Principal Research Scientist (AI Scaling)

TL;DR

Principal Research Scientist (AI Scaling): Leading a team to advance the state of the art in large-scale machine learning with an accent on post-training, RL and inference efficiency. Focus on driving algorithmic innovations for large-scale neural network training and optimizing end-to-end ML systems for distributed training.

Location: Mountain View, California; San Francisco, California

Salary: $270,000 — $340,000 USD

Company

hirify.global is the 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 research programs on foundation model efficiency, covering optimizer design, low-precision training, and scalable architectures.
• Oversee the design and execution of large-scale experiments, evaluating trade-offs in quality, latency, throughput, 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 metrics, evaluation protocols, and best practices for scaling-focused research across hirify.global AI.
• Champion the responsible and robust deployment of scaling innovations, ensuring model reliability and safety.

Requirements

• Must be based in or authorized to work in Mountain View or San Francisco, California
• Proven ability to lead a research team in developing novel techniques for foundation model efficiency with 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.
• Demonstrated ability to translate research innovation into scalable product capabilities.
• Excellent communication and stakeholder management skills for influencing cross-functional roadmaps.

Nice to have

• Experience in distributed training frameworks, compiler and kernel optimization, or compute-efficient model design.
• Strong industry and academic network with service (e.g., PC/area chair) at top ML conferences like ICLR, ICML, or NeurIPS.
• Record of high-impact publications or influential open-source contributions in optimization or efficiency.

Culture & Benefits

• Comprehensive benefits and perks tailored to the employee's region.
• Culture of scientific excellence, openness, and reproducible experimentation.
• Opportunity to work on cutting-edge AI technologies used by a large portion of the Fortune 500.
• Support for external representation through top-tier publications and conference talks.