Principal Backend Engineer (Forward Deployed)

Confirmed live in the last 24 hours

Invisible Tech

Austin, Texas - Hybrid; New York - Hybrid; San Francisco Bay Area - Hybrid

Hybrid

Posted April 6, 2026

Job Description

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 client.

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.

**Please note - We are always looking to connect with talented individuals for this role. This posting is part of our ongoing effort to build a strong pipeline of candidates and may not correspond to an immediate opening. Candidates who move forward in the process may be considered for current and future opportunities as they arise.

What You’ll Do

Partner with delivery and executive stakeholders to scope, design, and lead implementation of AI-driven solutions
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
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
Coach and mentor mid-level engineers and help shape the culture of forward-deployed AI engineering at Invisible

What We Need

10+ years of software engineering experience, including significant time spent building data, ML, or backend systems
Python & ML/LLM Frameworks: Deep proficiency in Python with hands-on experience using Hugging Face, LangChain, OpenAI, Pinecone, and related ecosystems
Deployment & Infrastructure: Skilled in full-stack and API-based deployment patterns, including Docker, FastAPI, Kubernetes, and cloud environments (GCP, AWS)
Platform Orchestration: Experienced with workflow orchestration libraries, pub/sub systems (Kafka), and schema governance
Data Management: Expertise in data governance and operations, including Unity Catalog and policy management, cluster/job orchestration, data contracts and quality enforcement, Delta/ETL pipelines, and replay processes
Strong product and system design instincts — you understand business needs and how to translate them into technical architecture
Experience building usable systems from messy data and ambiguous requirements
Excellent communication and client-facing skills; you’ve led conversations with technical and non-technical stakeholders alike
Proven experience owning projects from scoping through deployment in ambiguous, high-stakes environments
Be willing to be on-call for our customers when situations arise
Ability to travel roughly 25–50% of the time, sometimes short-notice trips—primarily across North America with occasional international roll-outs—to work directly on-site