Forward Deployed Engineer – New York

Join the Forward Deployed Engineering team at one of the fastest scaling applied AI companies in New York, building the deployments that put large language model systems into live production inside some of the most regulated and data heavy enterprises in the country. The team sits at the exact point where the product meets the customer, which means an engineer here owns a deployment end to end: running the discovery, writing the integration code, designing the evaluation harness, and staying with the account until the workflow is live and measurably working. Working across Python, TypeScript, model orchestration and enterprise data systems, this is one of the highest leverage engineering seats in the New York AI scene, and a Forward Deployed Engineer here writes code that a Fortune 500 team is using inside the same quarter, not code that disappears behind eighteen months of enterprise procurement.

The Opportunity

We are seeking a Forward Deployed Engineer to join a small, senior team in New York as the technical owner of a named portfolio of enterprise accounts. This is a builder's seat rather than a support seat, which means direct ownership of what gets built and how, genuine authority to say no to a bad request, and the autonomy that only a flat, engineering led organisation can offer. You will spend your weeks moving between writing production code and sitting in rooms with customer stakeholders, translating an ambiguous business process into a scoped technical build, then shipping it. You will also shape the platform itself, because everything you learn about what enterprises actually pay for goes straight back into the roadmap, and forward deployed engineers here carry unusual weight in that conversation. The company has moved from design partners to signed enterprise contracts faster than it can deliver them, which is the reason this seat exists.

Key Responsibilities

• Own end to end delivery of customer deployments, from the first discovery workshop through integration, evaluation and production launch, typically on four to eight week cycles.
• Write production Python and TypeScript against both the core platform and customer systems, building integrations, data pipelines, retrieval layers and agent workflows on top of real enterprise data.
• Design and run an evaluation harness for every deployment, agreeing with the customer up front what working means, then instrumenting the system so the answer is measured rather than asserted.
• Sit directly with customer stakeholders, from engineering leads through to line of business executives, and convert an ambiguous process into a scoped, sequenced technical build with a defined success metric.
• Act as the primary technical contact across your accounts, owning solution design, technical escalation, security review and the handover into ongoing support.
• Turn repeated deployment work into platform features, and carry customer reality back into the core engineering team with evidence rather than anecdote.
• Debug across the entire stack inside live customer environments, including model behaviour, retrieval quality, latency, permissions models and upstream data quality.
• Support the commercial team on technical discovery and proof of concept work for strategic prospects, including scoping calls and architecture reviews with prospective customers.

What We're Looking For

• Typically 4 to 8 years in software engineering, including meaningful time in a customer facing engineering seat: forward deployed, solutions engineering, implementation, professional services, deployment strategy, or founding engineer at an early stage company.
• Strong production Python, plus enough TypeScript and SQL to move across a full stack quickly without waiting on anyone else.
• Hands on experience taking something built on large language models into production: retrieval, agent orchestration, structured extraction, evaluation or fine tuning. Demos and side projects will not carry the interview. It has to have held real traffic and real users.
• Comfort with enterprise data reality, meaning messy schemas, legacy systems, permissions models, single sign on, and a security review that has to be passed before anything ships.
• Genuine commercial instinct. You can hold a room with a managing director, push back on a request that will not work, and re-scope a deployment mid flight without losing the account.
• Willingness to travel roughly 25 to 40 percent within the United States to customer sites, in blocks rather than continuously.
• Working fluency with cloud infrastructure (AWS, GCP or Azure), containers and CI/CD, and the instinct to instrument a system before it breaks rather than after.
• Excellent written communication. Deployment decisions here are documented, and the quality of your writing determines how far your work travels inside the customer.
• Degree in Computer Science or a related field, or equivalent practical experience.
• Right to work in the United States. Visa transfer will be considered for exceptional candidates.

Also Useful, Not Required

• Prior forward deployed or deployment strategy experience at Palantir, or at a company that adopted the model directly from it.
• Domain depth in financial services, insurance, legal or healthcare, which is where this company's customers sit.
• Experience as one of the first engineers somewhere that scaled from ten customers to a hundred, and the scar tissue that comes with it.

Why Join?

• Deployment work is the shortest feedback loop in applied AI. You will see what genuinely works on real enterprise data months before that becomes common knowledge, which is the most valuable thing anyone can hold in this market right now.
• The role converts. Forward deployed engineers move from here into founding engineering roles, product leadership and companies of their own, because they leave knowing precisely which workflows enterprises will pay for and which they will not.
• New York has overtaken San Francisco as the largest United States market for this role, so you build the specialism in the city where it is worth the most, without relocating to the Bay Area.
• Compensation is set against the scarcity of the profile rather than a standard engineering band, with base, meaningful equity, and a performance component tied to deployments landed.
• Small senior team, no layers between you and the founders, and technical decisions that stay yours once you have made them.