Senior Software Engineer, Machine Learning
H
HiveEvent marketing
Canada (Remote)Full-TimeSenior
SalaryMeaningful salary and equity
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Job Details
- Experience
- 8+ years of hands-on data engineering experience
- Required Skills
- PythonApache AirflowDjangoElasticSearchMongoDBMySQLClickhousePandas
Requirements
- 8+ years of hands-on data engineering experience.
- Track record designing, building, and operating large-scale distributed data and ML systems in production.
- Experience with high-throughput event streams and production SLAs.
- Knowledge of core ML foundations, including supervised and unsupervised learning, cross-validation, bias–variance, regularization, and evaluation metrics.
- Familiarity with regression, tree ensembles, and clustering algorithms.
- Experience with feature engineering using Python ML tooling, including pandas and scikit-learn; familiarity with PyTorch or TensorFlow.
- Experience building production ML pipelines and feature datasets for model training and inference.
- Knowledge of MLOps practices, including experiment tracking, model versioning or registries, deployment, and monitoring for drift and data quality.
- Strong distributed systems foundations, including partitioning, consistency models, backpressure, fault tolerance, and capacity planning.
- Experience applying LLMs and agentic systems in production data or ML contexts.
- Able to work independently and make progress in ambiguous, fast-changing environments.
Responsibilities
- Design and own a cloud-native big data platform for audience data at scale.
- Build infrastructure for moving models from experimentation to production, including feature stores, training pipelines, model serving, and monitoring.
- Provide reliable, low-latency access to features and infrastructure for building and shipping models.
- Own data workflows end to end, from change data capture through validation, transformation, and denormalization.
- Connect pipeline reliability, metric drift, and data freshness to customer and business impact.
- Define SLAs and build discoverable, reliable data products for internal teams and customers.
- Build LLM-powered pipelines and autonomous agents that enrich, classify, and act on audience data.
- Monitor production models and data systems, troubleshooting issues and building durable solutions.
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