Member of ML Technical Staff

Member of Technical Staff — LLM Research & Training

About the Role

We are looking for an exceptional Member of Technical Staff specializing in Machine Learning and Large Language Models to join an early-stage AI company building and training state-of-the-art foundation models.

This role sits at the intersection of LLM research, large-scale training infrastructure, post-training, and GPU/kernel optimization .

We are particularly interested in highly motivated researchers and engineers who want to contribute directly to training powerful models — whether their strengths are in theoretical model research, training systems, distributed infrastructure, or low-level performance optimization.

You will work in a small, highly technical team where researchers and engineers collaborate closely and are expected to take ownership across the stack.

Responsibilities

• Research, design, and implement new techniques for training and improving large language models.

• Build and optimize large-scale pre-training and post-training pipelines.

• Improve model training efficiency, throughput, stability, and scalability.

• Work on distributed training across large GPU clusters.

• Design and optimize model-parallel training strategies, including tensor, pipeline, sequence, and data parallelism.

• Optimize GPU workloads using technologies such as CUDA and Triton .

• Improve inference and training kernels when necessary.

• Explore new model architectures, training methodologies, and post-training techniques.

• Run experiments, analyze results, and rapidly iterate on research ideas.

• Collaborate on software/hardware co-design to maximize training throughput.

• Contribute to internal research infrastructure and potentially open-source initiatives.

What We're Looking For

LLM / ML Research Experience

• At least 1+ years of experience in theoretical LLM research or as an ML researcher/engineer at a highly technical AI or technology organization .

• Hands-on experience working with large language models beyond simply consuming existing APIs.

• Experience with one or more of:

  • LLM architecture research

  • Pre-training

  • Post-training

  • Reinforcement learning / preference optimization

  • Training framework development

  • Kernel or inference optimization

  • Large-scale distributed training

Experience working on language models at organizations or research environments comparable to OpenAI, Google DeepMind, Mistral AI, Qwen, DeepSeek, Z.ai , Allen Institute for AI, or leading academic labs is highly relevant.

Large-Scale Training

Strong understanding of large-scale AI infrastructure and at least some of the following:

• Distributed GPU training

• Model parallelism

• Tensor parallelism

• Pipeline parallelism

• Sequence parallelism

• Data parallelism

• Communication optimization

• Memory optimization

• Training throughput optimization

• Software/hardware co-design

Experience contributing to initiatives such as NanoGPT Speedrun, Marin , or similar open-source model-training projects is a strong plus.

Technical Skills

Strong proficiency with:

• Python

• PyTorch

• CUDA

• Triton

Experience with JAX is highly valued.

Additional experience with distributed training frameworks, custom kernels, GPU profiling, compiler optimization, or high-performance computing is a plus.

Research Background

We value candidates who have demonstrated strong technical depth through one or more of:

• ML/AI research during undergraduate, master's, or PhD studies

• Publications or meaningful research contributions

• Open-source ML contributions

• Competitive programming

• Building large-scale ML systems from first principles

A strong undergraduate degree is expected, ideally from a highly selective technical university. Advanced degrees are welcome but not required .

What Makes Someone Successful Here

You are likely to thrive in this role if you:

• Have extremely strong technical fundamentals.

• Are genuinely interested in understanding how modern language models work internally.

• Prefer building and improving models rather than simply applying existing LLMs to business use cases.

• Are comfortable moving between research and engineering.

• Have high energy, intellectual curiosity, and low ego.

• Enjoy working in small, fast-moving teams.

• Are comfortable tackling problems that do not yet have established solutions.

• Can independently turn research ideas into working systems and experiments.

Nice to Have

• Experience at an early-stage AI startup.

• Contributions to open-source ML frameworks or research projects.

• Experience optimizing GPU kernels or inference engines.

• Experience building training infrastructure from scratch.

• Experience training models across large GPU clusters.

• Strong systems engineering or HPC background.

Not a Fit If

This role is probably not the right fit if your experience is primarily:

• Integrating existing LLM APIs into applications.

• Building RAG or chatbot applications without working on the underlying models.

• Prompt engineering without model training experience.

• Working exclusively in large, highly structured engineering organizations with narrowly defined responsibilities.

Location

San Francisco, CA

This is an on-site position, 5 days per week , based in San Francisco's Financial District.

Visa Sponsorship

Visa transfers may be supported, including candidates currently on statuses such as OPT or H-1B , depending on individual circumstances.

Compensation

Base Salary: $200,000 – $350,000

Plus competitive equity .

Compensation will depend on experience, technical depth, research background, and expected impact.

Hiring Plan

We are looking to hire multiple exceptional engineers and researchers for this team.