Work alongside our first-class applications, infrastructure & operations teams to understand current manual processes and business requirements
Architect, design, and implement reusable machine learning frameworks, patterns, and services that integrate into the enterprise automation and observability platforms
Design, train, and deploy machine learning models for anomaly detection, forecasting, predictive analytics, event correlation, pattern recognition, classification, causal analysis, and more in distributed environments
Build near-real-time inference pipelines that generate actionable insights from live telemetry
Analyze incident and historical data to uncover leading indicators and predictive signals
Collaborate with cross-functional teams including network engineers, software developers, machine learning engineers, and operations teams

Requirements

7+ years of software engineering experience, with expertise in automation, machine learning, and AI technologies
Proven hands-on experience building production-grade ML models and inference pipelines
Strong proficiency with modern ML frameworks such as PyTorch, TensorFlow, Scikit-learn, etc.
Proven hands-on experience using software to build frontend, APIs and backend functionality; strong proficiency with Python, JavaScript, TypeScript, Go, or Rust
Strong hands-on experience building and deploying event-driven or streaming data, machine learning models in production
Solid foundation in statistics, data analysis, and applied machine learning techniques
Experience with modern cloud platforms, container orchestration (Kubernetes/Docker), identity/auth frameworks, data and workflow orchestration.
Experience operationalizing models in distributed, production environments
Experience working with large-scale, real-world datasets (noisy, incomplete, non-standardized, and evolving)

Benefits

A bonus and/or long-term incentive units may be provided as part of the compensation package,
Full range of medical, financial, and/or other benefits