C

Cohere

Builds language models and workplace AI tools

Forward Deployed Engineer - Inference

Applied Machine LearningConfirmed live in the last 24 hours
Full-timeIn-person

About the Job

Requirements

  • At least 8 years of engineering experience running production infrastructure, designing large-scale distributed systems, or deploying enterprise software in complex environments.
  • Deep technical experience with production Kubernetes, including orchestration across multi-cluster and hybrid or multi-cloud ecosystems such as GCP, Azure, AWS, and OCI.
  • Strong understanding of large language model architecture and inference dynamics, including attention mechanisms and how compute-bound prefill and memory-bound decode phases affect live performance.
  • Hands-on experience with modern inference engines and optimization stacks such as vLLM, TensorRT-LLM, lmd, or Dynamo.
  • Familiarity with distributed serving techniques such as prefill/decode disaggregated serving, distributed key-value caching, and multi-GPU workloads.
  • A proven record of scoping highly ambiguous problems and steering executive technical stakeholders.
  • Ability to write production code on the same day while managing client-side deployment politics or blockers.
  • Strong programming foundations in Python, Go, or C++, and comfort working across backend data pipelines and infrastructure-layer tuning.

Responsibilities

  • Embed directly with customer engineering teams to architect the connective tissue between Cohere AI models and complex production environments.
  • Design advanced model-serving topologies, including multi-tenant architectures, intelligent model routing, and cross-datacenter failover strategies for multi-cluster environments.
  • Optimize AI-in-a-box and private deployments, balancing localized site reliability engineering constraints such as Kubernetes multi-cluster scale with model performance.
  • Benchmark and profile customer large language model workloads, and identify the root causes of performance degradation through metrics, dashboards, and distributed logs.
  • Collaborate with clients to validate hardware and run Cohere models on next-generation accelerators, including GPUs, TPUs, AMD hardware, and custom ASICs.
  • Translate field issues into platform upgrades by providing prescriptive feedback and contributing directly to core product features.
  • Help customers establish reliable Day-1 production deployment patterns, service-level objectives, and observability frameworks.
  • Own the architecture for customer-specific inference challenges, including multi-tenant model serving, multi-datacenter disaster recovery, and custom hardware validation.
  • Lead technical adoption end-to-end to achieve low latency, high throughput, and resilience in customer environments.

About the Company

Cohere

Builds language models and workplace AI tools

Cohere develops language models and AI software for businesses that need to search documents, generate text and automate work. Its customers include large organizations and developers building AI features into their own applications, with tools that connect company information to employee workflows. The North platform brings assistants and task automation into a shared workspace, while its models handle text generation and retrieval through software interfaces. Cohere earns revenue from paid model usage, dedicated deployment capacity and enterprise software agreements tailored to customer needs. The company was founded in 2019 and has its headquarters in Toronto, Ontario.

Company size
501-1,000
Company stage
Series E
Total funding
$2.1B
Headquarters
Toronto, Canada
Founded
2019

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