Jobs at Cohere AI

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.

Support Corporate Communications by building reporting and analytics, developing agentic automation, project-managing executive and internal communications, executing rapid response and events, optimizing communication channels, and coordinating cross-functional storytelling with Policy and Government Affairs. Drive process, project management, and creative formats to amplify executive thought leadership and customer success.

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 design and delivery of leadership, manager, and talent development programs at a scaling AI company. Partner with HRBPs and senior leaders to build onboarding, succession, high-potential, and manager enablement frameworks from the ground up, balancing strategy and execution and driving organizational capability.

The RevOps Analyst will enhance reporting and processes to drive revenue growth, manage quote-to-cash processes, and ensure data hygiene while collaborating with the Revenue team and delivering insights to management.

Lead sales operations to optimize revenue growth by defining strategy, streamlining processes, managing CRM and pipeline, delivering performance analytics, coordinating cross-functional GTM efforts, and mentoring teams while aligning sales execution with company objectives and stakeholders.

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.

Lead end-to-end technical programs for ML infrastructure (inference, serving, efficiency, endpoints). Coordinate cross-functional teams, prioritize overlapping projects, manage timelines, budgets and scope, drive incident management and continuous improvement, and communicate status to engineering and leadership.

The role involves managing Cohere's platform for developers and enterprises, overseeing APIs, SDKs, and tooling for improved integration and user experience.

Build and own tooling and data pipelines for synthesis, annotation, validation, and analysis of real and synthetic data to support model training and evaluation. Collaborate with researchers and engineers to align data infrastructure with experiments, maintain documented standards, and develop frameworks to identify coverage gaps and improve data quality.

Lead and mentor a team of Forward Deployed Engineers to build and deploy agentic solutions with Cohere's North platform in on‑premises enterprise environments. Own technical implementation through delivery, collaborate with Product/Engineering/Sales, drive customer relationships, and scale agent development and deployment processes.

Build and maintain integrations for North's agentic platform, own the end-to-end integration pipeline, create evals and tests to ensure reliability, tune tool descriptions, and help other engineers add and maintain integrations while using AI responsibly to improve performance and quality.

Build, ship, and maintain full-stack features for North: large-scale file uploads, shared knowledge bases, agent extensibility, and data-sync pipelines. Own end-to-end design, implementation, testing, launch, and iteration while working closely with Product, UX, and modeling teams. Prioritize security, performance, and mentoring.

Lead and stand up executive communications for Cohere: refine strategy, manage a small team, drive executive content (speaking, op-eds, social), enable rapid response and crisis readiness, oversee speaking engagements and storytelling, partner cross-functionally to amplify executive thought leadership for regulated enterprise and government audiences.

Lead product and technology communications for Cohere by partnering with product, engineering, research, and GTM teams. Develop messaging, launch materials, thought leadership, and crisis communications; translate complex AI/technical concepts for technical and non-technical audiences; use insights to refine messaging and drive cross-functional alignment.

Run marketing org operations and planning cadence, translate VP priorities into executable plans, manage budget/headcount trade-offs, ensure cross-functional coordination, own leadership materials and special projects, and improve org cadence and accountability so programs ship on time and leaders focus on strategy.

Lead North's integrations strategy and execution: prioritize, ship, and drive adoption of connectors and ecosystem tooling. Partner with customers and engineering to convert integrations into reusable platform capabilities (auth, connector framework, data sync, permissions). Define developer/partner experience (APIs, SDKs, plugins) and instrument analytics to measure adoption, time-to-stand-up, and retention.

The Staff Software Engineer will develop and deploy high-performance machine learning systems, optimizing large language models for production environments. Responsibilities include ensuring high availability and low latency for AI applications and working collaboratively across teams.

The Site Reliability Engineer will develop, deploy, and operate AI infrastructure, focusing on high-performance and scalable machine learning systems using Kubernetes and cloud platforms.