AI/ ML Engineer

AI Engineer

New York City, NY (in person, relocation offered)

About the Company

Our client builds mission-critical trust and safety infrastructure for some of the most important

digital platforms in the world. Bad actors have always abused the internet, and AI is

accelerating fraud, abuse, and manipulation at a scale and speed no human team can handle

alone. Their platform gives trust and safety teams a single place to write and enforce policy,

deploy AI agents against abuse in real time, investigate threats, handle regulatory reporting,

and measure whether their safety programs are working.

Customers include some of the largest internet platforms in the world, and decisions made in

the software directly determine what stays up, what comes down, and how users are treated.

The company is a small, fast-moving, venture-backed team that values being intentional,

direct, and deeply focused on solving real customer problems.

Why This Role

The company is growing quickly and is adding an AI Engineer to partner with the existing AI

Engineers, Data Scientist, and Data Engineer. You’ll build the ML systems that make the

platform faster, more efficient, and more accurate, turning the enormous volume of customer

decision data it processes into production models that directly shape customer outcomes.

We’re looking for a builder. Someone who has taken models from messy data to production at

scale, who reaches for a gradient-boosted tree before a transformer when that’s the right call,

and who has stood up ML infrastructure from scratch rather than inheriting it fully built. What

matters most is judgment: knowing the smallest, most efficient, most reliable model for the

job, and understanding both ML methods and LLMs deeply enough to make that call yourself

rather than defaulting to whichever one you know best. This role needs someone who cares

as much about precision-recall tradeoffs, class imbalance, and serving latency as they do

about model architecture.

What you’ll do

• Turn real-world customer data into something a model can actually learn from, then

decide what model approach fits: a classical classifier when it wins on cost and latency,

a fine-tuned LLM when the tradeoff is worth it, a third-party API as a bootstrap. You own

the full path from data to decision, not just the model.

• Improve the classification pipeline, confidence cascading, and detection strategies so

harmful content is caught efficiently, balancing cost, latency, and accuracy deliberately.

• Develop intelligent features that help moderators make decisions, organize platform

content, and reveal patterns across the data.

• Partner with Engineering to build out the in-house model training, hosting, and

inference platform.

• Design and build the evaluation and metrics infrastructure customers rely on, including

how classifier scores and model outputs are calculated, stored, surfaced, and iterated

on.

• Partner with the Founding Data Scientist and AI Engineers to shape the agent

evaluation architecture, measuring whether the agent fleet is making the right decisions

with the right tools at the right cost.

• Partner with the Data Engineer to shape the data infrastructure powering the ML

systems, ensuring model training, feature pipelines, and production inference have the

right data flowing at the right latency and scale.

• Mentor teammates and raise the ML bar across the company as its ML capability

matures.

What we’re looking for

• 5 to 8+ years of machine learning engineering experience on a small team, with a

strong track record of shipping ML systems (gradient boosting, tree-based models,

classifiers, embedding-based methods) to production.

• You’ve taken a classification problem from messy, unlabeled, real-world data all the

way to a model that shipped and served production traffic.

• You understand LLMs well enough to make an informed, defensible call about when an

LLM is worth its cost and latency versus a classic model.

• Real, hands-on experience building classifiers under severe class imbalance, where

the signal you care about is a small minority of the data.

• Thrived in environments where the ML infrastructure wasn’t already built for you: you’ve

stood up training pipelines, serving infrastructure, evaluation harnesses, and monitoring

from scratch rather than inheriting a mature platform.

• Startup or small/mid-size company experience where you owned meaningful scope …