Machine Learning Engineer
Mindroiu Serban-Alexandru PFARomania18 hours ago
Full-timeEngineering, Information Technology
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About The Role
We are building technology to help commercial organisations detect disinformation and respond at the speed of the attackers.
The risk of disinformation is rising rapidly due to geopolitical instability, the low cost of generative AI–driven campaigns, and the difficulty of detecting coordinated, cross-channel attacks. Social media platforms are not helping commercial organisations, and current solutions lack the sophistication, maturity, and focus needed for fast and effective response.
We combine machine learning, proprietary knowledge of attack and defence techniques, and decades of experience in large-scale, high-throughput, real-time data analysis systems. Organisations must respond within minutes — and our platform enables them to do so at scale, across audiences, and directly on the platforms where disinformation is hosted.
Our founders bring over 40 years of combined experience building world-leading data systems used by national security and commercial organisations to combat terrorism, cybercrime, and other large-scale threats — as well as operating cybersecurity and identity platforms that protect billions of people daily.
We are now looking for a talented Machine Learning Engineer to lead ML initiatives from experimentation through to production. This is a hands-on role where you will own ML pipelines end-to-end, from prototyping to deployment.
Key Responsibilities
We are building technology to help commercial organisations detect disinformation and respond at the speed of the attackers.
The risk of disinformation is rising rapidly due to geopolitical instability, the low cost of generative AI–driven campaigns, and the difficulty of detecting coordinated, cross-channel attacks. Social media platforms are not helping commercial organisations, and current solutions lack the sophistication, maturity, and focus needed for fast and effective response.
We combine machine learning, proprietary knowledge of attack and defence techniques, and decades of experience in large-scale, high-throughput, real-time data analysis systems. Organisations must respond within minutes — and our platform enables them to do so at scale, across audiences, and directly on the platforms where disinformation is hosted.
Our founders bring over 40 years of combined experience building world-leading data systems used by national security and commercial organisations to combat terrorism, cybercrime, and other large-scale threats — as well as operating cybersecurity and identity platforms that protect billions of people daily.
We are now looking for a talented Machine Learning Engineer to lead ML initiatives from experimentation through to production. This is a hands-on role where you will own ML pipelines end-to-end, from prototyping to deployment.
Key Responsibilities
- Design & Build: Implement scalable ML pipelines from research to production deployment.
- Collaborate: Work closely with data scientists, engineers, and product teams to operationalize ML models.
- Optimize: Improve data workflows, feature engineering processes, and model performance.
- Innovate: Continuously evaluate and adopt new tools, frameworks, and technologies to improve ML operations.
- Ensure Quality: Maintain reproducibility, scalability, and long-term maintainability of ML systems.
- Proven experience taking ML models from research/prototype to production.
- Strong programming skills in Python (bonus: Go, Java, or other languages).
- Hands-on experience with ML frameworks (TensorFlow, PyTorch, or similar).
- Solid understanding of data pipelines, ETL processes, and cloud-based ML infrastructure.
- Experience with end-to-end ML lifecycle management in real-world environments.
- Problem-solving mindset and ability to work independently.
- Experience with containerization (Docker, Kubernetes) and MLOps tools.
- Background in scalable distributed systems or large-scale data processing.
- Experience in NLP, recommendation systems, or computer vision.
- Equity: 0.25% ownership in the company
- End-to-End Ownership: Opportunity to lead the ML lifecycle from experimentation to production
- Impact: Help solve one of the fastest-growing risks in the digital world
- Collaborative Culture: Work with a highly experienced technical team building cutting-edge solutions
Key Skills
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