Senior Machine Learning & AI Engineer

Description

We're looking for a Senior Machine Learning & AI Engineer to join our team and help build the next generation of intelligent security capabilities.

This is a unique opportunity to join a new initiative and build an AI/ML-driven security product capability from the ground up.

You'll play a key role in shaping the technical vision, making core architectural decisions, and taking ownership of the entire development journey – from early research and experimentation to scalable, production-ready solutions.

Working at the intersection of machine learning, AI, and cybersecurity, you'll turn complex security data into intelligent capabilities that identify suspicious behavior, detect emerging threats, and enable smarter security decisions.

If you're excited about building something new, solving complex technical challenges, and seeing your work translate into real product impact, this is an opportunity to make a difference.

What will you do?

• Lead the end-to-end development of ML/AI capabilities, from problem definition and data preparation to model development, deployment, and continuous improvement.
• Design and develop proprietary models and algorithms for behavioral analysis, anomaly detection, threat detection, and security risk scoring.
• Build scalable batch and streaming data pipelines to process and correlate large volumes of security telemetry, runtime events, system logs, and cloud-native activity.
• Explore and apply the right techniques for each challenge, including statistical methods, classical machine learning, deep learning, and LLMs where they deliver measurable value.
• Drive technical and architectural decisions, choosing the right models, frameworks, and infrastructure to build robust and scalable solutions.
• Evaluate and optimize model performance, focusing on detection accuracy, precision, recall, false-positive reduction, and inference efficiency.
• Build and maintain production-grade ML services and APIs, including monitoring, observability, model versioning, and automated deployment.
• Collaborate closely with security researchers to translate real-world attack techniques and research insights into intelligent detection capabilities.
• Continuously improve models based on real-world data, analyst feedback, and evolving security threats.

Why Aqua?

At Aqua, you'll have the opportunity to tackle some of the most challenging problems in cloud-native security.

You'll be part of a new initiative, with the opportunity to build something from the ground up, influence key technical decisions, and shape innovative AI/ML capabilities that will become part of a real-world security product.

Join us and help redefine how organizations detect, understand, and respond to security threats.

Requirements

• 5+ years of hands-on experience developing and deploying AI/ML solutions in production environments.
• Proven experience building and delivering ML solutions in cybersecurity, threat detection, security analytics, behavioral analytics, or risk analysis.
• Strong Python programming skills and experience with ML frameworks such as PyTorch, TensorFlow, scikit-learn, or XGBoost.
• Solid understanding of machine learning fundamentals, statistics, feature engineering, supervised and unsupervised learning, and model evaluation.
• Experience building data pipelines, working with large datasets, SQL, and production inference systems.
• Strong Linux skills and practical Bash scripting experience.
• Understanding of security concepts and system behavior, including processes, networking, identities, containers, and cloud workloads.
• Proven ability to independently take complex technical challenges from concept to production, with strong engineering ownership.
• Excellent problem-solving, communication, and collaboration skills.

Advantages

• Experience with Go (Golang).
• Experience with anomaly detection, behavioral analytics, graph-based ML, event correlation, or sequence modeling.
• Experience working with LLMs, embeddings, retrieval, or fine-tuning small language models.
• Familiarity with Kubernetes, cloud environments, distributed systems, or large-scale streaming architectures.
• Background in runtime security, eBPF, attack-path analysis, vulnerability prioritization, or MITRE ATT&CK.