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.