Staff Research Engineer, Scientific Computing and ML/Physics Infrastructure

Your Impact at LILA Lila Sciences is seeking a Research Engineer, Scientific Computing and ML/Physics Infrastructure to help turn promising research tools into robust, scalable systems. This role bridges research and production: you will work with scientists and ML researchers who can prototype useful tools, then help make those tools efficient, distributed, fault tolerant, and usable across Lila's compute environments. The Molecular Intelligence team is building ML and physics-based infrastructure for drug discovery, including biophysics workflows, computational chemistry tools, cofolding models, low-data learning systems, simulation workflows, and agent-usable scientific pipelines. We need an engineer who can improve code quality, architecture, GPU efficiency, cluster portability, and operational reliability without slowing down research velocity. What You'll Be Building Take research tools, prototypes, and scientific workflows developed by scientists or academic-style researchers and make them scalable, efficient, and maintainable. Collaborate directly with computational biophysics, computational chemistry, and machine learning scientists to turn research workflows into scalable agent-usable systems. Build and support ML and physics infrastructure for model training, molecular simulation, data processing, and agent-executed scientific workflows. Ensure workflows run reliably across multiple clusters and compute environments. Improve GPU utilization, distributed execution, throughput, fault tolerance, and reproducibility for ML and scientific workloads. Architect larger-scale systems around research code, including job orchestration, retry behavior, monitoring, artifact handling, and workflow traceability. Optimize ML, physics, and pipeline code for performance and scalability. Maintain development and execution environments across local, cloud, and GPU-based systems. Package scientific tools into reusable services, workflows, or APIs that can be used by researchers, pipelines, and AI agents. Partner with research, platform, and infrastructure teams to bridge exploratory scientific work with reliable engineering systems. Document systems clearly and establish pragmatic engineering patterns for research teams. What You'll Need to Succeed Strong software engineering skills in Python and experience working with ML, scientific computing, or simulation codebases. Experience building, scaling, or operating distributed systems for research, ML, physics, simulation, or data-intensive workloads. Practical knowledge of GPU computing, performance profiling, distributed execution, and failure modes in large-scale workloads. Experience with PyTorch, JAX, CUDA-aware workflows, or related ML/scientific computing frameworks. Practical knowledge of Linux, Docker or containers, dependency management, and reproducible development environments. Experience with orchestration, scheduling, or distributed execution systems such as Kubernetes, Slurm, Ray, Flyte, Argo, or similar tools. Ability to take prototype-quality research code and improve its architecture, scalability, reliability, and maintainability. Strong debugging skills across code, environments, infrastructure, data pipelines, and compute clusters. Ability to work directly with researchers, understand ambiguous technical needs, and convert them into robust engineering solutions.