Sr Machine Learning Engineer

About the Company

They're a data and ML consultancy with 10 years in the market, building recommendation systems, data infrastructure, and AI solutions for clients across the US and LATAM — retailers, fintechs, e-commerce companies. Databricks AI Enterprise Partner of the Year. AWS-certified in marketing and adtech. 300+ projects delivered across 200+ engineers.

The Role

This is a dedicated engagement for a specific client: a B2B marketing platform that processes tens of billions of messages per year for thousands of brands, with hundreds of millions in ARR and tier-1 VC backing. They need to build and mature the recommendation system that decides what message reaches what person, at what moment — at a scale of hundreds of millions of events per day.

The team starts at 3 ML Engineers and can grow to 10–15. This is the first time the consultancy is working with this client's AI/ML org. Whoever joins now defines how the whole thing gets built.

This is not a research role. It's not adjacent to recsys. If recommendation systems haven't been a core part of your day-to-day for the last two or three years, this isn't the right fit.

What You'll Do

Own the client's recommendation system end-to-end: design, training, deployment, and monitoring

Build and mature ranking and retrieval models that process behavioral signals — clicks, opens, purchases — at consumer scale

Operate ML systems at hundreds of millions of events per day, with direct impact on client revenue

Make independent technical decisions and engage directly with client engineering leadership — no intermediaries

Required Qualifications

6 to 9 years of experience in software or data engineering roles

4+ years building and operating ML systems in production

2 to 3+ years with recommendation systems as a core area of work — not adjacent exposure

Hands-on experience with ranking and retrieval architectures; able to discuss tradeoffs from real production work

Direct experience operating systems at consumer scale (hundreds of millions of events per day or more)

Strong Python skills and fluency with a modern ML stack: PyTorch or TensorFlow, scikit-learn, feature engineering libraries

SQL fluency for data exploration and validation

Production deployment experience on AWS, GCP, or Azure

Fluent spoken and written English — this role involves direct technical conversation with US client engineering leaders

Demonstrated autonomy in client or stakeholder-facing settings: owning scope, raising blockers, making independent decisions

Preferred Qualifications

Background in marketing technology, e-commerce, ad tech, or messaging platforms

Experience with multi-tenant or multi-brand data structures

Familiarity with feature stores (Feast, Tecton, or in-house) and low-latency online