hive.co - Senior Software Engineer, Machine Learning

• 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.