Research Scientist (Test Time Compute)

ZipRecruiter; San Francisco (CA); Remote; USD 120,000 - 180,000; Full time

Job summary

Join an innovative firm as an AI Research Scientist, focusing on enhancing model performance and inference efficiency. In this pivotal role, you'll tackle core challenges in model compression and deployment optimization, working at the intersection of machine learning and systems optimization. Your contributions will shape the architecture of cutting-edge inference optimization platforms, while collaborating with a dynamic technical team. This is an exciting opportunity to advance your career in a forward-thinking environment that values practical implementation and encourages contributions to the academic community.

Benefits

Full medical, dental, and vision coverage

Flexible PTO policy

Learning and development budget

Conference and research publication support

Home office setup allowance

Equity stake

Remote-first work environment

Qualifications

• Strong background in machine learning and systems optimization.
• Hands-on experience with modern ML frameworks and deployment tools.
• Track record of implementing efficient ML systems.

Responsibilities

• Design and implement novel architectures for efficient model inference.
• Develop frameworks for model compression and quantization.
• Collaborate with engineering team on implementation.

Skills

Machine Learning

Systems Optimization

Model Compression

Inference Optimization

Python Programming

Analytical Skills

Problem-Solving

Education

PhD in Machine Learning

Computer Science

Mathematics

Tools

PyTorch

TensorFlow

GPU Optimization

TPU Optimization

Version Control

Job description

Job DescriptionJob DescriptionAI Research Scientist (Test Time Compute) | naptha.ai

About the role

We are seeking an exceptional AI Research Scientist to join Naptha AI at the ground floor, focusing on advancing the state of the art in test time compute optimization for large models. In this role, you will be responsible for researching and developing novel approaches to improve inference efficiency, reduce computational requirements, and enhance model performance at deployment. Working directly with our technical team, you will help shape the fundamental architecture of our inference optimization platform.

This role is critical in solving core technical challenges around model compression, efficient inference strategies, and deployment optimization. You will work at the intersection of machine learning, systems optimization, and hardware acceleration to develop practical solutions for real-world model deployment and scaling.

Core ResponsibilitiesResearch & Development

• Design and implement novel architectures for efficient model inference

• Develop frameworks for model compression and quantization

• Research approaches to optimize test-time computation across different hardware

• Create efficient protocols for distributed inference and resource management

• Implement and test new ideas through rapid prototyping

Technical Innovation

• Stay at the forefront of developments in ML efficiency and inference optimization

• Identify and solve key technical challenges in model deployment

• Develop novel approaches to model compression and acceleration

• Bridge theoretical research with practical implementation

• Contribute to the academic community through publications and open source

Platform Development

• Help design and implement efficient inference pipelines

• Develop scalable solutions for model deployment and serving

• Create tools and frameworks for performance monitoring and optimization

• Collaborate with engineering team on implementation

• Build proofs of concept for new optimization techniques

Leadership & Collaboration

• Work closely with engineering team to implement research findings

• Mentor team members on advanced optimization techniques

• Contribute to technical strategy and roadmap

• Collaborate with external research partners when appropriate

• Help evaluate and integrate external research developments

In this role, you're a good fit if you have:

• Strong background in machine learning and systems optimization

• Deep understanding of model compression and efficient inference techniques

• Hands-on experience with modern ML frameworks and deployment tools

• Experience with ML infrastructure and hardware acceleration

• Track record of implementing efficient ML systems

• Excellent programming skills (Python required, C++/CUDA a plus)

• Strong analytical and problem-solving abilities

• PhD in Machine Learning, Computer Science, Mathematics, or equivalent experience is a plus

• Published research in relevant fields is a plus

Required Technical Experience:

• Python programming and ML frameworks (PyTorch, TensorFlow)

• Experience with model optimization techniques (quantization, pruning, distillation)

• MLOps and efficient model deployment

• Hardware acceleration (GPU, TPU optimization)

• Version control and collaborative development

• Experience with large models

About the hiring process:

• Initial technical interview

• Research presentation

• System design discussion

• Technical challenge

• Team collaboration interview

Compensation & Benefits:

• Competitive salary with significant equity stake

• Remote-first work environment

• Full medical, dental, and vision coverage

• Flexible PTO policy

• Learning and development budget

• Conference and research publication support

• Home office setup allowance

Additional Notes:

• Must be comfortable with ambiguity and rapid iteration typical of pre-seed startups

• Strong bias for practical implementation of research ideas

• Passion for advancing the field of efficient ML systems

• Interest in open source contribution and community engagement

Naptha AI is committed to building a diverse and inclusive workplace. We are an equal opportunity employer and welcome applications from all qualified candidates regardless of background.