Jobs at Fundamental

Lead and grow an MLOps team, define the ML infrastructure roadmap, and architect scalable automated pipelines, low-latency model serving, feature stores, monitoring, and CI/CD to move research into production.

The Forward Deployed Full-Stack Engineer will create production-grade applications, manage technical relationships, and ensure seamless integration of AI models into client environments.

The role involves optimizing large-scale ML training, troubleshooting inefficiencies, and collaborating with researchers to enhance model efficiency and productionization.

The role involves deploying machine learning models for enterprise solutions, managing stakeholder relationships, and optimizing models for business impact.

Manage relationships with Fortune 100 companies, handling complex sales processes, collaborating with senior executives, and driving business outcomes.

Manage and scale strategic technology partnerships to enhance the reach of Fundamental's AI model NEXUS. Collaborate across teams, execute joint go-to-market strategies, and drive partner integrations while navigating complex partner ecosystems.

As an ML Researcher, you will design evaluation frameworks, manage evaluation infrastructure, and track model performance through internal and external benchmarks.

The Forward Deployed Full-Stack Engineer will develop production-grade applications, manage integrations, and ensure usability and reliability of AI solutions for clients.

This role involves reviewing, negotiating commercial contracts, managing the NDA process, and advising stakeholders. The attorney will build contracting infrastructure and coordinate with outside counsel on legal matters.

Work with enterprise customers to identify high-value tabular ML use cases, design production-grade architectures, prototype solutions, lead deployments, coordinate cross-functional teams, and codify reusable deployment patterns to scale adoption of the NEXUS tabular foundation model.

Design, build, and operate cloud infrastructure and Kubernetes clusters for GPU/ML workloads; implement GitOps, IaC (Terraform), CI/CD, monitoring, automation (Python/Bash), and cost-optimization alongside ML engineers.

The Data Research Engineer will enhance ML model development by identifying data sources, building ETL pipelines, and collaborating on data storage solutions.

Own end-to-end SDLC and toolset standardization across Applied AI, Engineering, and Research. Formalize FDE-to-product handoffs, align APIs and technical specs, run cross-team operating rhythms, remove delivery blockers, and provide leadership with accurate forward capacity and delivery accountability.

Build and maintain Extensions backend for enterprise LTM products: design distributed, reproducible, observable workflows; optimize latency and throughput; collaborate with data scientists; drive architectural decisions; write high-quality, tested Python code; and document best practices for production-scale deployments.

Research, develop, and productize data-science methods to improve NEXUS predictive performance on enterprise tabular datasets. Implement production-quality Python components, design experiments and benchmarks, handle real-world tabular data challenges, collaborate with Research and Engineering, and validate approaches on customer datasets to drive product features.

Develop and optimize large neural-network tabular models in Python. Profile training and inference pipelines, improve memory efficiency, latency, and throughput. Implement performance-critical components in Rust (and optionally C++), ensure numerical stability and reproducibility, collaborate with researchers to productionize capabilities, and maintain tests and documentation.

Contribute to designing, implementing, and evaluating ML models, ensuring performance across the entire lifecycle while facilitating rapid development and testing of new ideas.

The Solutions Architect leads technical discovery, designs solutions, and guides enterprise customers through deploying predictive applications using a foundational AI model.

Build and maintain front-end and back-end systems for model interaction and large-scale inference. Develop APIs, session persistence, event-driven architectures, database integrations (Snowflake/Databricks), and monitoring/retry mechanisms. Collaborate with MLOps/DevOps to optimize deployment and ensure reliable, scalable model-serving workflows.

The role involves building the global Energy business by executing commercial strategies, closing large deals, maintaining executive relationships, and leading a global team in a collaborative environment.