Personal AI empowers organizations to deploy secure, role-specific AI personas for real-world business impact. Our proprietary platform enables advanced agent-to-agent AI workflows, distributed deployment across edge and cloud, and relentless focus on privacy, user control, and operational reliability. We're scaling rapidly and seeking production-focused engineers who build real AI systems that ship and scale.

The Company

• We are serial entrepreneurs with a track record of building and exiting VC-backed startups, bringing experience from LinkedIn, Qualcomm, Microsoft, and more.
• At Personal AI, we’re building a secure, highly accurate AI layer that integrates into enterprise workflows and augments human productivity.
• Our platform enhances memory, communication, and decision-making—positioning us to be the multiplier for the future of work.

The Culture

• We are deeply technical and product-focused, building tools that companies not only adopt but scale.
• We favor innovation over convention, constantly testing and adopting emerging technology.
• We value ownership, high standards, and people who take initiative and deliver exceptional work.

Who You Are

• You are an engineer first and foremost and scientist second. 
• You are excited about shipping AI functionalities to production.
• You are well versed in traditional and transformer based machine learning methods.
• You don’t code in Jupyter notebooks.
• You are not a prompt engineer.

Overview

• We're seeking a hands-on AI Engineer who builds production-ready AI systems, not research prototypes.
• You'll optimize our AI ingestion pipeline for more accurate, responsive agentic behavior, deploy high-performance models on GPU infrastructure using our Trident architecture, and maintain robust MLOps workflows from training through production deployment.
• This is for engineers who ship code, not just notebooks.

Why Join Personal AI?

You'll work with cutting-edge AI technology that real enterprises depend on, not just research papers. We offer the resources, autonomy, and technical challenges to build world-class AI systems that ship to production every day. If you're a production-focused AI engineer excited by the challenge of building reliable, scalable AI systems, we want to talk to you.

Responsibilities

• Enhance AI Pipeline Accuracy: Improve our data ingestion and processing pipeline to deliver more accurate responses and sophisticated agentic behaviors in production applications.
• GPU-Optimized Model Deployment: Deploy and optimize AI models on high-performance GPU infrastructure using our Trident architecture, ensuring efficient training, inference, and scaling.
• Production MLOps: Build and maintain end-to-end MLOps pipelines including RAG systems, model distillation, fine-tuning workflows, training orchestration, and production inference deployment.
• Data Model Engineering: Design and implement robust data models and processing workflows that power our AI persona capabilities.
• Infrastructure & DevOps: Create production-grade CI/CD pipelines, containerization (Docker), comprehensive logging systems, and monitoring for AI model performance.
• Real Production Deployment: Take AI systems from development through production deployment, focusing on reliability, performance, and operational excellence.

Required Technical Skills

Core Programming (Non-negotiable):

• Python (primary language for AI/ML work)
• Strong proficiency in C++, Java, or C# for performance-critical components
• Data modeling and processing at production scale

AI/ML Production Stack:

• RAG Pipeline development and optimization
• MLOps workflows: training, inference, model lifecycle management
• Model distillation and fine-tuning techniques for production deployment
• Experience deploying models to GPU infrastructure (Trident or similar architectures)

Production Engineering:

• CI/CD pipeline creation and management
• Docker containerization and microservices architecture
• Production logging, monitoring, and observability
• Experience scaling AI systems in real production environments

What We DO Want

• 3-5 years of production AI/ML engineering experience
• Engineers from mid-sized companies who have successfully deployed AI systems at scale
• Proven track record of building, deploying, and maintaining ML systems in production
• Experience optimizing AI systems for performance, cost, and reliability
• Strong system design and architecture skills for scalable AI applications

What We DON'T Want

• Pure researchers who can't ship production code
• Jupyter notebook-only developers
• Engineers who haven't deployed AI systems to real users
• Candidates focused solely on experimentation without production experience

What We Offer

• Competitive compensation
• Flexible remote work and async-friendly culture
• Opportunity to shape real-world AI tools for enterprise use cases
• High-trust, collaborative environment
• Regular team syncs and optional virtual events