TL;DR

Senior AI Software Research Engineer (AI): Turning cutting-edge AI research into high-performance, production-ready systems with an accent on implementing efficient, scalable algorithms for language and multimodal models. Focus on optimizing deep learning pipelines, exploring novel approaches to attention variants, and profiling large-scale models for speed and cost efficiency.

Location: This role will be eligible for a hybrid work model in Petah-Tikva, Israel, allowing employees to split their time between working on-site at their assigned %hirify_global% site and off-site.

Company

%hirify_global% is committed to strategically penetrating the AI market by delivering disruptive and transformative solutions, driving the software-defined transformation of the world's networks profitably.

What you will do

Translate experimental findings and research papers into optimized, measurable improvements in training and inference.
Design and optimize deep learning pipelines using frameworks like PyTorch, JAXvLLM, and sg-lang.
Explore and implement novel approaches to attention variants, state space models, and efficient distributed training.
Collaborate with research scientists, ML engineers, and infrastructure teams to move ideas from prototype to deployment.
Profile, benchmark, and tune large-scale models for speed, stability, and cost efficiency.

Requirements

M.Sc or PhD in Computer Science, Electrical Engineering, or equivalent.
5+ years of experience in software engineering or applied research, with deep expertise in Python and PyTorch.
Strong understanding of LLM and multimodal architectures, including transformers, state space, and Diffusion models.
Proven ability to implement ideas from AI/ML research papers into production-grade systems.
Hands-on experience with AI frameworks such as vLLM, Megatron-LM, and sg-lang.
Demonstrated skill in profiling, optimization, and scalable distributed systems for training or inference.
Familiarity with algorithmic aspects of AI, especially efficiency and model compression.

Nice to have

Contributions to open-source AI projects or published research.
Experience with model parallelism and memory-efficient attention.
Knowledge of evaluation and LLM fine-tuning pipelines.

Culture & Benefits

Work in an environment that values scientific curiosity, engineering rigor, and a bias toward shipping value products.
Mentorship orientation comfortable sharing knowledge with teammates.
Strong communication and organizational skills, with a drive to deliver value iteratively while pursuing long-term innovation.
Hybrid work model.