Latest ML Engineering jobs at Fundamental Research Labs

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.

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.