The Product

You'll be working, as part of a small and fast-moving team, at Beast Industries, home to one of the largest audiences in the world. Our businesses span media, consumer products, and creator platforms, with ML increasingly drives how those products work: what gets surfaced, what gets flagged, and what gets trusted. The heart of this role is product-facing ML, backed by a large set of content data. That means:

- Product-facing ML for our creator platforms: brand safety models, sponsor detection, topic classification, and video analysis, where model outputs feed real product decisions in production and a wrong call has a real cost.
- A customer 360 foundation: unifying signals from across our businesses into a single view of each person who interacts with the brand.
- Entity resolution at real scale: matching and deduplicating identities across systems where data is messy, partial, and constantly growing.

What You'll Do

- Develop, evaluate, and deploy machine learning models across brand safety, sponsor detection, topic classification, and video analysis, along with customer 360 records and entity resolution, owning them from experimentation through production monitoring.
- Build and maintain the data pipelines and feature workflows your models depend on, working with both SQL and NoSQL data at scale.
- Partner with engineers, product, and business teams across the company to turn ambiguous questions into measurable models and clear analyses.
- Coach and level up other engineers, setting the example for how ML work gets scoped, built, evaluated, and shipped.
- Use AI coding tools daily, contributing to a velocity that redefines what a small team can deliver.
- Write clean, reliable, and reproducible code with a strong sense of accountability for what you ship in production.

What We're Looking For

- 5+ years of experience in Data Science or Machine Learning Engineering.
- Proficiency in SQL and Python, with hands-on experience with NoSQL databases and Databricks or Snowflake.
- A track record of taking machine learning models from prototype to production and owning their performance once they're live.
- Set and lead a high bar on the team for model quality: building eval datasets, defining offline and online metrics, running A/B tests and shadow runs, and monitoring models in production.
- Excellent data analysis skills, from exploratory analysis to rigorous evaluation.
- Ability to communicate clearly with both technical and non-technical audiences.

Bonus points for experience with content classification or video understanding, LLM-based systems and evaluation, entity resolution or identity matching, fraud or anomaly detection, recommendation or ranking systems, MLOps tooling, JavaScript/TypeScript familiarity (relevant to our codebases), or prior work at a consumer-scale company.

Location: Remote (Brazil) · **Reporting To:** Director of Engineering · **Compensation:** [**R$399,000 to R$460,500** ] + equity + benefits