Lead Member of Technical Staff, Inference Infrastructure

Cohere Cohere · AI Frontier · San Francisco, CA · Inference

Lead Member of Technical Staff, Inference Infrastructure at Cohere. Responsible for the design, deployment, and operation of the AI platform delivering large language models through API endpoints. Focuses on optimizing NLP models for low latency, high throughput, and high availability, with a strong emphasis on Kubernetes, GPU workloads, and multi-cloud environments. Requires extensive experience in production infrastructure, distributed systems, and technical leadership, including mentoring engineers and guiding strategic infrastructure decisions.

What you'd actually do

  1. Provide technical leadership across multiple teams, driving the architecture and strategy for deploying optimized NLP models to production in low latency, high throughput, and high availability environments
  2. Serve as a key point of contact for customers, leading the design of customized deployments to meet their specific needs
  3. Mentor engineers to raise the technical bar across the team
  4. Lead the design of high-performance, scalable and reliable machine learning systems
  5. Help shape the next generation of AI platforms powering advanced NLP applications

Skills

Required

  • Production infrastructure at scale
  • Technical leadership
  • Architecture and design of large, highly available distributed systems
  • Kubernetes
  • GPU workloads
  • GCP, Azure, AWS, OCI, and multi-cloud on-prem / hybrid serving environments
  • Linux-based computing environments
  • Compute/storage/network resource and cost management
  • Computational characteristics of accelerators (GPUs, TPUs, custom)
  • Distributed systems
  • Golang, C++ or similar high-performance languages
  • Mentoring engineers
  • Cross-functional initiatives

Nice to have

  • Setting team-wide standards and best practices for Kubernetes
  • Guiding strategic infrastructure decisions
  • Troubleshooting complex Linux-based computing environments
  • Optimization strategies for resource and cost management
  • Leveraging accelerators for latency and throughput improvements
  • Establishing patterns and practices across engineering teams
  • Setting coding standards
  • Conducting senior-level technical reviews

What the JD emphasized

  • 8+ years of engineering experience running production infrastructure at a large scale, with a track record of technical leadership
  • Demonstrated experience leading the architecture and design of large, highly available distributed systems with Kubernetes and GPU workloads on those clusters
  • Deep expertise with Kubernetes dev and production coding and support, including setting team-wide standards and best practices
  • Extensive experience across GCP, Azure, AWS, OCI, and multi-cloud on-prem / hybrid serving environments, with the ability to guide strategic infrastructure decisions
  • Proven ability to lead the design, deployment, support, and troubleshooting of complex Linux-based computing environments at scale
  • Experience owning compute/storage/network resource and cost management at an organisational level, including optimisation strategies
  • Strong expertise in the computational characteristics of accelerators (GPUs, TPUs, and/or custom accelerators), and how to leverage them to drive latency and throughput improvements at scale
  • Deep knowledge of distributed systems, with experience establishing patterns and practices across engineering teams
  • Proficiency in Golang, C++ or other languages designed for high-performance scalable servers, with the ability to set coding standards and conduct senior-level technical reviews

Other signals

  • Deploying and operating the AI platform delivering Cohere's large language models through easy to use API endpoints
  • Provide technical leadership across multiple teams, driving the architecture and strategy for deploying optimized NLP models to production in low latency, high throughput, and high availability environments
  • Serve as a key point of contact for customers, leading the design of customized deployments to meet their specific needs
  • Mentoring engineers to raise the technical bar across the team