---
title: 'MLOps Engineer at Fundamental'
canonical: 'https://feeny.ai/job/mlops-engineer-fundamental-europe-wsjqr08qefv8'
type: 'job'
last_seen: '2026-09-09'
---

# MLOps Engineer at Fundamental

- **Company:** Fundamental
- **Location:** Europe
- **Employment:** full-time
- **Work type:** remote
- **Posted:** 2026-07-20
- **Last confirmed live:** 2026-09-09
- **Apply:** https://jobs.ashbyhq.com/fundamental/f66822a3-fd9f-438b-8532-e996e3932e3c

## Job description

## ABOUT FUNDAMENTAL

Fundamental is an AI company pioneering the future of enterprise decision-making. Founded by DeepMind alumni, Fundamental has developed NEXUS – the world's most powerful Large Tabular Model (LTM) – purpose-built for the structured records that actually drive enterprise decisions. Backed by world class investors and trusted by Fortune 100 companies, Fundamental unlocks trillions of dollars of value by 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.

## KEY RESPONSIBILITIES

- Develop and manage scalable, automated machine learning pipelines, CI/CD workflows, and orchestration frameworks
- Design and implement robust model serving infrastructure using platforms like TorchServe, TensorFlow, Triton etc.
- Develop scalable inference architectures optimized, with ultra-low latency and high throughput
- Ensure seamless model deployment by implementing A/B testing, canary releases, and rollback capabilities
- Develop logging, alerting, and monitoring solutions to track model development, and reliability
- Improve GPU usage, enable autoscaling, and streamline resource allocation to boost efficiency
- Design, implement, and maintain feature stores, robust data pipelines, and scalable storage solutions to efficiently handle large volumes of data

## MUST HAVE

- Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field (or equivalent practical experience)
- 5+ years of experience as MLOps engineer or DevOps roles, working with MLOps platforms (MLflow, WandB etc..) and frameworks (PyTorch, TensorFlow etc..)
- Experience building and designing MLOps infrastructure from the ground up
- Experience with model serving frameworks (TorchServe, TensorFlow Serving, Triton, KServe etc..) for high scalability and low latency inference
- Experience in building and managing data pipelines to support both model training and inference
- Experience with Kubernetes on a major cloud provider (AWS, GCP, or Azure) and with infrastructure as code (e.g. Terraform, Helm, GitOps)
- Strong software engineering skills in Python, Bash, and Go, with a focus on writing clean, maintainable, and scalable code
- Experience in AI/ML systems security, compliance, and model governance
- Proficient with observability and monitoring tools, such as Prometheus, Grafana, Datadog, and OpenTelemetry

## NICE TO HAVE

- Experience with ML workflow tooling (MLflow, Kubeflow, or similar)
- Experience with FastAPI and Backend applications
- Familiarity with data platforms like Databricks or Snowflake
- Exposure to SRE practices or cloud security certifications
- Hands-on experience with Prometheus, Grafana, or Datadog

## 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)

## Other roles at Fundamental

- [Principal Forward Deployed Data Scientist - Oil & Gas, Houston](https://feeny.ai/job/principal-forward-deployed-data-scientist-oil-gas-houston-fundamental-houston-m4rpv3jwyt9c) — Houston Texas, United States
- [MLOps Team Lead](https://feeny.ai/job/mlops-team-lead-fundamental-europe-mznfy9kpx57f) — Europe
- [Data Scientist - Extensions](https://feeny.ai/job/data-scientist-extensions-fundamental-europe-tvdb0pyrtwz7) — Europe
- [Backend Engineer - Extensions](https://feeny.ai/job/backend-engineer-extensions-fundamental-europe-yt2zm9asfe5c) — Europe
- [Solutions Architect](https://feeny.ai/job/solutions-architect-fundamental-san-francisco-drvtjmaz4c6f) — San Francisco, CA
- [Data Scientist (Forward Deployed)](https://feeny.ai/job/data-scientist-forward-deployed-fundamental-united-states-sz4mv2v5cxa1) — United States
- [Solutions Architect](https://feeny.ai/job/solutions-architect-fundamental-japan-j936hdcy011n) — Japan
- [Applied AI Engineer](https://feeny.ai/job/applied-ai-engineer-fundamental-europe-frcgd92v43ke) — Europe
- [SWE, ML](https://feeny.ai/job/swe-ml-fundamental-barcelona-h3tsrzya2st2) — Barcelona, Spain
- [Model Serving Engineer](https://feeny.ai/job/model-serving-engineer-fundamental-europe-9ev1t2teb0f9) — Europe