Forward Deployed Engineer, Inference

Job summary

Responsibilities

We’re training and deploying frontier models for enterprises who are building AI systems. We believe that our work is instrumental to the widespread adoption of AI and we are looking for folks that want to be part of that.

• Embed directly with customer engineering teams to architect the "connective tissue" between our AI models and their live, complex production environments.
• Design advanced model serving topologies, implementing multi-tenant architectures, intelligent model routing, and cross-datacenter failover strategies for multi-cluster…
• Optimize "AI-in-a-box" and private deployments, balancing localized SRE constraints like Kubernetes multi-cluster scale with raw model performance.
• Conduct rigorous benchmarking and profiling of customer LLM workloads, root-causing performance degradation (e.g., slow generation) via metrics, dashboards, and distributed logs.
• Accelerate hardware validation, collaborating with clients to run and support Cohere models on next-generation accelerators (GPUs, TPUs, AMD, and custom ASICs).

What they're looking for

• 8+ years of engineering experience running production infrastructure, designing large-scale distributed systems, or deploying enterprise software in complex environments.
• Deep technical depth in production Kubernetes, including orchestration across multi-cluster and hybrid/multi-cloud ecosystems (GCP, Azure, AWS, OCI).
• Strong understanding of LLM architecture and inference dynamics, specifically how attention mechanisms, compute-bound prefill, and memory-bound decode phases dictate live…
• Hands-on experience with modern inference engines and optimization stacks (e.g., vLLM, TensorRT-LLM, lmd, or Dynamo).
• Familiarity with distributed serving techniques, such as prefill/decode disaggregated serving, distributed KV caching, and multi-GPU workloads.

Tech stack

• Kubernetes
• GCP
• Azure
• AWS
• vLLM
• Python
• Go
• C++