AI Engineer

Sensor Tower’s digital intelligence platform provides unparalleled insight into market trends, advertising, competitor’s strategies, and consumer/user audience across mobile, web, and gaming. Sensor Tower’s insights are trusted by more than 2,500 enterprises worldwide across tech, retail/commerce, entertainment/media, gaming, consumer packaged goods and finance.

Our mobile gaming portfolio includes AppMagic and Playliner, which help game developers and publishers understand the mobile games market and how top games run their LiveOps.

We’re looking for an AI Engineer to build LLM-powered prototypes for our gaming analytics products . You’ll turn product ideas into working prototypes on real data, find out quickly which ones are worth building, and then take the successful ones all the way to production together with our engineering teams.

Our tech stack

Python, FastAPI, LangGraph, OpenCV, PostgreSQL, S3, Kubernetes, TypeScript, React

What we’d be particularly excited about

- Interest in or experience with mobile games, LiveOps or market analytics.
- Hands on experience in building AI projects
- Experience with LangGraph, LlamaIndex or similar frameworks, and an opinion on when they’re worth using.

Responsibilities

Build AI prototypes for game analytics, with a focus on multimodal data: screenshots, images and gameplay video. For example, recognizing LiveOps events from in-game screenshots or extracting what changed in a game update.; Choose and use the right models for each task (mainly Claude), balancing quality, speed and cost.; Build data pipelines with our Data Engineer to run models on large volumes of game data.; Measure quality so we know what works and what doesn’t.; Build retrieval and multi-step pipelines.; Turn the best prototypes into real product features together with our engineers.

Requirements

Strong Python (TypeScript is a plus).; Experience with multimodal models: screenshots, images or video.; Hands-on experience building products on top of LLM APIs, not just chatbots.; Understanding of RAG and vector search, and when they’re not needed.; You measure results and can show how well your solution works.; You can work independently and explain your decisions clearly.; Ability to pass technical interview both in Russian & English.