Forward Deployed Engineer

Industry: AI Infrastructure / Enterprise AI

Location: Remote (US)

Employer: Confidential

Employment Type: Full-time. Founding hire.

Salary: Based on experience and fit

About the Company

Our client is an early-stage AI infrastructure company building the layer that makes real-world human experience observable, structured, and learnable by AI systems. Language models learn from what people have said. Our client captures what people actually did: the decisions, revisions, exceptions, approvals, and outcomes that play out inside large enterprises, and turns that into structured data with clear provenance that AI systems can learn from.

The founding team has published in top-tier scientific journals, secured research funding from a major AI lab, and built and deployed AI systems inside Fortune 500 companies.

About the Role

Our client is hiring a Forward Deployed Engineer to take the product into customer environments and make it work. You'll sit between their stack and the customer's systems: mapping how each customer actually operates, deploying and integrating the platform, and building agentic solutions on top of their real processes.

This is a hands-on, heavily customer-facing role for an engineer who is as comfortable inside a messy legacy system as in the room with the customer, turning ambiguous problems into working technical solutions.

Responsibilities

Own customer engagements end to end: embed with the customer, build the relationship, and take responsibility for the outcome.
Work out the real problem. Break down ambiguous situations, spot the gaps that emerge as you map how the customer operates, and turn them into a clear technical plan.
Build the solution. Map customer processes and build agentic applications on top of them, adapting the approach when the first one does not fit.
Deploy and integrate the platform into the customer's environment, including older or legacy systems.
Work with whatever infrastructure the customer has, translating the stack across clouds where needed (for example, from GCP to a customer's AWS or Azure).
Work closely with the research and engineering teams, carrying what you learn in the field back to them, and using enough ML fluency to run inference and light fine-tuning.
Travel to customer sites for extended, multi-month engagements.

Requirements

A track record of owning technical customer engagements end to end, likely as a forward deployed, solutions, or implementation engineer, or in a similar hands-on customer-facing role. What you have done matters more than the title.
Exceptional customer instincts. You can lead the room with a customer, break down an ambiguous problem in real time, and turn it into a workable, well-scoped plan.
Comfort with ambiguity and high agency. You define the scope, choose the approach, and build the path yourself rather than waiting for a detailed spec.
Strong generalist engineering with solid Python, and the range to move into unfamiliar and sometimes legacy systems and make them work.
Breadth across cloud platforms (AWS, GCP, and Azure), enough to translate a solution to whatever the customer runs rather than a single stack.
General ML fluency: comfortable running inference on models and doing light fine-tuning rather than model training, enough to work alongside the research team.
Willingness to travel, including multi-month on-site engagements that move between customer locations.

Nice To Have

Experience as an early or founding field engineer, taking a young product into its first real customer deployments.
A background that blends engineering with direct customer ownership, such as hands-on solutions or sales engineering where you built as much as you presented.
PyTorch experience.
Terraform and infrastructure-as-code familiarity.
Experience deploying software into regulated or enterprise environments.

Why Join?

Be the person who takes a new category of AI infrastructure into the real world, inside the world's largest enterprises, and owns whether it lands.
A founding-stage seat with direct customer impact and real influence over how the product is delivered.
Work across the whole problem: discovery, customer relationship, engineering, integration, cloud, and applied AI, rather than a narrow slice.
Work directly with founders who have built and deployed AI inside Fortune 500s, and alongside a strong research team.
Meaningful equity and the upside of joining an early, high-conviction team.