Sr. Engineer, Machine Learning/Artificial Intelligence

Sourced from careers4graduates.com

STARZ is hiring a Senior Engineer, AI/ML to mine large, complex technology datasets for the signals that matter. You will work across STARZ's Snowflake data warehouse and streaming telemetry, applying ML, statistical, and generative AI techniques to surface anomalies, degradation patterns, and operational insights. The role partners closely with Engineering, Customer Care, Product/UX, and executives to embed signal-driven thinking into planning and incident response.

• Explore technology data domains (playback telemetry, customer care, device lifecycle, authentication) to surface hidden signals beyond standard dashboards
• Audit the ecosystem for new data sources and drive their onboarding
• Own a repeatable signals framework defining KPIs, thresholds, and rationale
• Apply ML and statistical techniques to detect emerging issues and risk trends before they appear in operational metrics
• Define platform health indicators and alert thresholds with clear escalation paths
• Apply anomaly detection, classification, clustering, and time-series forecasting models
• Leverage generative AI and LLMs to accelerate insight generation and root-cause analysis
• Implement AI guardrails, grounding, and output validation for trustworthy insights
• Mentor junior analysts and establish a signals review cadence with leadership

• Bachelor's degree in Computer Science, Statistics, Engineering, Mathematics, or related quantitative field
• 5-8+ years of hands-on experience in data science, analytics engineering, or related technical discipline
• Strong SQL and large-scale cloud data warehouse experience (Snowflake preferred)
• Hands-on ML lifecycle experience: feature engineering, data quality, anomaly detection, classification, clustering, time-series forecasting
• Familiarity with generative AI, LLMs, and techniques such as Agents, RAG, and Prompt Engineering
• Ability to distinguish meaningful patterns from statistical noise in operational or telemetry datasets

• Experience in media, streaming, or digital content businesses
• Kafka/Kinesis and pipeline orchestration experience
• AWS (S3, Lambda, CloudWatch) experience
• AI safety and security practices (guardrails, grounding, output validation)
• Statistical methods such as change-point detection and hypothesis testing