Jobs at Fundamental

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.

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 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.

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.

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.

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.

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.

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.

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.

Deploy and validate NEXUS in customer environments, perform end-to-end data and feature engineering, benchmark against baselines (XGBoost/LightGBM), optimize models for business impact, handle last-mile integrations (VPC/on‑prem/air-gapped), and translate field insights into product improvements while liaising with stakeholders.

The Social Media & Executive Brand Manager will enhance Fundamental's online presence, create engaging content for enterprise audiences, and develop strategies aligned with company initiatives.

The ABM Manager will create and lead account-based marketing strategies, coordinate campaigns, and enhance engagement with high-value clients and prospects across various channels and events.

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.

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.