Senior Machine Learning Engineer

Role is fully remote but requires candidates to be based in Europe or able to work standard European time zones. No information on visa sponsorship or relocation support provided.

Company overview

Our client is an early-stage AI company building a platform to counter disinformation in real-time. Following strong early traction with their product, they are now accelerating platform development. This Senior ML Engineer role is critical to building and operating the production-grade ML systems at the core of their offering.

Candidate Overview

The ideal candidate is a seasoned ML Engineer, not a researcher, with proven experience building, deploying, and maintaining machine learning systems in a live production environment. They must demonstrate end-to-end ownership of the ML lifecycle. Nonnegotiable technical skills include hands-on experience with CI/CD pipelines, containerization (e.g., Docker), both SQL and NoSQL databases, and large-scale data processing frameworks (streaming and batch). Without a clear track record of shipping and operating production ML systems, do not proceed.

About the role

You’ll be building and operating production-grade machine learning systems that detect and counter disinformation as it happens. This is a high-ownership role where you’ll work across the full ML lifecycle from model development to deployment and monitoring. You’ll collaborate with a cross-functional team spanning engineering, machine learning, and intelligence backgrounds, shaping both the product and the underlying ML infrastructure.

This is not a research-only role the focus is on real-world systems that are reliable, scalable, and fast. TECH STACK Python, SQL & NoSQL databases, streaming & batch data processing frameworks, Docker, CI/CD pipelines, cloud infrastructure.

What we look for

Production ML experience Experience building and deploying machine learning systems in production environments. Strong engineering fundamentals Comfortable writing clean, modular, maintainable code that others can build on. End-to-end ownership Able to manage the full lifecycle — from data and models to deployment, monitoring, and iteration.

Algorithmic versatility Broad exposure across ML approaches and the ability to apply the right method to the problem. Data systems experience Solid understanding of both relational and non-relational databases, as well as large-scale data processing. Systems thinking You understand trade-offs — latency vs accuracy, cost vs performance — and design accordingly. Infrastructure familiarity Hands-on experience with containerisation, CI/ CD, and production environments.

Requirements

- Experience in production engineering

- Algorithmic versatility in ML areas

- Hands-on experience with CI/CD pipelines and containerisation

- Solid grounding in SQL databases and NoSQL stores

- Experience with streaming and batch data processing frameworks

Green Flags

Demonstrated experience deploying and maintaining ML models in a real-time, highthroughput

production setting. Career history showing a transition from software engineering to machine learning, indicating strong engineering fundamentals. Experience at companies known for strong ML engineering practices in areas like trust & safety, security, or real-time content analysis. Articulates clear examples of owning the full ML lifecycle, from data ingestion and model training to deployment, monitoring, and iteration.

Pet projects/ "extra work"

EU-based.

Package

Salary to be discussed DOE

Equity 0.25–0.5% early-stage share options

5 days holiday + your birthday off

Pension Private pension contributions

Remote-first with flexible working and an outcomes-driven culture

Perks Early employee equity and high ownership from day one