Senior Software Engineer, Machine Learning
Senior engineer on Hive's R&D Data team building the company's cloud-native data platform and ML platform for event marketing — from CDC pipelines and ClickHouse-based storage to feature stores, training/serving infrastructure, and LLM-powered agentic systems. Core stack: Python/Django, ClickHouse, MySQL, MongoDB, ElasticSearch, Redshift, and Airflow or Dagster.
- Compensation
- Not disclosed
- Location
- Canada (Remote)
- Work setup
- Remote
- Seniority
- Senior
- Employment
- Not specified
- Experience
- 8+ years
- Category
- Ml / AI
- Posted
Skills
Must have
- 8+ years of hands-on data engineering experience designing, building, and operating large-scale distributed data and ML systems in production
- 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)
- Experience building 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 distributed systems foundations: partitioning strategies, consistency models, backpressure handling, fault tolerance, capacity planning
- Experience applying LLMs and agentic systems in production data or ML contexts
- Product and commercial orientation with stakeholder communication skills for non-technical audiences
- Comfortable operating independently and making progress in ambiguous, fast-changing environments
- Biased toward action; willing to make decisions with imperfect information and iterate quickly
- Skilled at troubleshooting complex ML systems and building durable solutions when things break
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
Full description
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.
What Data team at Hive looks like
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.
What you’ll get up to
- Build our Data Platform: Design and own a cloud-native big data platform handling audience data for millions of attendees and billions of interactions a year. You're not just building pipelines — you're building the infrastructure that determines the quality of every insight, recommendation, and decision Hive's customers make.
- Build our ML Platform: Design and own the infrastructure that takes models from experiment to production — feature stores, training pipelines, model serving, and monitoring. You switch hats between data engineering and ML engineering, ensuring reliable, low-latency access to the features and infrastructure we need to build and ship models confidently. When a model degrades in production, you're the one who built the observability to catch it before the customer does.
- Own the Full Pipeline — and Its Business Impact: From Change Data Capture through validation, transformation, and denormalization — you drive the stack end to end. But you also understand what breaks for a customer when a pipeline is late, a metric drifts, or a model gets stale data. You connect the technical dots to the business dots.
- Treat Data as a Product: You don't ship pipelines — you ship data products that internal teams and customers depend on like a production API. You define SLAs, obsess over data health, build for discoverability.
- Build and Leverage Agentic Systems: You bring an agentic engineering mindset to everything — both how you work and what you build. You use AI coding agents (e.g. Claude Code) as a force multiplier. And you build LLM-powered pipelines and autonomous agents that enrich, classify, and act on audience data at scale.