System Software Engineer - AI

Delos Data Inc•Montreal, QC

3d•Hybrid

About The Position

We are a stealth-mode startup building foundational technology to address performance, scalability, and resiliency challenges in large-scale AI data center clusters. We are backed by top-tier VC firms and notable angel investors. The company is led by experienced builders and operators who have founded companies, taken them to scale, and exited successfully. We work with a strong sense of unity and shared responsibility, and we expect trust, integrity, and respect in how we collaborate and make decisions. We hold ourselves accountable to one another and to the quality of the work we deliver. Headquartered in Silicon Valley, we operate across a mix of remote and on-site locations in the U.S. and Canada. We aim to create an environment where people are treated fairly, supported in their growth, and are empowered to do meaningful work alongside others who take the craft seriously. We are looking for a talented System Software Engineer to help us redefine the infrastructure layer of AI. In this role, you will bridge the gap between high-level AI frameworks and low-level system software. You will be responsible for designing and implementing the communication and execution primitives that allow large-scale AI models to run efficiently across thousands of GPUs. We are looking for a "builder" who thrives in the early stages of a product’s lifecycle and is passionate about solving the "hard" systems problems of the generative AI era.

Requirements

Strong proficiency in C++ and Python, with a deep understanding of systems programming fundamentals (memory management, concurrency, OS internals).
Proficient in a Linux development environment.

Nice To Haves

Experience with GPU programming (CUDA) and performance optimization for parallel architectures.
Familiarity with distributed AI frameworks (PyTorch, JAX, or DeepSpeed) and/or inference engines (vLLM, SGLang, Dynamo/TRT-LLM).
Hands-on experience with large-scale cluster orchestration and telemetry tools.

Responsibilities

Collaborate across the stack to influence the design of our foundational technology, ensuring it meets the needs of next-generation AI models.
Identify and resolve performance bottlenecks in distributed training and inference workloads through deep-dive analysis of the software-hardware interface.
Conduct rigorous performance benchmarking and characterization on multi-node clusters.