Senior Software Engineer, Machine Learning

Role overview Own and evolve the data and machine-learning platform that powers a marketing platform used by event brands, venues, festivals, and promoters across North America. The role spans cloud-native big-data infrastructure, end-to-end ML systems, and the use of LLM-powered agents across pipelines. It is fully remote with flexible hours and is framed around outcomes rather than ticket completion.

Responsibilities - Design and own a cloud-native big-data platform handling audience data for millions of attendees and billions of interactions per year. - Build the ML platform: feature stores, training pipelines, model serving, and monitoring for drift and data quality. - Drive the full pipeline from change data capture through validation, transformation, and denormalization, connecting technical work to customer and business impact. - Ship data as a product with defined SLAs, discoverability, and ongoing health monitoring. - Apply an agentic engineering mindset: use AI coding assistants and build LLM-powered pipelines and autonomous agents that enrich, classify, and act on audience data. - Communicate trade-offs and decisions in terms of customer outcomes and business metrics.

Requirements - 8+ years of hands-on data engineering experience designing, building, and operating large-scale distributed data and ML systems in production with real SLAs. - Core ML foundations including supervised and unsupervised learning, cross-validation, bias-variance, regularization, and common algorithms like regression, tree ensembles, and clustering. - Feature engineering with Python tooling such as pandas and scikit-learn, plus familiarity with PyTorch or TensorFlow. - Production ML pipelines, feature datasets, and MLOps practices including experiment tracking, model versioning, deployment, and monitoring. - Strong distributed systems fundamentals: partitioning, consistency models, backpressure, fault tolerance, and capacity planning for 10x volume. - Experience applying LLMs and agentic systems in production data or ML contexts. - Product and commercial orientation with strong stakeholder communication skills.

Nice to have - History of owning or re-architecting a data platform end to end in a fast-growing environment. - Background in SaaS or event-driven products where data systems directly power user-facing features.

Benefits and work setup - Competitive salary plus equity tied to impact. - Fully remote work with flexible hours, minimal meetings, and no fixed 9-to-5. - Health and dental coverage plus parental leave top-ups on top of EI benefits. - Unlimited vacation or PTO to support work-life balance.