Machine Learning Engineer (Cybersecurity)

TL;DR

Machine Learning Engineer (Cybersecurity): Build and deploy ML models at the edge for real-time anomaly detection, bot mitigation, and zero-day intrusion detection in a payments platform with an accent on high-throughput low-latency inference and proactive defense. Focus on designing data pipelines from attack simulations, monitoring model performance, and automating retraining against evolving threats.

Location: Fully remote within a distributed team (São Paulo mentioned)

Company

Fintech building AI-driven payments and credit infrastructure.

What you will do

• Design and train ML models for anomaly detection, bot mitigation, and zero-day intrusion detection at the edge.
• Deploy and optimize inference models for ultra-low latency on massive HTTP traffic volumes.
• Collaborate with offensive security to simulate threats and generate datasets for training.
• Build data pipelines to ingest logs, monitor performance, detect drift, and automate retraining.

Requirements

• Fluency in Python and SQL; proficiency in PyTorch, TensorFlow, or Scikit-Learn.
• Experience deploying ML models to production with high-throughput, low-latency needs.
• Strong knowledge of web security, HTTP protocols, OWASP Top 10, L7 DDoS, credential stuffing.
• Familiarity with edge platforms (Cloudflare, Fastly, AWS Edge), CDN/WAF.
• Solid software engineering in Python, Rust, TypeScript; cloud infra (GCP/AWS), large datasets.
• Experience with LLMs and Agents.
• Autonomous, collaborative; effective communication in English and Portuguese.

Nice to have

• Cloudflare Workers, WAF, Workers AI experience.
• Cybersecurity background: IDS/IPS, threat hunting, malware analysis.
• Payment security: PCI DSS, tokenization, acquiring.
• Open-source contributions, security research, CTF.

Culture & Benefits

• Fully remote and distributed team environment.
• Highly collaborative and self-driven culture.
• Focus on engineering proactive security systems.