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,** [****](http://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.