Senior Applied AI Engineer

About 1001

1001 builds AI-powered operational intelligence for the world's most complex, data-heavy environments. We turn fragmented data into a live, unified model of operations and use it to drive better decisions and solve high-stakes problems. Our work sits inside government and large enterprises, in environments defined by critical operations and messy, real-world data.

Our engagements start with forward-deployed teams embedded in the customer environment. They work on real data, build quickly, and iterate until the system proves itself, then scale it across the organization.

The company is backed by Lux Capital, General Catalyst, civ, Hanabi, Sanabil, and 9Yards, with angels including Chris Re, Amjad Masad, Karim Atiyeh, Kareem Amin, and Russell Kaplan.

Working at 1001

We take on high-stakes problems in environments where mistakes carry real consequences. That demands an uncompromising bar, real speed, and systems that hold up under live operations. The people who thrive set that bar for themselves and keep raising it. They own outcomes end to end, bring rigor to everything, and lift everyone around them.

About the role

You'll sit between deep machine learning and full-stack engineering, taking machine learning prototypes and shipping them as production AI features inside live enterprise deployments. This is not research, and it is not wrapping a model in an api. It is the harder middle: turning an ambiguous product ask into a workflow that operators trust and that holds up under real load and real data.

You'll own systems end to end, from model code to product surface. That means the APIs, services, evaluation harnesses, and monitoring that make a prototype safe in production, and the architecture, security, and maintainability decisions behind them. You'll work across several customer deployments at once on the core engineering team, moving between contexts and deciding where to invest and where to ship.

Scope is broad and changes fast. Deployments are rarely clean, so you're comfortable with hybrid, on-prem, and sovereign cloud constraints and the security realities they bring.

Judgment matters more than fluency in any single framework. As a senior engineer, you raise the bar around you through mentorship and review, not just individual output.

What you'll work on

• Take machine learning prototypes to production: the apis, services, evaluation harnesses, and monitoring operators can rely on.
• Turn ambiguous product asks into real AI workflows that solve the operational problem, not just wrap models behind endpoints.
• Build sophisticated AI features from product requirements using modern large language model and agent tooling, without running the underlying research yourself.
• Own systems end to end across multiple live enterprise deployments, from model code to product surface.
• Make and defend tradeoffs between speed, quality, security, and maintainability, where mistakes carry real consequences.
• Ship into hybrid, on-prem, and sovereign cloud environments with the constraints enterprise and government customers bring.
• Set the standard for how production AI gets built through mentorship and review.

Requirements

• 6+ years in software engineering, with 2+ deploying and maintaining machine learning models in production.
• Engineering-first and machine learning-literate: you reason about models comfortably without being a researcher.
• Strong fundamentals in distributed systems, APIs, data pipelines, and testing.
• Hands-on with modern large language model and agent tooling: frameworks like LangChain, LlamaIndex, or dspy, vector stores, and eval and observability tooling.
• Production experience in both TypeScript and Python.
• A track record of shipping machine learning-backed features at a top-tier tech company, AI lab, or fast-moving startup.
• Comfort with broad, fast-shifting scope in an early-stage team.
• The judgment to make pragmatic tradeoffs, which we value over knowledge of any one tool.
• Effective use of AI coding agents, while staying fully accountable for the code they produce.
• A top computer science or engineering degree, or equivalent demonstrated experience.

Nice to have

• Prior experience deploying into enterprise environments.
• Familiarity with hybrid, on-prem, or sovereign cloud deployments.