We are a well-funded, early-stage AI lab focused on building the next generation of frontier multimodal AI models. Founded by former DeepMind researchers, including Andrew Dai, who was previously a leader on Gemini. Our team currently consists of 20 world-class scientists and engineers. We recently raised $55M in seed funding from Striker Ventures, Menlo Ventures, Altimeter Capital, and NVIDIA. We are tackling some of the hardest problems in artificial intelligence, and we are growing fast.
We're looking for a software engineer to design, optimize, and scale the core systems that serve our foundational visual reasoning models. Your work will make inference, backend services, and distributed execution faster, more cost-effective, and highly reliable, so our researchers can focus on advancing multimodal model reasoning capabilities rather than managing backend bottlenecks.
Our focus is on performant, efficient systems for frontier visual intelligence—both to power real-world, high-throughput applications and to accelerate fundamental research. This role owns the backend and systems software that ensures every deployment, distributed evaluation, and execution pipeline runs seamlessly at scale for our visual foundation models.
What you will doArchitect and own the core distributed backend services, APIs, and data execution pipelines that power our visual reasoning model ecosystem
Solve fundamental scale, concurrency, and reliability challenges across large-scale distributed clusters to achieve maximum throughput and sub-second latency
Build event-driven streaming frameworks, distributed task scheduling, and state management systems designed for high-volume multimodal data workloads
Lead the technical design for fault tolerance, high availability, and auto-scaling across our entire backend service topology
Partner closely with research and engineering teams to translate cutting-edge visual AI capabilities into high-performance, developer-facing systems and APIs
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 of 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
Direct experience working inside 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.
Location: Palo Alto, CA (Hybrid, 3 days in office)
Type: Full-time
Compensation: $300k – $450k + meaningful equity stake + full health/wellness benefits
Skills Required
- 3+ years of software engineering experience building, scaling, and maintaining distributed backend systems
- Experience handling massive data volumes and high-throughput traffic
- Deep understanding of distributed systems principles, high concurrency, event-driven architectures, and fault-tolerant design
- Experience managing and optimizing 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 and operating high-throughput production systems
- Experience eliminating system bottlenecks under heavy load
- Experience at an AI frontier lab, AI research institute, or core platform engineering team supporting foundation model development
- Experience building backend systems or data pipelines for multimodal models
What We Do
Elorian is an early-stage AI research lab founded by former DeepMind and Apple researchers. They are focused on building the next generation of frontier multimodal AI models with a specific emphasis on advanced visual reasoning. By moving beyond text-centric LLMs, they aim to enable AI to understand the spatial and structural complexity of the physical world, pushing the boundaries toward Artificial General Intelligence (AGI) for applications in robotics, science, and industry.
