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 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).
• Translate complex field friction into platform upgrades, delivering prescriptive feedback and contributing directly back to core product features.
• Ensure Day-1 production reliability, assisting customers in establishing sound deployment patterns, SLOs, and observability frameworks.
You may be a good fit if you have
• 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 performance.
• 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.
• Exceptional customer-facing technical leadership, with a proven track record of scoping highly ambiguous problems and steering executive technical stakeholders.
• The high agency, grit, and adaptability to write same-day production code while managing client-side deployment politics or blockers.
• Strong programming foundations in Python, Go, or C++, comfortable moving fluidly between backend data pipelines and infrastructure layer tuning.