Software Engineer - Core Platform

Elorian•Palo Alto, CA

9h•Hybrid

About The Position

We are seeking a software engineer to design, optimize, and scale the core systems serving foundational visual reasoning models. The role owns backend and systems software that supports inference, backend services, distributed execution, deployments, evaluations, and execution pipelines. The goal is to make these systems faster, more cost-effective, and reliable, enabling researchers to focus on advancing multimodal model reasoning. The work supports high-throughput real-world applications and fundamental research in visual intelligence. The position is hybrid in Palo Alto, California, with three days in the office. Compensation is $300,000–$450,000, plus meaningful equity and health and wellness benefits.

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 track record managing and optimizing large-volume data architectures, including distributed task queues, streaming pipelines, and low-latency communication frameworks.
Strong proficiency in modern systems/backend 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 working 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.