San Francisco · Hybrid
A well-funded AI infrastructure company is building next-generation multimodal foundation models alongside a high-efficiency serving platform. With deep industry backing and close collaboration with hardware partners, the team is scaling rapidly to deliver the full stack powering frontier AI models and real-world, high-performance applications.
As a Research Engineer / Machine Learning Engineer, you will work across the entire foundation model lifecycle: large-scale pretraining, post-training and reinforcement learning, sandbox environments for evaluation and agentic learning, and deployment plus inference optimization.
This role is ideal for someone who enjoys moving quickly between research and production—turning ideas into scalable systems, contributing to core infrastructure, and shipping models that operate reliably at scale.
We are an equal opportunity employer and value diversity at our company. All qualified applicants will be considered without regard to race, color, religion, sex, sexual orientation, gender identity or expression, national origin, age, disability, veteran status, or any other protected characteristic.
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Posted 8 months ago
San Francisco
This role is with a well-funded AI infrastructure company building next-generation multimodal AI models and a high-performance training and serving platform. The team is pushing the boundaries of large-scale AI systems and translating cutting-edge research into real-world, production-grade applications.
Overview This role is with a rapidly growing AI infrastructure company building next-generation multimodal AI systems and a high-performance training and serving platform. The team is well-funded and works closely with leading accelerator partners to push the limits of performance for large-scale AI workloads. About the Role We are looking for a GPU Kernel Engineer who is passionate about extracting maximum performance from modern accelerators. In this role, you will design, implement, and optimize custom GPU kernels that power large-scale AI training and inference systems. You will work across the full hardware–software stack, from low-level kernel development to integrating optimized operations into high-level machine learning frameworks. This role is ideal for engineers who thrive at the intersection of GPU programming, systems engineering, and cutting-edge AI workloads. Compensation: $190K–$250K base + equity Equal Opportunity This employer is an equal opportunity organization. All qualified applicants will be considered without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran status, or disability.
Overview This role is with a fast-growing AI infrastructure company building next-generation multimodal models and a high-performance model training and serving platform. The team is backed by significant funding and works closely with leading accelerator partners to build the full software stack powering frontier AI systems. About the Role We are seeking a highly skilled Distributed Training Engineer to design, optimize, and maintain the software stack that enables large-scale AI training workloads. You will work across the entire machine learning infrastructure—from low-level CUDA/ROCm runtimes to high-level frameworks like JAX and PyTorch—ensuring systems are fast, stable, and scalable. This role is ideal for engineers who enjoy deep systems work, debugging complex hardware–software interactions, and optimizing performance across the full ML stack. You will play a critical role in enabling the training and deployment of large language models and generative AI systems. Compensation: $190K–$250K base + equity
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