Arpeely is a Data-Science startup, leveraging data analysis, ML, engineering, and multi-disciplinary thinking to gain a market edge and exploit hidden opportunities in real-time advertising. Processing over 350k requests per second and serving over 20B sub-second predictions daily, we build and operate Machine Learning algorithms running on the world's largest Real-time-bidding (RTB) Ad-Exchanges. Arpeely is a Google AdX vendor and serves clients spanning from startups to Fortune-50 companies.
What you'll do
As our AI Engineer you will build the company's agentic backbone: long-running, self-improving AI agents that execute real growth and algo workflows end to end, from researching features and generating creatives to analyzing experiments and reducing our AI costs. The role sits in the Engineering department but demands algorithmic thinking just as much as engineering excellence. You will need to turn a fuzzy goal into a measurable objective with a feedback loop that knows when the agent is wrong. This is not a prompt-engineering role.
This position touches every part of the company. You will work with engineers, data scientists, creative, and growth teams, and you will need to hold a professional conversation with each of them in their own language: architecture and reliability with engineers, metrics and experiments with data scientists, and business impact with growth.
Design and build long-running agents that own multi-step growth and algo workflows, from feature research pipelines to creative generation and validation.
Build the feedback loops that make agents trustworthy: evals, assertions, state management, self-correction, and human-in-the-loop review for both technical and non-technical users.
Own AI cost reduction across the company (Claude / Cursor and our products), including monitoring token spend, model routing, context optimization, and turning repeated workflows into cheap reusable skills.
Own the company's AI knowledge hub, including how knowledge is routed, kept fresh, and made self-improving from real agent sessions.
Define the metrics and guardrails that let agents run for hours or days unattended without silent degradation or runaway cost.
Partner with every department to identify workflows worth automating, and prove the automation actually works.
Continuously evaluate new models, tools, and agent frameworks, and decide what is worth adopting based on real results.
Work in a results-driven, high-paced, rewarding environment.
Requirements
4+ years of hands-on experience as a Software Engineer, Algorithm Developer, or similar, with proven ability to ship robust, production-grade systems.
Strong problem-definition skills: you turn "make it better" into a metric, a baseline, and a decision rule before writing code.
Algorithmic thinking and comfort reasoning about optimization, evaluation, and trade-offs.
Strong Python, and comfort building and debugging systems with non-deterministic components.
Hands-on experience building with LLMs beyond chat: agents, tool use, evaluation harnesses, or automated pipelines.
Strong communication across disciplines. Able to talk shop with engineers, data scientists, creative, and growth alike.
Experience with cloud environments (GCP preferred) and data tooling (SQL / BigQuery a plus).
A cost-aware mindset: you treat tokens and dollars like memory and CPU.
Strong analytical mind with proven problem-solving abilities. Independent, proactive, and able to drive ambiguous projects to completion.