Principal Research Scientist (AI Scaling & Optimization)

TL;DR

Principal Research Scientist (AI Scaling & Optimization): Leading a team to advance the state of the art in large-scale machine learning, focusing on LLM scaling, efficiency, and systems performance. Focus on driving algorithmic innovations for neural network training and inference, optimizing distributed ML systems, and translating research breakthroughs into product capabilities.

Location: Mountain View, California or San Francisco, California

Salary: $270,000 — $350,000 USD

Company

hirify.global is a data and AI company providing a Data Intelligence Platform to unify and democratize data, analytics, and AI for over 10,000 organizations worldwide.

What you will do

• Define and lead independent research programs on foundation model efficiency, including optimizer design and scalable architectures.
• Oversee large-scale experiments and benchmarking against state-of-the-art methods to evaluate quality, latency, and cost.
• Develop high-quality, efficient code in Python and PyTorch for research implementation and rapid prototyping.
• Collaborate with distributed systems and infrastructure teams to optimize parallelism strategies and hardware utilization for LLMs.
• Establish evaluation protocols and metrics for scaling-focused research, such as training efficiency and energy usage.
• Lead and mentor a multidisciplinary team of world-class researchers and engineers.

Requirements

• Proven track record of leading research teams in foundation model efficiency with significant industry impact.
• Deep expertise in generative AI, LLMs, distributed ML systems, or model optimization.
• Strong programming skills in Python and PyTorch for research implementation and experimentation.
• Ability to translate research innovations into scalable product capabilities in partnership with product teams.
• Excellent communication and stakeholder management skills to align research with business impact.
• Must be based in Mountain View, CA or San Francisco, CA.

Nice to have

• Experience in distributed training frameworks or compiler and kernel optimization for deep learning workloads.
• Strong academic network and record of first-author publications at top conferences like ICLR, ICML, or NeurIPS.
• Influential open-source contributions or experience with widely used deployed systems.

Culture & Benefits

• Culture of scientific excellence, openness, and reproducible experimentation.
• Opportunity to work on cutting-edge AI research that is put directly into production.
• Comprehensive benefits and perks tailored to the employee's region.
• Strong commitment to diversity, inclusion, and equal employment opportunities.