invisibletech
Staff Software Engineer, Forward Deployed
At a Glance
- Location
- Austin, Texas - Hybrid; New York - Hybrid; San Francisco Bay Area - Hybrid
- Experience
- 8+ years
- Compensation
- nges by location are: Tier 1: $213,000 - $300,000 Tier 2: $194,000 - $284,000 *
- Posted
- 2026-03-18T12:35:13-04:00
Key Requirements
Required Skills
Domain Knowledge
- Engineering
Requirements
8+ 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:
Compensation & Benefits
Invisible is committed to fair and competitive pay, ensuring that compensation reflects both market conditions and the value each team member brings. Our salary structure accounts for regional differences in cost of living while maintaining internal equity.
For this position, the annual salary ranges by location are:
Tier 1:
$213,000 - $300,000
Tier 2:
$194,000 - $284,000
Responsibilities
As a Staff 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.
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