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.

Our Values

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Move Fast: We act with speed and precision, breaking down big challenges into achievable steps.

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Focus: We complete one goal at a time with care, collaborating as a team to deliver features with precision.

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Balance: Sustained performance comes from rest and recovery. We ensure a healthy work-life balance to keep you at your best.

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Craftsmanship: Innovation through excellence. Every detail matters, and we take pride in mastering our craft.

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Minimal: Simplicity drives our innovation. We eliminate complexity through discipline and focus on what truly matters.

What We're Looking For

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 can be based out of our New York City or San Francisco office, with an in-office requirement of at least 2-3 days per week and occasional team and company offsites. We are not able to provide visa sponsorship for this role at this time. The annual base pay range for this role is $150,000 - $195,000, in addition to a variable pay component and meaningful equity.

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:

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• 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

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Interconnect choices (Ethernet vs Infiniband / RDMA vs Roce) Storage strategies (local NVMe vs networked / object storage)

Model and optimize for: Tokens/sec, tokens/$

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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)

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Autoscaling and workload orchestration Helm-based deployments and multi-tenant environments

Help customers balance: Raw Kubernetes flexibility vs platform abstraction (Lightning)

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Demos, POCs, and Execution

Build and deliver technical demos and POCs that showcase:

Distributed training workflows

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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

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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

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Solutions Engineering / Sales Engineering / ML Consulting

Strong understanding of: Training vs inference workloads

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Model optimization (quantization, batching, caching, etc.)

GPU & Distributed Systems

Experience working with:

GPU clusters (NVIDIA stack preferred)

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Distributed training or inference systems

Familiarity with: NCCL, CUDA, or GPU performance profiling

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Networking concepts (RDMA, Roce, Infiniband, high-throughput systems)

Kubernetes & Cloud Platforms

Hands-on experience with:

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• Kubernetes (EKS, GKE, or on-prem)

• Slurm

Containerization (Docker)

Exposure to: GPU sche