Lead Research Engineer

Who We Are Lightning AI is the company behind PyTorch Lightning. Founded in 2019, we build an end-to-end platform for developing, training, and deploying AI systems—designed to take ideas from research to production with less friction. Through our merger with Voltage Park, a neocloud and AI Factory, Lightning AI combines developer-first software with cost-efficient, large-scale compute. Teams get the tools they need for experimentation, training, and production inference, with security, observability, and control built in. We serve solo researchers, startups, and large enterprises. Lightning AI operates globally with offices in New York City, San Francisco, Seattle, and London, and is backed by Coatue, Index Ventures, Bain Capital Ventures, and Firstminute. What We're Looking For We are seeking a highly skilled Lead Research Engineer to lead optimization efforts for training and inference workloads running on Lightning AI infrastructure. This role sits at the intersection of ML systems, AI infrastructure, performance engineering, and practical research. You’ll drive improvements across models, inference systems, and platform infrastructure to improve performance, scalability, and reliability for real-world AI workloads. This is a highly cross-functional role that combines deep technical leadership with hands-on implementation. Successful candidates are comfortable operating broadly across the stack — from model behavior and inference systems to distributed infrastructure and developer tooling — while partnering closely with customers and internal engineering teams to solve complex AI systems challenges at scale. This role is based in one of our hubs (NYC, SF, London, or Seattle — NYC and London are preferred), with a minimum of 2 in-office days per week and occasional team and company offsites. What You'll Do Lead optimization efforts for large-scale training and inference workloads across GPUs, accelerators, and distributed systems Partner directly with customers to analyze workloads, identify bottlenecks, and drive improvements in performance, scalability, and reliability of deployed AI systems Architect and improve inference pipelines, model serving systems, and performance-oriented tooling for production AI workloads Lead the design and implementation of profiling, debugging, and observability tools to analyze model execution and guide optimization strategies Drive performance improvements across the software stack through clean APIs, automation, and seamless integration with the Lightning ecosystem Collaborate cross-functionally with infrastructure, product, and research teams to shape technical direction and improve the developer and user experience for AI workloads running on Lightning Partner with hardware vendors and ecosystem partners to support efficient execution across diverse compute backends (NVIDIA, TPU, and emerging accelerators) Contribute technical leadership to open-source projects through new features, tooling improvements, documentation, and community engagement Stay current with advancements in large-scale inference, distributed training, and ML systems optimization, and help guide adoption of new technologies and approaches What You’ll Need Required Qualifications Strong expertise with deep learning frameworks such as PyTorch Significant experience working with large-scale training or inference workloads Strong understanding of distributed systems and parallelism strategies (data/model/pipeline parallelism, checkpointing, elastic scaling, distributed inference) Strong software engineering fundamentals, including designing APIs, building tooling, debugging complex systems, and shipping production-quality code Experience leading or driving performance optimization efforts across ML systems, infrastructure, or distributed workloads Hands-on experience with inference optimization techniques such as quantization, mixed precision, speculative decoding, memory-efficient training, or throughput/latency optimiza