Senior Research Engineer (AI)

Location: Hybrid role in Toronto, ON, Canada, or Sunnyvale, CA, USA

Company

hirify.global Systems develops large-scale AI accelerator hardware and systems designed to deliver high-speed model training and inference.

What you will do

• Adapt, design, implement, and optimize transformer architectures for NLP and computer vision on hirify.global hardware.
• Research and prototype inference algorithms and model architectures focused on speculative decoding, pruning and compression, sparse attention, and sparsity.
• Train models to convergence, run hyperparameter sweeps, and analyze experimental results.
• Bring up new models, validate functional correctness, and troubleshoot integration issues on the hirify.global system.
• Profile and optimize model code to maximize throughput and minimize inference latency.
• Develop diagnostic tools and collaborate with software, hardware, and product teams to deliver inference projects.

Requirements

• Relevant bachelor's degree with 7+ years of ML software development experience, master's degree with 4+ years of software development experience, PhD with 2+ years of relevant experience, or equivalent practical experience.
• 4+ years of experience testing, maintaining, or launching software products, including 2+ years in software design and architecture.
• 3+ years of machine-learning-focused software development experience, including deep learning, large language models, or computer vision.
• Strong programming skills in Python and/or C++, experience with generative AI and ML systems, and proficiency in PyTorch, Transformers, vLLM, or SGLang.
• Deep understanding of transformer-based models, inference optimization, specialized hardware performance, sparse attention, pruning and compression, and speculative decoding.
• Evidence of ML research impact through publications, open-source contributions, or high-quality preprints.

Nice to have

• Experience with large language models, mixture-of-experts models, multimodal learning, or AI agents.
• Experience with quantization, post-training techniques, inference evaluations, and large-scale model deployment.
• Triton or CUDA experience.
• Experience independently taking complex ML or inference projects from prototype to production-quality implementation.

Culture & Benefits

• Opportunity to build AI infrastructure beyond traditional GPU constraints.
• Support for publishing and open-sourcing AI research.
• Work with a high-performance AI supercomputer platform.
• Startup vitality combined with job stability.
• Non-corporate culture focused on individual beliefs, learning, growth, and inclusion.