ML Researcher: Enterprise AI & Foundation Models

Fundamental; United States; Remote; USD 150,000 - 230,000; Full time

Benefits offered by this job

Competitive compensation with salary

Equity

Health coverage for dependents

Parental leave

Relocation assistance

Job summary

Fundamental is seeking an AI research scientist to join the Research team, advancing breakthrough ML models for enterprise decision-making. You will help design, implement, and evaluate models end-to-end, from ideation to near-production deployment, focusing on scalable training and efficient inference.

The role emphasizes solid software engineering, collaboration with researchers and engineers, and publishing impactful work in AI conferences or open source projects where applicable.

Qualifications

• Experience with the full ML research cycle.
• Strong fundamentals of software engineering.
• Proficient in Python and ML frameworks (e.g., TensorFlow, PyTorch).
• Experience with the full lifecycle of AI model development.
• Experience with ML infrastructure frameworks and tools.
• Developing new ML methods, algorithms and models.
• GPUs (or TPUs) and distributed training.
• Scaling up models and training regimes.
• Knowledge of classical ML methods and algorithms.
• Deep Learning techniques.

Responsibilities

• Ideate, design, implement, and evaluate breakthrough ML models across the lifecycle.
• Ensure research code enables rapid development and testing of new ideas.
• Improve model performance through novel architectures and training methods.

Skills

ML Research

Python

Distributed Training

Software Engineering

Deep Learning

Tabular Data

Model Development

ML Infrastructure

GPUs/TPUs

Big Data

Education

BSc/MSc/PhD in CS/ML

Tools

TensorFlow

PyTorch

Kubernetes

Job description

Fundamental is seeking an AI research scientist to join the Research team, advancing breakthrough ML models for enterprise decision-making. You will help design, implement, and evaluate models end-to-end, from ideation to near-production deployment, focusing on scalable training and efficient inference.

The role emphasizes solid software engineering, collaboration with researchers and engineers, and publishing impactful work in AI conferences or open source projects where applicable.