Jobs at Fundamental

Founding Product Designer will establish product design, build an AI-native design system, and lead UX/UI for enterprise platform and agentic products. Responsibilities include user research, product discovery, interaction design, prototyping, usability testing, and translating complex AI and data-science workflows into intuitive experiences. The role partners closely with Product, Engineering, and AI teams across CLI, API, and graphical interfaces, while shaping design strategy, standards, accessibility, implementation quality, and product direction.

Owns customer outcomes for enterprise AI deployments by identifying high-value prediction use cases, defining success metrics, translating model performance into business value, driving adoption, and shaping expansion roadmaps. Partners with forward-deployed engineers to remove data and stakeholder barriers, trains users, communicates with senior customer leaders, and turns deployment patterns into reusable playbooks. Requires extensive customer engagement and travel exceeding half the working time.

Own the strategy, roadmap, delivery, adoption, economics, and reliability of NEXUS inference products. Partner with engineering, research, applied AI, customers, and go-to-market teams to define deployment offerings, enterprise administration, governance, pricing, and measurable product outcomes. Translate inference performance, capacity, security, compliance, latency, throughput, and cost tradeoffs into product decisions while driving cross-functional delivery from early development through launch.

Own the roadmap and product decisions for data science products powered by NEXUS. Partner with data scientists, engineering, research, design, and applied AI teams to build self-service workflows, AI agents, MLOps capabilities, and predictive model monitoring. Conduct customer discovery, define requirements and success metrics, prioritize roadmap decisions using data and user evidence, and guide products from discovery through launch and iteration.

Manage the full employee journey across onboarding, benefits, career milestones, people operations, and employee support in a distributed global organization. Maintain People Operations systems, improve workflows, automate repetitive tasks, analyze team data, create self-service documentation, support compliance, and enhance employee experience. Partner with Talent on candidate-to-employee transitions and contribute to performance development, engagement, and distributed culture initiatives.

Conduct research across the full machine learning lifecycle, including ideation, model design, implementation, evaluation, training, inference optimization, and deployment. Develop novel machine learning methods and foundation models, establish evaluation protocols, scale training with GPUs or TPUs, and improve research engineering infrastructure for rapid experimentation.

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

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

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.

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.

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.

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.

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.

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.