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 Staff Technical Product Manager is a senior-level leader responsible for driving strategy, execution, and delivery of webAI’s most complex and high-impact product initiatives. This role sits at the intersection of AI research, distributed systems engineering, and enterprise product delivery. You will define long-term product direction, lead cross-functional teams, and ensure that webAI’s distributed AI platform, LLM capabilities, and on-device inference stack evolve with precision and speed.

As a senior individual contributor, you will own technical decision-making, influence architecture, and act as a thought partner to engineering leadership across the company. This role is ideal for someone who thrives in ambiguity, enjoys solving deeply technical problems, and can translate complex AI systems into scalable, intuitive, customer-ready products.

• Develop and own long-term product strategy for core platform components including distributed inference, model execution, APIs, and enterprise integrations
• Work closely with Engineering, Research, and Applied AI leadership to define technical requirements and drive architectural decisions
• Lead product planning and prioritization ensuring alignment across teams and with company objectives
• Translate mission-critical customer requirements into actionable product specifications
• Build detailed product documentation, product briefs, roadmaps, and success metrics for high-complexity initiatives
• Partner with Go-to-Market, Customer Success, and Solutions teams to ensure smooth deployments and strong adoption
• Break down ambiguous challenges into clear work streams and drive execution across the full product lifecycle
• Identify technical risks, interdependencies, and bottlenecks, recommending data-supported mitigation strategies
• Define and maintain product standards including documentation, release processes, and cross-functional communication
• Stay current with advancements in AI, LLMs, distributed systems, and emerging technologies
• Mentor junior and mid-level product managers and elevate technical product thinking across the team

• 5+ years of technical product management, ML engineering, or technical program management experience
• Strong understanding of AI and ML technologies including LLMs, generative AI, inference frameworks, and ML training workflows
• Proven ability to lead high-impact product initiatives requiring deep technical judgment
• Experience working closely with engineering teams on architecture, scaling, or infrastructure products
• Knowledge of distributed systems, API design, compute infrastructure, or edge-based workloads
• Strong communication skills with the ability to simplify complex technical topics
• Track record of delivering products in fast-paced, high-growth, or ambiguous 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, model quantization, or edge inference
• Hands-on ML or AI engineering background
• Familiarity with data pipelines, MLOps, and AI model performance…