1h ago

Senior ML Engineer - Malware Detection

Saudi ArabiaRemoteFull-timesenior

Machine Learning EngineerData

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Quick Summary

Key Responsibilities

Machine Learning Model Development: Design, train, fine-tune,

Requirements Summary

Train and evaluate an initial production-data model during the first month, bring multiple models into production or shadow testing by month three,

Technical Tools

Machine Learning EngineerData

The Senior ML Engineer will develop and deploy advanced machine learning solutions to detect web-based malware across large-scale Linux hosting environments. Working with an extensive dataset of real-world malware samples, analyst decisions, detection rules, and cleanup outcomes, you will transform production intelligence into more accurate and efficient security systems. Your work will span classical machine learning, transformer models, LLM-assisted triage, and automated signature generation. You will own the journey from raw data and experimentation to production deployment, with success measured by real detection improvements and controlled false-positive rates. Collaborating closely with malware analysts, infrastructure specialists, and fellow ML engineers, you will have access to dedicated GPU resources and modern AI-assisted development tools. This remote-first opportunity is ideal for an experienced ML practitioner who thrives on experimentation, rigorous evaluation, and delivering measurable cybersecurity impact.

You will lead the development, evaluation, and deployment of machine learning systems that improve malware detection at scale while balancing accuracy, operational costs, and resource constraints. Your key responsibilities will include:

Requirements

~1 min read

The ideal candidate combines substantial production ML experience with hands-on transformer expertise, strong evaluation discipline, and the ability to engineer efficient models for adversarial security environments. You should be comfortable making evidence-based decisions, communicating asynchronously, and taking ownership of the full ML lifecycle. Key qualifications include:

Responsibilities

~2 min read

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Fully Remote Work: Work remotely with flexibility to organize your schedule and work from different locations worldwide, subject to hiring arrangements.
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Flexible Working Hours: Autonomy to structure your working day in a remote-first, asynchronous environment.
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Paid Vacation: 24 days of paid vacation annually.
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Public Holidays: 10 days of national holidays.
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Sick Leave: Unlimited sick leave.
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Healthcare Support: Financial compensation toward private medical insurance.
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Wellness Benefits: Reimbursement for co-working spaces and gym or sports activities.
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Learning and Development: An education budget and a strong focus on professional growth.
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Innovation Rewards: The opportunity to receive a reward for developing an innovative idea that can be patented.
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Dedicated Technical Resources: Access to dedicated GPU resources and potential additional computing investment supported by evidence-based proposals.
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Challenging Technical Work: Opportunities to work with large-scale real-world malware datasets and develop production ML systems that address evolving cybersecurity threats.
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AI-Enabled Engineering Environment: Access to AI coding agents and integrated development tools to support experimentation, implementation, and collaboration.

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