Senior Software Engineer, Storage Platform

Moonlite delivers high-performance AI infrastructure for organizations running intensive computational research, large-scale model training, and demanding data processing workloads.We provide infrastructure deployed in our facilities or co-located in yours, delivering flexible on-demand or reserved compute that feels like an extension of your existing data center. Our team of AI infrastructure specialists combines bare-metal performance with cloud-native operational simplicity, enabling research teams and enterprises to deploy demanding AI workloads with enterprise-grade reliability and compliance.

You will be central to building our high-performance storage platform that enables research teams to work with massive datasets for AI training, large-scale simulations, and data-intensive computational workloads. Working closely with product, your platform team members, and infrastructure specialists, you’ll design and implement storage systems that deliver high-throughput data access, manage multi-TB to PB-scale datasets, and provide the data protection and lifecycle management required by enterprise and research organizations.    

• Storage Platform Architecture: Design and build scalable storage orchestration systems supporting block, object, and file storage optimized for AI training datasets, model checkpoints, simulation data, and large-scale data processing pipelines. 
• Research Cluster Storage: Design and implement storage systems for research computing environments including Kubernetes and SLURM clusters, enabling shared datasets, persistent storage for distributed training, model checkpoints, and high-throughput data access for batch processing workloads.
• High-Performance Data Access: Implement storage solutions that deliver consistent high-throughput and low-latency performance for demanding research workloads, including distributed training, large-scale simulations, and real-time data processing.
• Data Pipeline Engineering: Build robust data ingestion, processing, and movement systems that handle massive datasets, support efficient data loading for training pipelines, and enable seamless data access across compute infrastructure.
• Multi-Tiered Storage Orchestration: Build systems that coordinate across NVMe, SSD, and high-capacity storage tiers, placing and moving data based on access patterns and workload requirements.
• Enterprise Data Protection: Implement comprehensive backup, snapshot, replication, and disaster recovery systems that meet enterprise data protection requirements and support zero-data loss policies.
• Storage APIs & Integration: Create storage management APIs and SDKs that integrate seamlessly with compute platforms, research workloads, and financial data feeds.
• Observability and Performance: Build monitoring and optimization systems that ensure consistent storage performance, track capacity utilization, and provide visibility into data access patterns.  

• Experience: 5+ years in software engineering with proven experience building storage platforms, distributed storage systems, or data infrastructure for production environments.
• Kubernetes Storage & Container Orchestration: Strong familiarity with Kubernetes storage architecture, persistent volumes, storage classes, and CSI drivers. Understanding of pods, deployments, stateful sets, and how Kubernetes manages storage resources.
• Storage Systems Expertise: Deep understanding of storage architectures including block storage, object storage, file systems, distributed storage, and performance optimization techniques.
• Programming Skills: Expert-level Python proficiency. Experience with C/C++, Rust, or Go for performance critical storage components is highly valued.
• Linux & Systems Programming: Strong experience with Linux in production environments, including file systems, storage subsystems, and kernel-level storage interfaces
• Data Systems Engineering: Strong background in building data pipelines, large-scale data processing systems, and managing data lifecycle at scale.
• Experience: 5+ years in software engineering with proven experience building compute platforms, container orchestration, or distributed systems for performance-critical applications.
• Storage Systems Expertise: Deep understanding of storage architectures including NVMe, distributed storage, caching strategies, and performance optimization for latency-sensitive workloads.
• Programming Skills: Expert-level Python proficiency. Experience with C/C++,, Rust, Go for performance-critical components is highly valued.
• Data Systems: Strong background in building data pipelines, ETL processes, and large-scale data processing systems.
• Platform & API Design: Proven experience building storage platforms with multi-tenancy, data isolation, and enterprise-grade reliability features.
• Problem Solving & Architecture: Demonstrated ability to solve complex performance and scalability challenges while balancing pragmatic shipping with good long-term architecture considerations.
• Autonomy and Communication: Comfortable navigating ambiguity, defining requirements collaboratively, and communicating technical decisions through clear documentation.
• Commitment to Growth: Growth mindset with focus on learning and professional development.

• Background provisioning or managing storage for research computing environments (Kubernetes, SLURM, or HPC clusters)
• Experience with high-performance storage solutions and their APIs
• Background with object storage systems (S3-compatible APIs) and distributed file systems
• Knowledge of storage networking technologies (NVMe-oF, iSCSI, storage fabric protocols)
• Understanding of AI/ML data pipeline requirements and model training data access patterns
• Experience building storage solutions for regulated industries with audit and compliance requirements
• Knowledge of time-series databases and market data storage optimization is a plus
• Extra points for experience with financial services data infrastructure and regulatory data protection requirements

• Python, NVMe, Distributed Storage Systems, C/C++, Rust, Go, Kafka, PostgreSQL, Redis, S3 APIs, Docker, Kubernetes, Terraform, FastAPI, Time-Series Databases

• Build Next-Generation Infrastructure: Your work will create the platform foundation that enables financial institutions to harness AI capabilities previously impossible with traditional infrastructure.
• Hands-On Ownership: As an early engineer, you’ll have end-to-end ownership of projects and the autonomy to influence our product and technology direction.
• Shape Industry Standards: Contribute to defining how enterprise AI infrastructure should work for the most demanding regulated environments.
• Collaborate with Experts: Work alongside seasoned engineers and industry professionals passionate about high-performance computing, innovation, and problem-solving.
• Start-Up Agility with Industry Impact: Enjoy the dynamic, fast-paced environment of a startup while making an immediate impact in an evolving and critical technology space.

We offer a competitive total compensation package combining a competitive base salary, startup equity, and industry-leading benefits. The total compensation range for this role is $165,000 – $225,000, which includes both base salary and equity. Actual compensation will be determined based on experience, skills, and market alignment. We provide generous benefits, including a 6% 401(k) match, fully covered health insurance premiums, and other comprehensive offerings to support your well-being and success as we grow together.

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