Forward Deployed Engineer, Inference

Excerpt from the original posting

Who are we? 
Cohere is the leading security-first enterprise AI company. We build cutting-edge foundation AI models and end-to-end products that are designed to solve real-world business problems.
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.
We obsess over what we build. Each one of us is responsible for contributing to increasing the capabilities of our models and the value they drive for our customers. Cohere is a team of researchers, engineers, designers, and more, who are all passionate about their craft.
We are a global technology company headquartered in Toronto with key offices in London, New York City, San Francisco, Montreal, Paris, Berlin and Seoul. Join us!
Why this role? 
Are you energized by bridging the gap between frontier AI capability and complex customer reality? Do you want to architect high-performance, resilient machine learning systems directly inside strategic enterprise environments? We are looking for a Staff Forward Deployed Engineer (FDE) to join our team. The team is responsible for taking our large language models and making them work reliably, securely, and at peak performance within our customers' custom infrastructure and data perimeters.
In this role, you will act as the principal technical translation layer between our core engineering teams and our strategic partners. You will own the architecture for highly ambiguous, customer-specific inference challenges—ranging from multi-tenant model serving to multi-datacenter disaster recovery and custom hardware validation. You will lead technical adoption end-to-end, ensuring our models achieve exceptionally low latency, high throughput, and seamless resilience in the field.

As a Forward Deployed Engineer, you will: 
• 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 environments.
• 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 degradat…

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