Founding Forward Deployed Engineer
First Forward Deployed Engineer at a seed-stage AI startup building a revenue-execution platform for B2B enterprises. Day to day: lead enterprise customer onboardings, map messy data warehouses to business concepts, build verified SQL semantic layers, and deploy/monitor LLM agent workflows, on-site in NYC. Core stack: SQL, dbt, Python, LLMs, AI agents.
- Compensation
- $180K–$250K / yr
- Location
- New York, New York, United States
- Work setup
- Not specified
- Seniority
- Not specified
- Employment
- Full time
- Experience
- 2+ years
- Category
- Solutions engineering
- Posted
Skills
Must have
- 2–5 years in a forward deployed, data science, or customer-facing engineering role
- Track record of personally leading technical customer deployments (data access, schema mapping, stakeholder alignment, demonstrating value)
- Experience building and shipping production data pipelines or systems end-to-end
- Strong SQL and data warehouse skills, including poorly documented or legacy schemas with self-validated output
- Daily AI-first workflow using tools like Claude or Cursor, with built or tailored agent-based workflows
- Solid applied statistics sense (distributions, seasonality, validation design, distinguishing noise)
- Background in forward deployed/customer/solutions engineering at a data or AI company, analytics engineering or data science plus customer-facing delivery, or consulting/PE/banking with CS and enginee
- Confidence to hold a technical position under customer pushback while staying open to evidence
- Comfort with detailed, repetitive data verification
- Bachelor's degree in computer science, data science, statistics, or quantitative STEM from a top-tier university
- Based in or willing to relocate to New York City, working on-site five days a week
Nice to have
- Experience at an early-stage startup (seed through Series B)
Full description
Founding Forward Deployed Engineer
New York City · On-site · Full-timeCompensation: $180K–$250K base + competitive equity
About the Company
Our client is a well-funded, seed-stage AI company building the revenue execution platform for B2B enterprises. Their product pairs LLMs with structured customer data to tell post-sales teams which accounts to prioritize, why, and what to do next, automating work that account management and customer success teams have long done by hand. They are already live with Fortune 500 customers and have meaningful contracted revenue.
Founded 2025 · Small founding team · Industry: AI, B2B, Enterprise
The Role
As the first Forward Deployed Engineer, you will own the technical deployment of the platform into enterprise customer environments. You will sit between messy customer data warehouses and production agent workflows, turning each customer's business knowledge into verified data models that drive measurable revenue outcomes. You will work directly with the founding team and report to the CTO.
What you'll be doing
- Leading new customer onboardings from start to finish: scoping data access, exploring undocumented warehouses, and mapping complex schemas to business concepts like churn, expansion, and ARR
- Building and validating the semantic layer by working with customer stakeholders, reconciling conflicting definitions, and encoding them as verified SQL
- Deploying workflows and agent pipelines into customer instances, then monitoring them to make sure they deliver results
- Running 2–3 customer engagements at a time in shared channels with customer data and engineering teams (roughly a quarter of your time customer-facing, the rest hands-on data work)
- Using the internal agent tooling daily and shaping how the team improves evals, validation, and the agent harness
Tech stack: SQL, dbt, data warehousing, Python, LLMs, AI agents, signal engineering, eval frameworks
Requirements
- 2–5 years in a forward deployed, data science, or customer-facing engineering role
- A track record of personally leading technical customer deployments, including data access, schema mapping, stakeholder alignment, and demonstrating value
- Experience building and shipping production data pipelines or systems on your own, from writing the code through debugging and deployment
- Strong SQL and data warehouse skills, including working confidently in poorly documented or legacy schemas and producing output you have validated yourself
- A daily AI-first workflow: you use tools like Claude or Cursor and have built or tailored your own agent-based workflows
- Solid applied statistics sense, such as understanding distributions, seasonality, and validation design, and knowing when a result is noise