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Own Bolna's real-world presence by running events end-to-end, building and activating partnerships, and growing a voice-AI community. Run sponsorship decisions, measure event outcomes (pipeline, hires, partnerships), and work directly with founders and sales to turn outreach into partnered initiatives and measurable business results.

Build Bolna's 0->1 content function: interview customers, craft case studies and thought leadership, run editorial calendar and content-to-pipeline systems, own social and short-form content, script videos, brief designers, and manage customer communications and email sequences to drive pipeline and buyer understanding.

Embed with Forward Deployed Engineers to identify recurring customer field patterns, write specs (PRDs), co-own feature builds with surface PMs, and create a library of vertical pre-builts (templates, prompts, integrations) to reduce future deployment time.

As an AI Solutions Engineer, you'll design and iterate on prompts for voice agents, collaborate with customers, and enhance agent performance.

The role involves working with the founders on high-priority tasks, managing customer relationships, leading sales conversations, running projects, and making product recommendations based on customer feedback.

The role involves developing a data platform to analyze call data, build benchmarks, and optimize model routing for AI voice agents. It requires technical depth in data and machine learning, effective evaluation and experimentation strategies, along with strong systems thinking and communication skills.

The Product Manager will oversee user experience in Bolna's Studio, focusing on reducing time-to-first-live-call, optimizing templates, and enhancing user activation. Responsibilities include managing the agent creation process, guiding content positioning, leading pricing experiments, and facilitating partnerships.

Manage end-to-end recruitment, maintain hiring processes, build a talent pipeline, coordinate with stakeholders, and assist in candidate experience.

The Machine Learning Engineer will manage the full lifecycle of model deployment, enhance model accuracy and performance, and design data pipelines for voice AI applications.