Want to know if this job is worth applying to?
San Francisco, California, United States
Onsite
Available
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.
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.
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.
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.
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.
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.
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.
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.
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.
San Francisco, CA
This is an on-site position, 5 days per week, based in San Francisco's Financial District.
Visa transfers may be supported, including candidates currently on statuses such as OPT or H-1B, depending on individual circumstances.
Base Salary: $200,000 – $350,000
Plus competitive equity.
Compensation will depend on experience, technical depth, research background, and expected impact.
We are looking to hire multiple exceptional engineers and researchers for this team.
