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, scale, and harden the core distributed systems behind Elorian’s frontier multimodal models. Our models can only advance as fast as the systems underneath them.
You’ll build the backbone that connects research to production: distributed training and evaluation, and low-latency inference for visual reasoning. You’ll work directly with researchers, who then spend their time on model capabilities rather than infrastructure.
What you will doDistributed core systems: Build and own high-throughput, low-latency services, async job execution engines, and streaming pipelines for training, evaluation, and inference on large-scale image and video data.
Fleet performance: Improve utilization, concurrency, and inter-service communication across large GPU clusters.
Reliability and scaling: Design for fault tolerance, automated node recovery, capacity planning, and auto-scaling across multi-cloud and bare-metal environments.
Safe delivery and observability: Own the operational lifecycle: CI/CD with canary releases, SLOs/SLIs, runbooks, and blameless on-call.
Load and capacity testing: Build benchmarking harnesses that simulate 10-100x load spikes, and fix the concurrency, network, and memory bottlenecks they expose.
Research-to-production interface: Work with research scientists to turn new visual reasoning capabilities into stable, ergonomic APIs and internal research platforms.
3+ years designing, shipping, and operating distributed backend systems in production at high data volume and traffic
Strong systems fundamentals: concurrency, networking, memory management, event-driven architecture, and fault-tolerant design
Strong proficiency in C++ and Python
Experience running production services: on-call, incident response, runbooks, and good telemetry and alerting
Hands-on experience with infrastructure as code (e.g. Terraform), containers, and orchestrators (Kubernetes, Slurm, or Ray)
High agency: comfortable with ambiguity, and able to balance speed against long-term stability.
Experience at a frontier AI lab, research institute, or platform team supporting foundation models
Experience building data pipelines or serving systems for multimodal models
Familiarity with PyTorch and Rust, and with serving or training runtimes such as vLLM
Exposure to accelerator networking (InfiniBand, RoCE), distributed file systems, multi-tenant isolation, or usage metering
Location: Palo Alto, CA (on-site)
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.
.png){kind=logo}
