Fundamental

AI

MachineLearningEngineer(ForwardDeployed)

Tokyo, Japan; Amagasaki, Japan; Yokosuka, Japan FULL TIME
Market Sentiment
HIGH DEMAND

Neural analysis suggests this role is
optimal for Mid candidates.

The Brief

“Machine Learning Engineer (Forward Deployed) at Fundamental. Skills: Machine Learning, Data Science, Deployment, Customer Collaboration. Deploy into production use cases. Rigorous head-to-head benchmarking”

What You'll Achieve.

Proving value over legacy baselines; Proving value over net new use cases; Ensuring solutions run securely; Achieve definitive ROI

Industry & Context.

AI
Problems you'll solve

Address complex technical challenges; Identify right business problems

Eligibility Requirements

VPC integration, On-prem integration, Air-gapped integration

What They're Looking For.

Must Have

PhD / master in CS / Math / Stats or equivalent deep statistical literacy, 2+ years as a technical individual contributor (data scientist or software engineer), Experience with containerization (Docker), Writing performant APIs (FastAPI/Flask), Master the end-to-end pipeline, Deep understanding of data handling (PySpark, Pandas), Memory optimization, Demonstrated experience optimizing models for a specific business problem, Communication skills

Nice to Have

Experience with PyTorch, Cloud-native ML pipelines (AWS, GCP, Azure), Experience as a Forward Deployed Engineer, Staff Engineer, Machine Learning Engineer, Staff Data Scientist, Industry-based subject matter expertise

What You'll Do.

Deploy into production use cases

Rigorous head-to-head benchmarking

Execute data engineering

Execute feature engineering

Translate operational pain points

Translate data anomalies

Identify right business problems

Handle last mile integration

Align diverse stakeholders

Explain predictions to business users

How You'll Work.

Team & Collaboration

Collaborate with customers; Work in collaboration with research teams; Work in collaboration with product teams; Collaboration with Sales teams; Collaboration with Solution Architect teams

Communication Scope

Translate architectural nuances into clear business value

Full 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. ABOUT THE ROLE Fundamental is seeking a Forward Deployed Data Scientist/MLE to facilitate the adoption of NEXUS and collaborate with customers to address complex technical challenges. The Data Scientist is an integral part of our FDE team, which is dedicated to driving the successful deployment of Fundamental products and proving value over legacy baselines or net new use cases. They work hand-in-hand with customers from the Proof of Value stage to post-implementation, ensuring our solutions run securely in the client's production heartbeat. In this role, you’ll manage customer relations involving multiple stakeholders (IT, C-suite, and data science teams) and function as a key bridge, translating field insights into our product roadmap. KEY RESPONSIBILITIES - You’ll individually help deploy into production use cases with considerable business impact, moving from "science experiments" to definitive ROI - You’ll work on rigorous head-to-head benchmarking against client baselines (XGBoost, LightGBM), executing the work of data engineering, feature engineering, and validation - You’ll work in collaboration with our research and product teams to translate operational pain points and data anomalies i

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