Forward Deployed Engineer
Role Description
About ForgeVista
ForgeVista deploys AI inside real businesses: production systems that change how work gets done, not slide decks or proofs of concept. Our forward-deployed teams embed with operating companies to build, ship, and scale AI at startup speed with enterprise quality. We work AI-first and CLI-native, and we hire for evidence, not pedigree.
Three things define how we work: AI Now (we ship with AI daily, not "someday"), CLI Native (the terminal is our cockpit, every role, every person), and High Agency (you own the outcome and move without waiting for permission).
Before you apply, please read our culture deck: https://forgevista.ai/culture — our culture isn't aspirational, it's how we actually operate. If it doesn't resonate, this probably isn't the right fit.
Full role, time-allocation, and skills map: https://forgevista.ai/careers/forward-deployed-engineer
The role ------------
You'll embed with a client, translate their messy business problems into AI-powered systems, and ship those systems into production. Not slide decks, not proofs of concept. This is a build role: you write the code, design the workflows, and own the result. Remote-first, with travel set by each client (fully remote to ~25–50% on-site).
How we work: ship with AI daily, live in the CLI, and operate with high agency, owning the outcome and moving without waiting for permission. Please read our culture deck before applying; we'd rather you self-select than find a mismatch later.
What you'll do ------------------
- Sit with a client's team and map how the work actually happens, not how the org chart says it does
- Design AI-native replacements for manual workflows and build them to run in production
- Prototype in the CLI with AI agents, test against real data, and iterate with the client
- Run eval loops that prove a solution works, not just demos well
- Ship automations that replace hours of manual effort
- Write playbooks so the next similar deployment starts ahead
What we look for --------------------
- 2–5 years in business analysis, consulting, operations, or a similar problem-solving role
- Something you've built and shipped with AI tools that people actually used
- Comfort in the terminal; you pair with AI agents (Claude Code, Codex, Gemini) as a daily habit
- Strong discovery instincts: walk into a process, understand it, and find where it breaks
- Direct experience working with clients or stakeholders
We hire on proof, not pedigree. No CS degree or prior "engineer" title required.
Nice to have ----------------
- Prior client-facing delivery or implementation work
- A portfolio of automations, prototypes, or tools you can walk us through
Compensation & logistics -----------------------------
- $120K–$180K base, benchmarked and paid regardless of outcomes, plus Sprint Point Velocity incentives tied to deployment impact
- Health, dental, and vision coverage, plus a professional-development budget
- Remote-first; travel depends on the client (fully remote to ~25–50% on-site)
- To apply: share something you've built with AI: a repo, a Loom, or a short writeup
If reading this made you excited rather than worried, we should talk.
How to apply
Apply below. We evaluate artifacts over résumés — share something you've built with AI.
*ForgeVista is an equal opportunity employer. We evaluate candidates based on demonstrated ability and proven immersion — not pedigree or credentials.*
The pay range for this role is:
120,000 - 180,000 USD per year(Remote)
About Forward Deployed Engineering
Forward Deployed Engineers are embedded directly with customers to build custom solutions, integrate products into existing infrastructure, and bridge the gap between product engineering and customer success. The role combines deep technical skills with the ability to operate in client environments and translate business requirements into working software.
Originally pioneered by Palantir, the FDE model has spread across AI, enterprise SaaS, and cloud infrastructure companies. FDEs write production code, architect integrations, train customer teams, and feed product insights back to the core engineering organization. At companies like OpenAI, Salesforce, and Databricks, FDE teams are treated as elite engineering units that can ship custom solutions in days rather than quarters.
Typical FDE stack: Python, TypeScript, SQL, REST/GraphQL APIs, cloud platforms (AWS/GCP/Azure), and increasingly LLM APIs and AI orchestration frameworks. Strong communication and the ability to context-switch between technical and business conversations are as important as coding ability.
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