Technical Product Manager, AI Austin, TX

About Us:

webAI is pioneering the future of artificial intelligence by establishing the first distributed AI infrastructure dedicated to personalized AI. We recognize the evolving demands of a data-driven society for scalability and flexibility, and we firmly believe that the future of AI lies in distributed processing at the edge, bringing computation closer to the source of data generation. Our mission is to build a future where a company's valuable data and intellectual property remain entirely private, enabling the deployment of large-scale AI models directly on standard consumer hardware without compromising the information embedded within those models.

We are developing an end-to-end platform that is secure, scalable, and fully under the control of our users, empowering enterprises with AI that understands their unique business. We are a team driven by truth, ownership, tenacity, and humility
, and we seek individuals who resonate with these core values and are passionate about shaping the next generation of AI.

The Technical Product Manager, AI will play a central role in defining and guiding the development of webAI’s AI capabilities across distributed inference, large language models, on-device machine learning, and enterprise integrations. You will collaborate closely with engineering, applied AI, research, and customer-facing teams to translate complex technical requirements into clear product plans, ensuring our platform delivers meaningful value to public sector and enterprise users.

This role requires strong technical depth, exceptional communication skills, and the ability to drive execution in fast-paced, high-impact environments. You will shape product strategy, prioritize roadmaps, and ensure AI features are delivered with performance, usability, and security in mind.

• Develop product strategy, requirements, and roadmaps for AI features across distributed inference, LLM integration, and model optimization

• Partner with engineering and research teams to translate technical concepts into actionable product specifications

• Own the full product lifecycle from ideation to launch including requirements gathering, prioritization, execution, and delivery

• Work directly with customers and partners to understand mission needs and ensure solutions align with real-world use cases

• Create detailed product documentation, technical briefs, release notes, and internal communication materials

• Lead cross-functional collaboration between Engineering, Applied AI, Research, and Go-to-Market teams

• Evaluate competing priorities and make data-driven tradeoff decisions that balance complexity, user needs, and business goals

• Track performance metrics, model outputs, and reliability indicators to drive continuous improvement

• Stay up to date on AI advancements including LLMs, multimodal models, on-device inference, and infrastructure trends

• Ensure clear communication across teams and alignment on timelines, risks, and deliverables

• 3+ years of product management, technical program management, machine learning engineering, or related experience

• Strong understanding of AI and machine learning technologies including modern LLMs, generative models, inference optimization, and distributed compute

• Experience managing technical software or ML products from concept to delivery

• Ability to translate customer needs into detailed, actionable product requirements

• Strong technical background with the ability to understand model architecture, inference constraints, and system integration

• Excellent communication skills capable of bridging technical and non-technical stakeholders

• Experience working with engineering teams in fast-paced, iterative development environments

• Bachelor’s degree in Computer Science, Engineering, Mathematics, or related field

• Have shipped a widely adapted AI product

• Experience with on-device machine learning, edge inference, or hardware-constrained environments

• AI or ML engineering experience or graduate-level education in machine learning

• Familiarity with data pipelines, MLOps, and AI model evaluation frameworks

• Experience defining metrics, performance…