invisibletech

Staff Software Engineer, Forward Deployed

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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

AWSData ScienceDockerETLGCPKafkaKubernetesPython

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