Our partner is looking for a Senior ML Engineer - Malware Detection based remotely in Canada.

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. Machine Learning Model Development:

Design, train, fine-tune, and deploy ML models using production malware data to improve detection coverage and identify threats that traditional signatures and rules may miss.

Develop models that distinguish malicious files from benign content, prioritize suspicious files for deeper inspection, and route samples to the most appropriate detection engines.

Build systems that generate detection signatures from emerging malware campaigns, helping security analysts respond faster to new and evolving threats.

Develop methods to identify malicious content that requires contextual analysis, including phishing pages, webshells, script injections, and compromised website components.

Improve detection precision and cleanup effectiveness so that infected websites remain clean after remediation and recurring threats can be identified.

Establish rigorous evaluation frameworks using time-based and campaign-based dataset splits, historical false-positive and false-negative cases, and regression testing to prevent data leakage and verify improvements before deployment.

Optimize model throughput, latency, CPU usage, memory consumption, and cost per file to support reliable deployment across large hosting fleets.

Production Deployment and Monitoring:

Move models from experimentation into production, using shadow deployments and controlled releases to gather evidence before enabling live detection.

Incorporate fresh analyst feedback and newly identified false negatives into training datasets, establish regular retraining cycles, and monitor trends in detection rates and model performance.

ML Infrastructure and Experimentation:

Develop reusable training pipelines, maintain dataset tooling, make effective use of GPU resources, and enable multiple parallel research and development efforts.

Work closely with malware analysts, detection engineers, and infrastructure teams to investigate customer-reported detection failures, understand emerging threats, and translate operational findings into model improvements.

Technical Documentation and Knowledge Sharing:

Document architectural decisions, evaluation methods, model limitations,



hardware requirements, and production results. Train and evaluate an initial production-data model during the first month, bring multiple models into production or shadow testing by month three, and help establish ML as a major source of new detections within the first year.

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

At least five years of experience building and deploying machine learning systems in production, including at least two years in an adversarial domain such as cybersecurity, fraud, spam, or abuse prevention.

Practical experience training and fine‑tuning transformer models, including data preparation, code tokenization, LoRA/QLoRA or full fine‑tuning, distillation, and quantization. Classical Machine Learning:

Strong understanding of gradient boosting, linear models with sparse features, probability calibration, class imbalance, and the ability to determine when traditional ML methods outperform LLM-based approaches.

Experience designing leakage‑free splits by time and malware family, evaluating precision under fixed false‑positive constraints, maintaining regression datasets, and understanding the limitations of offline evaluation when predicting production performance.

Technical Stack:

Strong Python, PyTorch, and Hugging Face skills, along with SQL expertise for analyzing large datasets using ClickHouse or comparable analytical databases.

Web Technology Familiarity:

Experience with PHP or JavaScript, or a willingness to learn these technologies while collaborating with malware analysts.

AI‑Assisted Engineering:

Daily use of AI coding agents, with the ability to demonstrate how they support development, investigation, and engineering productivity.





English proficiency at B2 level or higher, with particularly strong written communication skills for technical documentation and asynchronous collaboration.

Ability to investigate complex detection problems, interpret model results, make sound technical trade‑offs, and prioritize measurable improvements over experimental metrics alone.

Comfort taking responsibility from experimentation through production deployment while working effectively with analysts, engineers, and other stakeholders.

Web Malware Security:

Experience with web malware, webshells, phishing kits, or WordPress security.

Code‑Based Machine Learning:

Experience developing models over source code using abstract syntax trees (ASTs), token features, code‑focused language models, or graph models of program structure.

Malware Analysis:

Knowledge of malware clustering, similarity analysis, and methods for identifying related samples or emerging campaigns.

Training Infrastructure:

Experience with multi‑GPU training on bare‑metal infrastructure, MLflow or Weights & Biases, and dataset versioning.

Systems Programming:

Published work, technical presentations, or conference talks focused on machine learning for cybersecurity.

Fully Remote Work:

Work remotely with flexibility to organize your schedule and work from different locations worldwide, subject to hiring arrangements.

Flexible Working Hours:

Autonomy to structure your working day in a remote‑first, asynchronous environment.

Paid Vacation:

24 days of paid vacation annually.

Public Holidays:

10 days of national holidays.

Financial compensation toward private medical insurance.

Dedicated Technical Resources:

Challenging Technical Work:

Opportunities to work with large‑scale real‑world malware datasets and develop production ML systems that address evolving cybersecurity threats.

AI‑Enabled Engineering Environment:

Access to AI coding agents and integrated development tools to support experimentation, implementation, and collaboration.

AI‑powered matching process

Data Privacy Notice:

By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre‑contractual measures under applicable data protection laws (including GDPR).

📌 Senior Remote Machine Learning Engineer (Canada)
🏢 Jobgether
📍 Canada