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 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.
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
Familiarity with Lightning AI, PyTorch Lightning, or similar frameworks is a plus
Customer-Facing Excellence
Ability to:
Explain complex infrastructure and ML tradeoffs clearly
Run technical discovery and uncover quantifiable success metrics
Experience working cross-functionally with:
Sales, product, and engineering teams
Compensation
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:
Comprehensive medical, dental and vision coverage (U.S.); Private medical and dental insurance (U.K.)
Retirement and financial wellness support (U.S.); Pension contribution (U.K.)
Generous paid time off, plus holidays
Paid parental leave
Professional development support
Wellness and work-from-home stipends
Flexible work environment
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.
