Описание

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

Responsibilities

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

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

Nice to have

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

We offer

Full Time employment

Hiring process

Apply for this position