Senior Software Engineer, Forward Deployed

Senior Software Engineer, Forward Deployed

Austin, Texas - Hybrid;
New York - Hybrid;
San Francisco Bay Area - Hybrid;
Washington DC–Baltimore - Hybrid

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.

As a Senior Software Engineer, Forward Deployed Engineer (FDE) you'll work directly with clients and our internal delivery teams to build and deploy AI‑powered solutions that transform how work gets done. You’ll own projects end‑to‑end: scoping ambiguous problems, prototyping AI workflows, and deploying scalable systems on top of our products — all while interfacing with technical and non‑technical stakeholders.

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.

• Collaborate with delivery leaders to scope technical solutions to operational problems
• Identify workflow optimizations through deep engagement with customer problems and work to build into a stable and scalable solution
• Design and implement AI‑powered workflows using LLMs, embedding models, retrieval systems, and automation tools
• Translate messy real‑world constraints (e.g., inconsistent data, latency requirements) into elegant engineering solutions
• Iterate quickly based on real‑time feedback from operators and clients
• Build reusable tooling and infrastructure that accelerates future deployments

• 6+ 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, Lang Chain, 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 with clients
• Strong engineering background demonstrated by a Bachelor’s degree in Data Science, Computer Science and related fields OR equivalent professional experience

Invisible is committed to fair and competitive pay, ensuring that compensation reflects both market conditions and the value each team…