Founding Forward Deployed Engineer (Tribeca)

Job Description

Job Description

Founding Forward Deployed Engineer

Location: Tribeca, New York City
Employment Type: Full-time
Work Arrangement: On-site, 5 days per week
Salary: $180,000–$250,000 base
Equity: Competitive equity
Visa: Open to eligible visa transfers, including H-1B and OPT
Relocation: Up to $10,000 for candidates relocating from outside New York

A well-capitalized, post-revenue enterprise AI company is hiring a Founding Forward Deployed Engineer to lead the technical deployment of its platform within complex customer environments.

The company is building an AI-powered revenue execution platform that helps enterprise teams identify which customers to prioritize, understand why, and determine what actions to take next. Its technology combines large language models with structured customer data to automate account scoring, customer segmentation, and revenue-expansion workflows.

Already working with Fortune 500 customers and generating meaningful revenue, the company is scaling with a lean, highly technical founding team. This hire will own one of the most important paths in the business: turning messy customer data and institutional knowledge into reliable, production-ready AI workflows.

• Own enterprise customer deployments from initial data access through production launch and measurable value delivery.

• Explore undocumented data warehouses and map complex or legacy schemas to business concepts such as churn, retention, expansion, and annual recurring revenue.

• Interview customer stakeholders to understand business logic, reconcile conflicting definitions, and translate institutional knowledge into validated SQL queries.

• Build and validate the semantic and ontology layers that power customer-facing AI agents.

• Develop, deploy, and maintain production data pipelines and agent workflows within customer environments.

• Independently write code, diagnose technical issues, and deploy systems without extensive oversight.

• Monitor deployed workflows, validate their outputs, and ensure they produce measurable customer outcomes.

• Manage two to three customer engagements simultaneously while collaborating directly with customer data, engineering, and business teams.

• Use and help improve internal agent tooling, evaluation frameworks, validation processes, and guardrails.

• Balance customer communication with highly detailed technical execution; approximately 70–80% of the role involves hands-on data exploration, transformation, and verification.

• Approximately 2–5 years of experience as a Forward Deployed Engineer, Customer Engineer, Solutions Engineer, customer-facing Software Engineer, or in a closely related technical role.

• Experience owning end-to-end technical customer deployments involving data access, schema mapping, stakeholder management, production delivery, and value validation.

• Deep SQL and data-warehouse fluency, including the ability to navigate messy, legacy, or undocumented schemas independently.

• Experience with dbt, data pipelines, data warehouses, or comparable production data systems.

• Strong hands-on programming skills, ideally in Python, with experience independently building, debugging, and deploying production systems.

• An AI-native working style, including regular use of tools such as Claude or Cursor.

• Experience building or customizing agent-driven workflows, not simply using off-the-shelf AI tools.

• Applied data-science literacy, including familiarity with distributions, seasonality, validation design, and distinguishing meaningful findings from noise.

• Ability to translate ambiguous business requirements into reliable technical systems and validated data models.

• Strong customer-facing communication skills, including the ability to defend technical decisions under pressure and change direction when presented with better evidence.

• High tolerance for detail-oriented and repetitive data-verification work.

• A bachelor's degree in computer science, data science, statistics, or another quantitative STEM discipline.

• A strong academic pedigree or experience at a recognizable technology, data, consulting, or financial-services organization; candidates should have at least one of these indicators.

• Willingness to work on-site in New York City five days per week.

You may be especially well suited for this role if you have worked as:

• A Forward Deployed Engineer, Customer Engineer, or Solutions Engineer at a leading data or AI company.

• An Analytics Engineer or Data Scientist who transitioned into customer-facing implementation and delivery.

• A technical consultant with a genuine computer science foundation and hands-on engineering experience.

• An early-stage startup engineer who independently owned customer deployments and production systems.

• Experience working at an early-stage startup, ideally from Seed through Series B.

• Experience deploying LLM-powered products or AI agents in production.

• Experience building evaluation frameworks, validation systems, semantic data layers, or signal-processing systems.

• Founder or founding-engineer experience.

• A track record of succeeding in ambiguous, fast-moving environments without extensive direction.

• Genuine enthusiasm for detailed data investigation, testing, and verification.

• Join a lean founding team tackling a multibillion-dollar gap in enterprise software.

• Help shape a platform already serving major enterprise customers.

• Own a critical, high-impact function rather than inheriting a narrow piece of an established system.

• Work directly with company leadership, engineering, and customers.

• Receive an aggressive compensation package with competitive equity.

• Comprehensive medical, dental, and vision coverage for employees and their families.

• Parental leave, a monthly wellness stipend, daily lunches and snacks, and a flexible vacation policy.

• Up to $10,000 in relocation support for candidates moving from outside New York.

• Culture and experience interview

• Technical interview

• Half-day on-site working simulation based on a realistic customer deployment scenario