ML Engineer (Recommendation Systems / Infrastructure)

About the Company

A stealth AI infrastructure startup is building the monetization layer for the next generation of consumer AI and interactive entertainment platforms. Backed by top-tier investors and strategic partners in gaming and ad tech, the company is operating at the intersection of AI, recommendation systems, and large-scale user engagement.

The team is still very small and highly technical, offering significant ownership and direct impact from day one.

About the Role

The company is hiring its first dedicated ML Engineer to own and scale a real-time recommendation engine responsible for selecting the best ad experience for users across millions of daily interactions.

This is a highly technical, full-stack ML role spanning data infrastructure, model development, ranking systems, and low-latency serving architecture. The engineer will work directly on systems that influence both user engagement and revenue performance.

Key Responsibilities

• Design and deploy real-time recommendation and ranking systems
• Build ML pipelines, feature stores, and training infrastructure
• Develop user and context models using behavioral and engagement signals
• Optimize serving infrastructure for latency, scalability, and cost efficiency
• Own ML systems end-to-end from data architecture to production deployment
• Improve model performance across retrieval, ranking, and reranking workflows

Required Qualifications

• 4+ years of ML engineering experience
• Experience shipping production ML systems at scale
• Strong backend and infrastructure depth, including pipelines and serving systems
• Experience with scalable ML architecture and production reliability
• Strong understanding of latency-sensitive systems and distributed infrastructure
• Builder mentality with strong execution and ownership
• Strong English communication skills
• Based in LATAM with reliable overlap for async collaboration

Nice to Have

• Experience with recommendation systems, ranking, or AdTech platforms
• Background from large-scale consumer or marketplace companies
• Experience with PyTorch
• Familiarity with AI-native development workflows and tooling

Ideal Candidate Profile

An engineer with strong ML and infrastructure depth who enjoys building production-grade systems in fast-moving startup environments. The ideal candidate is highly execution-oriented, comfortable owning systems end-to-end, and motivated by solving real-time scalability and personalization challenges.