dragos
Senior AI/ML Engineer
At a Glance
- Location
- United States
- Work Regime
- remote
- Experience
- 6+ years
- Posted
- 2026-07-20T20:58:51-04:00
Key Requirements
Required Skills
Domain Knowledge
- Cybersecurity
- Engineering
Benefits & Perks
tive Equity Package Comprehensive Benefits Plan #LI-JF1 #LI-REMOTE #LI-NH1 #LI-REM
Requirements
6+ years of engineering experience with at least 4 years focused on machine learning implementations in production environments.
Strong software engineering foundation with expertise in Python and SQL as well as experience with at least one additional language (Go, Rust, Java, or JVM-family languages).
Demonstrated experience building and deploying ML systems using modern frameworks and libraries (scikit-learn, PyTorch, TensorFlow, HuggingFace, or similar).
Experience with LLMs, retrieval-augmented generation (RAG), or advanced NLP techniques is beneficial.
Proven track record implementing ML solutions such as classification systems, time series analysis, anomaly detection, or NLP applications that deliver measurable business impact.
Experience with MLOps practices, including model versioning, monitoring, pipeline orchestration, and deployment in high-reliability environments.
Compensation & Benefits
:
Salary:
$190,000.00
Competitive Equity Package
Comprehensive Benefits Plan
#LI-JF1 #LI-REMOTE
Responsibilities
Design and implement production-grade machine learning systems that expand Dragos product capabilities, with consideration for both cloud and resource-constrained on-premises environments.
Build and optimize ML model architectures for ICS/xOT cybersecurity use cases, including threat detection, asset classification, behavioral analysis, anomaly detection, and natural language processing systems.
Develop robust data pipelines and ML workflows that integrate with existing data infrastructure, supporting both real-time and batch processing requirements.
Collaborate with OT detection experts to translate research concepts and prototypes into scalable, production-ready ML systems.
Partner with Data Engineers to establish data contracts and implement observability frameworks for ML pipelines, including monitoring, versioning, and deployment best practices.
Contribute to ML infrastructure improvements, including automated testing frameworks, CI/CD pipelines, and deployment strategies for containerized environments (Kubernetes, Docker).