Associate Forward Deployed Engineer

Tenex; New York (NY); On-site; USD 110,000 - 150,000; Full time

Job summary

Tenex is building a software factory around coding agents to deliver production-ready systems that bridge business problems to real results. The platform emphasizes agent autonomy, end-to-end delivery, and collaboration with C-suite leaders to turn AI capabilities into measurable value.

The Associate Forward Deployed Engineer will own the outcome, work directly with customers, frame problems, and choose architectures, shipping integrated systems in production at the frontier of what is possible.

Qualifications

• Shipped production software that real users depend on.
• Built something real with LLMs and can honestly explain outcomes.
• Able to learn a new domain quickly and contribute within days.
• Capable of articulating a technical plan and defending it when challenged.

Responsibilities

• Embed with the customer to frame problems and define architecture.
• Decide on product direction and defend it when senior stakeholders disagree.
• Ship complete systems with architecture decisions and end-to-end ownership.
• Measure production outcomes and iterate after deployment.

Job description

Our mission is to turn frontier intelligence into real results for every business. We charge for results, not access.
Tenex was founded in New York City in 2025. We are roughly 50 people today and our end-of-year target is 150. We ship production AI for Fortune 500 clients, and one in three of our engineers has founded a company. Our engineers work directly with C-suite leaders to find where AI can change their business, and then build the custom systems that make it work in production at the boundary of what is possible.
The Associate Forward Deployed Engineer is how that happens. It is not a support role and not a services role. It is the role.

Why the work is hard

We now have superintelligence on demand, but model capabilities are advancing faster than companies can adapt, so most of that value goes untapped. The hard part starts after the model. Building reliable systems around frontier models means connecting fragmented data, rethinking workflows from first principles, building evaluations and controls, and adapting as fast as the frontier labs ship.
The most important work now happens at the edge, where AI meets real world conditions. That is the only place we work.

What You Will Do

You own the outcome. You work with the customer, frame the problem, choose the architecture, build the system, ship it, and stay accountable for what happens in production:

• Discover. You embed inside a real business, learn how it makes money, and find where AI would create real value rather than a demo.
• Decide. You turn ambiguity into a product direction and a technical plan, and you defend both when someone senior disagrees.
• Build. You ship the system, not a disconnected piece of it, and you make the build-versus-buy and architecture calls yourself.
• Own. You measure what happens in production and keep improving it after the merge.

At this level you own a workstream outright. You decide what gets built and in what order, you are the technical voice the client hears on that work, and when the scope turns out to be wrong you are the one who says so and re-cuts it. You are closer to a founder inside an engagement than a contributor on an assembly line.

The platform you help build

Tenex is rebuilding the software development lifecycle around coding agents. The goal is a software factory that carries work from a business problem to a production system with increasing agent autonomy, where planning, architecture, implementation, testing, review, and delivery become one connected system rather than a series of handoffs.

You will learn how to structure work for agents, engineer durable context, evaluate outputs, and decide where deterministic software and human judgment remain in control. The system is early, it is already used by everyone here, and it provides real leverage today. Engineers who join now will help decide how it works.

The work itself

Every engagement changes the problem. Each one brings a different industry, architecture, and set of technical constraints, across finance, software companies, media, industrials, energy, logistics, professional sports teams, healthcare, consumer packaged goods, real estate, and market research. That exposure keeps the work interesting and forces you to learn faster than you have anywhere else. Recent work includes the operating system a private equity firm with $115 billion under management runs on, AI verification of every in-store sampling station for a national retailer with 70,000 field staff, and camera systems across thousands of retail stores that catch theft, manage queues, and track inventory on the floor.
Some of what we hit has no established solution yet, including agent orchestration, evaluation, memory, and long-running execution. You will work at the boundary between what the newest models appear capable of and what can actually be made reliable in production.

Growth

Tenex has more demand than capacity. New engagements, products, and teams create a constant need for people who can own larger problems, so nobody here waits for a seat above them to open. Engineers who deliver are trusted with broader technical decisions, more important client relationships, and greater responsibility across the company. One year here is worth several years elsewhere in engineering, product, customer, and leadership growth, because the mechanism is the work itself rather than a development program.

Who We Look For

We are a people business. The engineers we put in front of clients are the product, so hiring is the highest-stakes decision we make each week. The pattern we bet on is the high-slope generalist, meaning a strong engineer who has shipped real software, moved hard into AI, and improves faster than their peers. At this level, you are likely one or two years into your career, though we care much more about what you have built than how long you have been building. We look for a few specific things:

• You have shipped production software that real people depended on, whether at a job, at your own company, or on something you built alone.
• You have built something real with LLMs and can explain honestly where it worked and where it did not.
• You get productive in an unfamiliar domain within days, because every engagement asks you to.
• You can explain a