Browse Jobs
By Role
By City
Posted May 13, 2026
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:
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 our New York City office hub, with an in-office requirement of at least 3 days per week and occasional team and company offsites. We are not able to provide visa sponsorship for this role at this time.
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
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
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)
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)
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
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.)
Experience working with:
GPU clusters (NVIDIA stack preferred)
Distributed training or inference systems
Familiarity with:
NCCL, CUDA, or GPU performance profiling
Networking concepts (RDMA, Roce, Infiniband, high-throughput systems)
Hands-on experience with:
Kubernetes (EKS, GKE, or on-prem)
Slurm
Containerization (Docker)
Exposure to:
GPU scheduling in Kubernetes environments
Multi-tenant or production ML deployments
Strong Python skills (PyTorch preferred)
Experience building:
ML pipelines
APIs or inference services
Familiarity with Lightning AI, PyTorch Lightning, or similar frameworks is a plus
The annual base pay range for this role is $150,000 - $195,000, in addition to a variable pay component and meaningful equity. ## Benefits and Perks
We offer a comprehensive and competitive benefits package designed to support our employees’ health, well-being, and long-term success. Benefits may vary by location, team, and role. Benefits include:
At Lightning AI, we are committed to fostering an inclusive and diverse workplace. We believe that diverse teams drive innovation and create better products. We provide equal employment opportunities to all employees and applicants without regard to race, color, religion, gender, sexual orientation, gender identity, national origin, age, disability, veteran status, or any other protected characteristic. We are dedicated to building a culture where everyone can thrive and contribute to their fullest potential.
Don't want to apply yourself?
Our team writes your resume, applies for you, preps you for interviews, and negotiates your offer.