Jobs at Fundamental

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.

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.

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.

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.