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Own full sales cycle for federal government agencies in Latin America: drive net-new acquisition, develop regional sales plans, engage senior decision-makers, work with partners and channel distributors, collaborate with product, engineering, SDRs and marketing, and close enterprise AI/SaaS deals including proofs-of-concept and procurement.

The Applied AI Engineer will design, build, and deploy enterprise-grade AI agents using LLMs, collaborate with customers, and mentor teams to enhance engineering quality.

Manage a global portfolio of HR programs and initiatives within the HR PMO. Build project plans, milestones, workstreams, governance, and ownership; coordinate cross-functional execution across HR, Finance, Legal, IT, and other teams; manage risks, dependencies, decisions, and stakeholder alignment; and develop scalable workflows and tools for HR operations. The role requires thriving in ambiguity, moving quickly, and independently delivering complex projects in a high-growth environment.

Lead and scale global corporate communications: build an eight-person team, define strategy across executive, policy, internal, crisis and enterprise communications, integrate AI for content, manage channels and events, drive storytelling and data-informed programs to protect and grow company reputation.

Drive Cohere's AWS-sourced and AWS-influenced partner pipeline. Define and execute partner GTM for the Enterprise Market; recruit, onboard, enable and certify partners; lead co-selling, joint pipeline generation, business planning and QBRs; relay partner feedback to product and policy teams and evangelize Cohere at events.

Own the full sales cycle in Korea for Cohere's enterprise AI platform: prospecting, outbound net-new acquisition, POC collaboration, channel partner engagement, and cross-functional coordination with Product, Engineering, Sales Development, and Sales Engineering to close large enterprise deals.

Drive analytics and measurement for AI product adoption and impact. Build agentic analytics, small language models, predictive models, and customer-facing insights. Design and run experiments (A/B tests, causal inference), ship production models, and translate ambiguous business questions into actionable recommendations used by product, sales, and leadership.

Lead and mentor a GPU infrastructure engineering team to design, deploy, and scale GPU/TPU clusters. Define technical roadmap, implement topology-aware scheduling, monitoring, IaC, and cost/capacity optimization. Collaborate with AI researchers, cloud providers, and cross-functional teams to ensure reliable, secure, and performant infrastructure.

Build and maintain production-grade data processing systems and foundational infrastructure for enterprise AI. Collaborate with researchers and engineers, deliver end-to-end implementations, transform unstructured data into performant datasets, and use distributed processing frameworks and modern analytics tooling to inform products and strategy.

The Senior ML Systems Engineer will build and maintain the training framework for large-scale language models, focusing on distributed training and performance optimization.