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Own the AI strategy and expansion roadmap for a strategic oil and gas enterprise account. Build executive relationships, identify and prioritize prediction use cases, develop business cases and ROI models, secure sponsorship and budgets, orchestrate technical delivery teams, measure realized value, and turn successful deployments into broader programs and repeatable expansion frameworks. The role requires strong oil and gas expertise, commercial acumen, technical fluency, and regular engagement with customer sites and company offices.

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.

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 a software engineer in the research team, you will develop and maintain the core codebase, optimize workflows for researchers, and ensure production readiness while implementing best practices in ML software development.

Deploy machine learning solutions for enterprise Oil & Gas customers, benchmarking against existing models and delivering measurable ROI. Responsibilities include data engineering, feature engineering, validation, model optimization, data leakage prevention, and secure last-mile integrations across cloud, on-premises, and air-gapped environments. The role partners with customers, sales, research, product, and solution architecture teams while translating technical insights into business value and product roadmap priorities.

Build and operate scalable MLOps pipelines, model serving, and inference architectures. Implement CI/CD, monitoring, A/B/canary deployments, feature stores, GPU autoscaling, and secure governance for production ML systems.

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.

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

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