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 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.