Fundamental Research Labs

Principal forward-deployed data scientist who deploys and validates machine-learning solutions for Houston-based oil and gas customers, proves business value, and translates customer needs into product improvements. Requires 7+ years of technical experience, advanced statistical expertise, customer-facing experience, and strong production ML skills.

Build and manage scalable MLOps infrastructure, automated ML pipelines, model serving, and monitoring for a Large Tabular Model (LTM) at an enterprise AI company. Requires 5+ years MLOps/DevOps experience with Kubernetes, PyTorch/TensorFlow, and cloud infrastructure.

Lead and mentor an MLOps team to build scalable ML infrastructure, automated pipelines, and low-latency model serving for large tabular models. Requires 7+ years MLOps experience including 3+ years leading teams, deep expertise in Kubernetes, model serving frameworks, and observability tools.

Research and develop data science methods to enhance NEXUS tabular model performance across enterprise datasets. Design production-grade Python components and run rigorous experiments to improve prediction on real-world structured data.

Develop and productize data science capabilities for enterprise AI platform. Own reliability, performance, and scalability of Extensions backend with focus on distributed workflows and production-grade Python systems.

Lead technical discovery, solution design, and end-to-end delivery for enterprise deployments of tabular foundation models. Partner with sales and engineering to scope, prototype, and productionize predictive applications for Fortune 100 customers.

Forward Deployed Data Scientist deploying NEXUS (Large Tabular Model) into customer production environments. Works end-to-end from data engineering and model benchmarking through last-mile integration and stakeholder communication.

Develop and optimize large neural network-based tabular models. Profile and rewrite performance-critical components in Rust and C++ to improve efficiency, latency, and throughput for enterprise AI systems.

Build and own the software infrastructure supporting large-scale tabular model research, from experimentation through production. The role requires 5+ years of software engineering experience, expert Python and PyTorch skills, strong architecture expertise, and familiarity with modern ML tooling and cloud infrastructure.

Builds and optimizes production inference pipelines for large tabular models using Triton Inference Server. Requires 5+ years in ML infrastructure, expert Python skills, and deep knowledge of inference frameworks and optimization techniques.

The role optimizes large-scale distributed training and inference for foundation models, focusing on profiling, parallelization, memory efficiency, and productionization. It requires strong Python skills, multi-GPU training experience, and expertise in modern ML architectures.

Owns and scales strategic technology partnerships with cloud providers, data platforms, and infrastructure vendors to drive NEXUS adoption. Requires 8+ years in technical partnerships (3+ in AI), cloud ecosystem knowledge, and cross-functional collaboration skills.

Leads DevOps team to architect cloud infrastructure, manage Kubernetes for GPU/ML workloads, implement IaC with Terraform and GitOps via ArgoCD. Requires 7+ years DevOps experience including 3+ in leadership, multi-cloud expertise, and strong CI/CD skills.

Develops full-stack systems for AI model interaction, including backend APIs for inference, scalable data pipelines, and responsive frontends. Requires 5+ years experience with Python/FastAPI, modern JS frameworks, event-driven architectures, and cloud data platforms.

Builds production-grade full-stack applications and integrations for enterprise AI model deployment, handling API, data visualization, security, and backend services in complex environments. Requires 5+ years full-stack experience with React/TypeScript, Python/Go backends, and Kubernetes.

Designs and maintains cloud infrastructure, Kubernetes clusters for GPU/ML workloads, implements GitOps with ArgoCD and Terraform IaC. Requires 5+ years DevOps experience, Kubernetes expertise, AWS/GCP proficiency, and Python.

Researches, develops, evaluates, and optimizes machine learning models across the full research lifecycle, with a focus on novel methods, scalable training, and efficient inference. Requires strong Python, software engineering, machine learning fundamentals, and experience with GPUs or TPUs and distributed training.