Senior Software Engineer, Machine Learning

At Hive, we’re all about creating moments that matter and helping event marketers connect with their biggest fans. Our platform powers marketing for 1,500+ iconic events, festivals, venues, and promoters across North America. We help them grow their customer base and sell out shows using intelligent, automated, and personalized digital marketing tools.

Hive integrates with 25+ platforms (like Ticketmaster and Shopify) to provide rich customer data in real-time, enabling event marketers to engage their audiences with precision and impact.

Hive’s R&D Data team is responsible for how we store and query production data at scale. We aren’t focused on only BI or dashboards — we build the systems that power Hive’s products and make data accessible, reliable, and performant.

As a Senior Data Engineer, you’ll play a vital role in evolving our data platform, which directly determines what our customers can do, how fast our product moves, and how confidently leadership can make bets. You'll own outcomes, not tickets. If a business metric is off and it touches data, that's yours to care about.

• Programming: Python and Django

• Data Stores: Clickhouse, MySQL, MongoDB, ElasticSearch, Redshift

• Orchestration: Airflow or Dagster

• 8+ years of hands-on data engineering experience, with a proven track record of designing, building, and operating large-scale distributed data and ML systems in production — high-throughput event streams, real SLAs, and real consequences when things fail.

• Core ML foundations (supervised/unsupervised, cross-validation, bias–variance, regularization, eval metrics) and common algorithms (regression, tree ensembles, clustering).

• Feature engineering with Python ML tooling (pandas, scikit-learn; familiarity with PyTorch or TensorFlow).

• Production ML pipelines and feature datasets feeding model training and inference.

• MLOps practices: experiment tracking, model versioning/registry, deployment, and monitoring for drift/data quality.

• Strong foundations in distributed systems principles — partitioning strategies, consistency models, backpressure handling, fault tolerance, and capacity planning at 10x the volume you designed for.

• Experience applying LLMs and agentic systems in production data or ML contexts — whether enriching pipelines, automating classification, or building autonomous workflow components

• A product and commercial orientation — you consistently frame technical decisions in terms of customer impact and business outcomes, and you have the stakeholder communication skills to make that case to non-technical audiences.

• Comfortable operating independently and making progress in ambiguous, fast-changing environments

• Biased toward action. You’re willing to make decisions with imperfect information and iterate quickly, communicating with other teams inside product and engineering

• Skilled at troubleshooting complex ML systems and building durable solutions when things break

• Excited to shape the future of Hive’s data/ML infrastructure and team in a high-growth, fast-paced company

• 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.