Machine Learning Engineer

About the company

Founded by operators and technologists with deep roots in ad platforms and e-commerce, this company is tackling a massive market by bringing advanced machine learning and autonomous agents to the performance marketing space. The team is 20 people, half engineers, and scaling fast. Backed by a tier-1 VC with a mega seed round in the top 1% for their stage.

What you will build

You will be one of the first ML hires, shaping the core data and intelligence layer that powers autonomous marketing agents. The work spans the full ML stack across focused R&D sprints.

* Design and build the core agentic platform — the engine that creates, manages and continuously improves autonomous agents

* Architect the foundational data and signal platform using a modern lakehouse architecture with robust pipelines and ML serving systems

* Collaborate with data scientists to build MLOps infrastructure for training, fine-tuning and deploying state-of-the-art reasoning models

* Build reliable tool systems that allow agents to interact with the world at scale

What we are looking for

Engineers who have built recommendation, ranking or ads systems at scale at a recognisable tech company, and are ready to bring that experience to a fast-moving early-stage team.

* 3 to 8 years of ML engineering experience with strong production-grade Python

* Hands-on experience in ads, search, recommendation or relevance systems at a well-known tech company

* Time at a high-growth, venture-backed startup alongside that big tech experience

* Experience building ML platforms or core modeling and training systems

* Familiarity with LLMs, agentic frameworks or RAG pipelines

* Data engineering depth — ETL, streaming, Spark, Airflow, dbt or lakehouse architecture

* Degree from a top programme in Computer Science or a related quantitative field

Why this role

* Ground floor at a company with real revenue, real customers and tier-1 VC backing

* Advisors who have built some of the most sophisticated ad platforms in the world

* A genuinely high-density team for the stage — every hire has been deliberate

* Real technical ownership, not a ticket queue

* 4 to 6 week R&D sprints — fast feedback, fast shipping

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