Software Engineer - Core Platform

Elorian•Palo Alto, CA

20h•$300,000 - $450,000•Hybrid

About The Position

The Software Engineer will design, optimize, and scale core systems that serve foundational visual reasoning models. The role owns backend and systems software supporting inference, backend services, distributed execution, deployments, distributed evaluations, and execution pipelines. The goal is to make these systems faster, more cost-effective, reliable, and scalable, enabling researchers to focus on multimodal model reasoning. The position focuses on performant and efficient systems for frontier visual intelligence, including real-world, high-throughput applications and fundamental research. The engineer will build and maintain services and infrastructure that support visual foundation models and high-volume multimodal data workloads, partnering with research and engineering teams to deliver high-performance, developer-facing systems and APIs.

Requirements

At least 3 years of software engineering experience building, scaling, and maintaining high-impact distributed backend systems handling massive data volumes and high-throughput traffic.
Deep understanding of distributed systems principles, high concurrency, event-driven architectures, and fault-tolerant design at scale.
Proven experience managing and optimizing large-volume data architectures, including distributed task queues, streaming pipelines, and low-latency communication frameworks.
Strong proficiency in modern systems or backend programming languages.
Track record of shipping mission-critical backend features, eliminating system bottlenecks under heavy load, and operating high-throughput production systems.

Nice To Haves

Direct experience at an AI frontier lab, AI research institute, or core platform engineering team supporting foundation model development.
Practical experience building backend systems or data pipelines for multimodal models.

Responsibilities

Architect and own core distributed backend services, APIs, and data execution pipelines for the visual reasoning model ecosystem.
Address scale, concurrency, and reliability challenges across large-scale distributed clusters to maximize throughput and achieve sub-second latency.
Build event-driven streaming frameworks, distributed task scheduling, and state management systems for high-volume multimodal data workloads.
Lead technical design for fault tolerance, high availability, and auto-scaling across the backend service topology.
Partner with research and engineering teams to translate visual AI capabilities into high-performance, developer-facing systems and APIs.