Apply on Employer Site

Emerald AI · 16 hours ago

Software Engineer - Backend/Distributed Systems

San Francisco Bay Area

Full-time

Hybrid

Mid, Senior Level

5+ years exp

Emerald AI is focused on scaling AI sustainably while addressing power constraints in data centers. They are seeking a Distributed Systems / Backend Engineer to drive architectural decisions, optimize performance, and build foundational infrastructure for their backend platform.

Artificial Intelligence (AI)EnergyPower GridSaaSSoftware

Responsibilities

Build scalable and reliable services, and APIs, used by both internal teams and enterprise customers

Continuously optimize performance across GPU clusters, cloud infrastructure, and backend systems

Implement observability, reliability, and security best practices

Proactively identify technical debt, performance bottlenecks, and strategic opportunities for architectural improvements

Collaborate closely with engineers, product managers, and energy domain experts to translate system constraints into scalable solutions

Guide other engineers through technical design reviews, mentorship, and high-quality code standards

Qualification

Backend systems developmentDistributed systems architectureMicroservices architecturePerformance optimizationObject-oriented programmingAI/ML frameworksCluster resource management

Required

5+ years of software engineering experience with a focus on backend or distributed systems development

Extensive experience designing, building, and maintaining microservices architectures and distributed systems at scale

Advanced system design capabilities with demonstrated expertise in performance optimization, horizontal scaling, and high-availability patterns

Solid understanding of system performance, profiling, and optimization

Experience with object-oriented programming languages such as but not limited to: Python, Go, or Java

Preferred

Experience with AI/ML frameworks or libraries (e.g., TensorFlow, PyTorch) and familiarity with MLOps tools or workflows for deploying machine learning models into production

Understanding of cluster resource management and scheduling systems (e.g., experience with Slurm or similar job schedulers) and principles of efficient resource provisioning for compute-intensive workloads