
type: 'job'
last_seen: '2026-10-14'
---

# Evaluations Team Lead at Fundamental

- **Company:** Fundamental
- **Location:** Europe
- **Employment:** full-time
- **Work type:** remote
- **Posted:** 2026-10-09
- **Last confirmed live:** 2026-10-14
- **Apply:** https://jobs.ashbyhq.com/fundamental/f0e27c50-261a-42cb-9fe7-b3b2e9d6a2ce

## Job description

## About Fundamental

Fundamental is an AI research lab pioneering the future of enterprise decision-making. Our flagship model, NEXUS is the world's most powerful Large Tabular Model (LTM) - purpose-built for the structured records that contain trillions of dollars in business value. With $275m in funding from leading investors and trusted by Fortune 100 companies, Fundamental is giving businesses the Power to Predict.

At Fundamental, you'll work on unprecedented technical challenges in foundation model development and build technology that transforms how the world's largest companies make decisions. This is your opportunity to be part of a category-defining company from the ground-up. Join the team defining the future of enterprise AI.

## About the role

NEXUS is already in production, and we are working to substantially improve its predictive quality, latency, and cost efficiency. You will lead the team that owns how NEXUS is measured, building the shared evaluation platform that engineering, research, and Applied AI all rely on: consistent benchmarks, curated datasets, and standards for metrics, data splits, leakage prevention, and benchmark contamination that hold up under scrutiny. Research needs to trust results before committing compute to it, engineering needs regressions caught before they reach production, and when a customer's own data science team benchmarks NEXUS against their own models, your platform is what Applied AI relies upon. You will benchmark NEXUS against competing approaches, using fair tuning budgets, data access, and latency measurement protocols, and turn what you find into research priorities and release recommendations. This is a player-coach role: you will hire and manage a small team while staying hands-on with the code, the experiment design, and the methodology yourself. There is no evaluation function to inherit here - what you build becomes the standard the rest of the company measures NEXUS against.

## Key responsibilities

- Build a shared evaluation platform for engineering, research, and Applied AI. Continuously add and maintain models and curated datasets so teams can run benchmarks and investigate results independently.
- Define evaluation standards for metrics, data splits, leakage prevention, calibration, uncertainty, and benchmark contamination.
- Build reproducible pipelines with versioned inputs and artifacts, and integrate regression checks into research and release workflows.
- Benchmark NEXUS against competing approaches using fair tuning budgets, data access, compute, and latency measurement protocols.
- Support Applied AI’s customer POC evaluations with tooling, methodological guidance, and analysis.
- Measure predictive quality, latency, and cost across deployment configurations, task types, and dataset characteristics.
- Turn findings into research priorities, release recommendations, and evidence-backed customer improvement plans.
- Hire and develop the team, set priorities, and stay hands-on with code and experimental design.

Must have

- Experience owning evaluation for tabular ML systems used in production or consequential customer decisions.
- Strong statistical judgment: choosing metrics and validation schemes, estimating uncertainty, comparing models across datasets, and accounting for repeated experimentation.
- Practical experience finding leakage in preprocessing, feature construction, joins, temporal dependencies, and related entities across splits.
- Strong Python and SQL skills, familiarity with scikit-learn and gradient-boosted trees, and experience building reliable ML tooling or platforms used by other teams.
- Experience designing fair model comparisons, including hyperparameter search, resource budgets, and end-to-end latency measurement.
- Prior people management experience, including hiring, technical coaching, and performance feedback, while remaining technically involved.
- Clear written and spoken communication with researchers, engineers, and customer data scientists, including the willingness to challenge claims the evidence does not support.

## Nice to have

- Experience evaluating tabular foundation models or AutoML systems.
- Experience measuring how model optimisations affect predictive quality and inference performance.
- Experience with relational or multi-table data, and Snowflake or Databricks environments.
- Experience evaluating automated or agent-driven ML workflows, including failures that aggregate metrics can hide.

## Benefits

- Competitive compensation with salary and equity
- Comprehensive health coverage for you and your dependents
- Paid parental leave for all new parents, inclusive of adoptive and surrogate journeys
- Relocation support for employees moving to join the team in one of our office locations
- A mission-driven, low-ego culture that values diversity of thought, ownership, and bias toward action

## About Fundamental

## Company Overview
- **One-liner**: Fundamental builds foundation models purpose‑designed for tabular data, enabling enterprises and governments to make accurate predictions and confident decisions.
- **Entity Type**: Private (Series A)
- **Headquarters**: San Francisco, California, United States
- **Founded**: 2024
- **Founders**: Jeremy Fraenkel (CEO), Gabriel Suissa (Co‑Founder); built by DeepMind alumni

## Core Business
- Primary industry: Artificial intelligence / Enterprise AI (Large Tabular Models)
- Target customers: Large enterprises and government agencies (Fortune 100 clients)
- Mission: “To create a world of abundance by giving humanity the Power to Predict.”

## Products & Services
- **[NEXUS – Large Tabular Model (LTM)]**: A deterministic foundation model pre‑trained on billions of tables to ingest structured data (rows/columns) and capture complex, non‑linear feature interactions. Deployable with one line of code, integrates deeply with AWS, and designed for enterprise‑grade privacy and security. Unlike LLMs, NEXUS can reason over extremely large datasets (billions of rows) and provides consistent answers every time.

## Market Standing
- **Valuation/Market Cap**: $1.4 billion post‑money (as of February 2026)
- **Key Metric**: Total funding raised – $255 million (Series A: $225M led by Oak HC/FT, Valor Equity Partners, Battery Ventures, Salesforce Ventures; Angel round: $30M from investors including Perplexity CEO Aravind Srinivas, Brex co‑founder Henrique Dubugras, Datadog CEO Olivier Pomel)
- **Notable Investors/Partners**: Oak HC/FT, Valor Equity Partners, Battery Ventures, Salesforce Ventures, Hetz Ventures; strategic partnership with Amazon Web Services (AWS)
- **Growth Signals**: Emerged from stealth in February 2026 with seven‑figure contracts from Fortune 100 clients; headcount grew from founding to 46 employees with +28% monthly growth; operates globally (San Francisco, Barcelona, Israel, UK, etc.)

## Competitive Advantages
- **Deterministic & scalable**: Unlike transformer‑based LLMs, NEXUS is deterministic and can analyze tables with billions of rows without context‑window limitations.
- **Purpose‑built for tabular data**: Captures non‑linear feature interactions that LLMs miss, unlocking trillions of dollars in value from structured data.
- **Research‑led with academic rigor**: Team includes DeepMind alumni; model built on a non‑transformer architecture designed specifically for real‑world tabular data.
- **One model, many use cases**: Replaces armies of data scientists with a single foundation model that works across industries (finance, healthcare, energy, etc.).

## Strategic Focus
- **Expansion**: Deepening the AWS integration and scaling NEXUS across more Fortune 500/global government clients.
- **Talent acquisition**: Hiring across research (Barcelona), engineering (Europe, SF), and commercial roles (SF, Houston) to accelerate model development and go‑to‑market.
- **Category creation**: Pioneering the “Large Tabular Model” (LTM) category to differentiate from LLM companies and establish leadership in predictive AI for structured data.

## Why Work Here
- **Solve hard problems**: Work on groundbreaking research and engineering for a new modality of AI (tabular data) that most labs ignore.
- **Exceptional team**: Collaborate with DeepMind alumni and top researchers from Cohere, AI21 Labs, Google DeepMind, Mistral AI, and others.
- **Global and flexible**: Hubs in San Francisco (HQ), Barcelona (research), and remote‑friendly roles across Europe and the US.
- **Rapid growth**: Joining a well‑funded, high‑valuation startup at Series A stage with real revenue and Fortune 100 customers—clear trajectory.
- **Mission‑driven**: “Power to Predict” aims to transform decision‑making in enterprises and governments, offering tangible societal impact.
- **Culture**: Described as rigorous, ambitious, and supportive of deep research and patient engineering.

## Sources
1. [fundamental.tech](https://fundamental.tech/)
2. [fundamental.tech/company](https://fundamental.tech/company)
3. [fundamental.tech/careers](https://fundamental.tech/careers)
4. [TechCrunch - Funding announcement](https://techcrunch.com/2026/02/05/fundamental-raises-255-million-series-a-with-a-new-take-on-big-data-analysis/)
5. [LinkedIn Company Page](https://www.linkedin.com/company/fundamentalhq)

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