Forward Deployed AI Engineer

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.

About the Role:

As a Forward Deployed AI Engineer at webAI, you will be on the front lines of integrating cutting-edge AI solutions into real-world enterprise environments. You will work directly with customers to understand their needs, deploy AI systems into their infrastructure, and ensure high performance and reliability at scale. You’ll help bridge the gap between research, engineering, and operations—playing a pivotal role in building the future of edge AI.

This role will require a minimum of 25% travel, including occasional on-site work at customer locations to support deployment, troubleshooting, and integration of our platform within enterprise environments.

Key Responsibilities:

• Collaborate closely with customers to scope, deploy, and maintain AI solutions in production environments.
• Debug and optimize data pipelines and AI systems running on customer networks.
• Translate complex, often ambiguous customer requirements into well-scoped technical solutions.
• Work across the stack—from model inference on consumer hardware to infrastructure automation.
• Read hardware schematics/logs to identify performance bottlenecks and suggest improvements.
• Serve as a trusted technical advisor to enterprise clients, representing the engineering team externally.
• Contribute feedback and insight to internal teams to continuously improve product robustness and usability.

Required Skills & Qualifications:

• 5+ years of combined experience in software engineering and machine learning.
• Proven track record of deploying and maintaining machine learning/AI systems in production.
• Strong expertise in MLX and/or PyTorch.
• Experience debugging complex systems involving data pipelines, model inference, and hardware interaction.
• Exceptional communication skills; able to challenge vague requirements and turn them into actionable plans.
• Comfortable working in dynamic environments with high customer exposure.

Preferred Qualifications:

• Experience deploying models on edge devices or consumer hardware.
• Familiarity with distributed systems, DevOps tooling, and performance tuning.
• Prior experience in a customer-facing engineering role.
• Knowledge of privacy-preserving AI and secure compute environments.
• Master’s degree in a relevant technical discipline.