Machine Learning Solutions Engineer (ML + Infrastructure Focus)

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 Lightning is looking for a Machine Learning Solutions Engineer with a focus on ML and Infrastructure to join ou Sales team in New York. As a Machine Learning Solutions Engineer, you will operate at the intersection of machine learning, distributed systems, and cloud infrastructure. You will partner with customers to design and deploy end-to-end AI systems, spanning: Model development and training GPU infrastructure and cluster design Distributed inference and production deployment This role goes beyond traditional ML solutions engineering—you will act as a technical architect, helping customers make critical decisions across compute, orchestration, and system design. The role is hybrid out of one of our hub locations (New York City, San Francisco, Seattle) with an in-office requirement of at least 2 days per week and occasional team and company offsites. We are not able to provide visa sponsorship for this role at this time. What You’ll Do Customer Architecture & Technical Leadership Partner with customers to understand ML workloads, infrastructure constraints, and scaling requirements Architect end-to-end solutions across: Data pipelines (CPU → GPU workflows) Distributed training (multi-node, multi-GPU) High-throughput inference systems Translate business goals (latency, cost, throughput) into technical system design decisions GPU & Infrastructure Design Design and optimize workloads across GPU clusters (H100, H200, B200, etc.) Advise on: Training vs inference cluster design Interconnect choices (Ethernet vs Infiniband / RDMA vs Roce) Storage strategies (local NVMe vs networked / object storage) Model and optimize for: Tokens/sec, tokens/$ Throughput vs latency tradeoffs GPU utilization and scheduling efficiency Kubernetes & Platform Systems Design and support deployments on Kubernetes (EKS, GKE, on-prem clusters) Work with: GPU scheduling (time-slicing, MIG, bin-packing) Autoscaling and workload orchestration Helm-based deployments and multi-tenant environments Help customers balance: Raw Kubernetes flexibility vs platform abstraction (Lightning) Demos, POCs, and Execution Build and deliver technical demos and POCs that showcase: Distributed training workflows Scalable inference endpoints End-to-end ML pipelines on Lightning AI Scope and lead POCs aligned to customer success metrics (latency, cost, reliability) Cross-Functional Impact Act as the bridge between customers, product, and engineering Provide feedback on: Platform gaps in infrastructure, orchestration, and performance Emerging patterns in GPU usage and distributed systems Influence roadmap across ML workflows and infrastructure capabilities Enablement & Thought Leadership Create technical content Architecture guides (e.g., high-throughput LLM inference systems) Best practices for GPU utilization and scaling Educate customers on modern AI infrastructure patterns What You’ll Need ML + Systems Expertise 3–6+ years experience in: Machine Learning / AI Engineering Solutions Engineering / Sales Engineering / ML Consulting Strong understanding of: Training vs inference workloads Model optimization (quantization, batching, caching, etc.) GPU & Distributed Systems Experience working with: GPU clus