About the role
About Us
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.
About the role
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 do
- Distributed 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.
What we are looking for
- 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.
Nice to have
- 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
Logistics
- Location: Palo Alto, CA (on-site)
- Type: Full-time
- Compensation: $300k – $450k + meaningful equity stake + full health/wellness benefits