Backend Engineer

About Invisible

Invisible Technologies makes AI work. Our end-to-end AI platform structures messy data, automates digital workflows, deploys agentic solutions, measures outcomes, and integrates human expertise where it matters most.

Our platform cleans, labels, and structures company data so it is ready for AI. It adapts models to each business and adds human expertise when needed, the same approach we have used to improve models for more than 80% of the world’s top AI companies, including Microsoft, AWS, and Cohere.

Our successes span industries, from supply chain automation for Swiss Gear to AI-enabled naval simulations with SAIC, and validating NBA draft picks for the Charlotte Hornets.

Profitable for more than half a decade, Invisible reached $134M in revenue and ranked as the number two fastest growing AI company on the 2024 Inc. 5000. In September 2025, we raised $100M in growth capital to accelerate our mission of making AI actually work in the enterprise and to advance our platform technology.

About The Role

As a Principal Forward Deployed Engineer (FDE) at Invisible, you'll lead high-impact, AI-powered solutions that reshape how our clients operate their most critical workflows. You won't just build and deploy — you'll drive the strategy, architecture, and execution of end-to-end systems, working directly with clients.

This is a hybrid role: equal parts AI engineer, software builder, and technical consultant. It's perfect for someone who wants to be hands-on with models and close to the impact they generate. The team is increasingly focused on applied AI work — building and deploying LLM-powered and agentic systems in production — so strong ML engineering instincts can be a valuable addition alongside your architecture and leadership experience.

What You’ll Do

• Partner with delivery and executive stakeholders to scope, design, and lead implementation of AI-driven solutions, including LLM and agentic systems
• Identify transformational opportunities in messy, ambiguous workflows and turn them into repeatable systems
• Lead architecture design and trade-off discussions across performance, scalability, cost, and reliability — including for ML/AI-specific systems like RAG pipelines and production-grade AI applications
• Own projects from first discovery call through full deployment — including client-facing delivery, internal coordination, and post-launch iteration
• Build shared infrastructure, reusable components, and internal playbooks to level-up the team, including MLOps practices for model versioning, monitoring, and lifecycle management
• Coach and mentor mid-level engineers and help shape the culture of forward-deployed AI engineering at Invisible