ML Infrastructure Engineer

About the Role

As our ML Infrastructure Engineer, you’ll build the large-scale systems that power training, inference, and data pipelines for state-of-the-art models. Your work will enable our researchers and ML engineers to train faster, deploy smarter, and iterate without bottlenecks.

If you love building scalable, high-performance systems and want to see your work directly accelerate research breakthroughs, you’ll thrive here.

Responsibilities

• Design and maintain large-scale distributed training pipelines

• Build and optimize inference infrastructure using vLLM, SGLang

• Implement and scale RL training frameworks for large models

• Architect ML data services pipelines for ingestion, preprocessing, and retrieval

• Optimize cluster utilization across GPUs/TPUs and cloud (AWS, Azure)

• Develop monitoring, benchmarking, and debugging tools for training and inference

Qualifications

• Strong engineering skills in distributed systems and high-performance computing

• Proficiency in Python

• Experience with ML frameworks (PyTorch, JAX) and serving frameworks (vLLM, SGLang)

• Familiarity with RL training infrastructure

• Experience with container orchestration (Kubernetes/EKS) and cloud-native ML workloads

Bonus

• Contributions to ML infra projects or serving frameworks

• Knowledge of model optimization techniques (quantization, pruning)

What makes us interesting

• Small, elite team of ex-founders, researchers from top AI Labs, top CS grads, and engineers from top companies

• True ownership You will not be blocked by bureaucracy, shipping meaningful work within weeks rather than months

• Serious momentum We're well-funded by top investors, moving fast, and focused on execution

What we do

• Ship consumer products powered by cutting-edge AI research, and

• Build infrastructure that facilitates research and product, and

• Innovate cutting-edge research that will open up new consumer product forms

The Details

• Full-time, onsite role in Menlo Park

• Startup hours apply

• Generous salary, with additional benefits to be discussed during the hiring process