Who We AreLightning 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 ForLightning AI is seeking a
Storage Infrastructure Engineer to join our Infrastructure Engineering team.
In this role, you will focus on building and operating the storage systems that power large-scale AI/ML training, inference, and HPC workloads. You will work at the intersection of software, hardware, and operations-developing automation, improving reliability, and scaling distributed storage systems across our bare-metal infrastructure.
You will help own the data plane of our storage infrastructure, supporting high-throughput, low-latency data access for some of the most demanding AI workloads. You'll play a key role in managing and evolving our storage stack (including VAST and S3-compatible systems like Ceph), ensuring performance, reliability, and efficiency at scale.
We're flexible on location for this team. This role can work hybrid out of one of our US-based hubs (Seattle, NYC, or SF) or fully remote within the U.S., with occasional company and team offsites. We are not able to provide visa sponsorship for this position at this time.
What You'll DoStorage Systems & Infrastructure- Operate and scale distributed storage systems, including VAST and S3-compatible object storage (e.g., Ceph)
- Improve performance, reliability, and efficiency of storage systems supporting large-scale AI/ML workloads
- Troubleshoot complex storage and data path issues across hardware and software layers
- Optimize storage performance to support high-throughput, low-latency AI training and inference workloads
Automation & Tooling- Build and maintain automation for provisioning, managing, and monitoring storage infrastructure
- Develop Python-based tools and workflows to reduce manual operational overhead
- Improve lifecycle management of storage clusters, from deployment through maintenance and scaling
Systems & Operations- Manage and operate Linux-based systems in production, including bare-metal environments
- Partner with infrastructure and data center teams on hardware bring-up, upgrades, and issue resolution
- Support capacity planning, utilization tracking, and forecasting for storage systems
- Leverage monitoring and telemetry to diagnose issues and improve system performance and reliability
Cross-Functional Collaboration- Work closely with Infrastructure Engineering, Network Engineering, and Platform teams to integrate storage into the broader platform
- Contribute to design discussions around new infrastructure deployments and scaling strategies
- Help define best practices for operating storage systems in high-performance computing environments
What You'll NeedRequired Qualifications- 5+ years of experience in infrastructure engineering, systems engineering, or related roles
- Hands-on experience operating distributed storage systems (e.g., VAST, Ceph, or similar)
- Strong Linux systems experience in production environments
- Proficiency in Python or similar scripting/programming languages for automation
- Experience working with bare-metal infrastructure and hardware-oriented systems
- Ability to debug complex issues across system boundaries (storage, OS, hardware, networking)
- Experience with storage networking protocols (e.g., NFS or similar)
- Experience with capacity planning, monitoring, and performance tuning
Ideal Experience- Experience with VAST storage systems in production environments
- Experience operating S3-compatible object storage at scale
- Data center operations experience, including working with physical hardware
- Familiarity with AI/ML or HPC workloads and their storage requirements
- Background in high-performance or low-latency distributed systems
- Familiarity with high-performance data transfer technologies (e.g., RDMA, GPU Direct Storage)
- Experience supporting GPU-based workloads or large-scale compute clusters
CompensationWe are committed to offering competitive compensation that reflects the value each team member brings to our mission. Final offers are based on factors such as experience, skills, geographic location, and role expectations. In addition to base salary, our total rewards package for eligible roles includes a discretionary bonus, a meaningful equity component, and comprehensive benefits.
The anticipated annual base salary range for this role is:
$180,000-$200,000 USD
Benefits and PerksWe 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.
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