Staff ML Infrastructure Engineer

• Design, implement, and maintain high-performance infrastructure for training and serving LLMs
• Optimize model pipelines for efficiency, latency, and cost at scale
• Collaborate with ML researchers, platform engineers, and product teams to deploy models safely into production
• Build monitoring, alerting, and tooling to ensure reliability and observability of large-scale ML systems
• Evaluate and integrate new frameworks, tools, and architectures to improve ML workflows
• Provide technical leadership and mentorship to other engineers on the team

• 7+ years of software engineering experience, including 3+ years building production ML systems
• Deep experience with distributed training and inference frameworks (e.g., PyTorch, JAX, TensorFlow)
• Familiarity with model serving technologies and orchestration (e.g., Triton, Ray, Kubernetes)
• Strong understanding of GPU/TPU infrastructure, performance optimization, and scalability challenges
• Proven experience solving reliability, latency, and cost trade-offs in production ML systems
• Excellent collaboration, communication, and problem-solving skills

Experience mentoring or leading engineering teams is a plus

Flexible work arrangements and competitive compensation