Own complex technical initiatives end-to-end, from technical design through production deployment and operational excellence
Design and develop infrastructure supporting the full cycle of machine learning, including data pipelines and workflow orchestration, data discovery and quality tools, and feature libraries
Drive data and ML-driven solutions for diverse engineering use cases such as recommendation systems, object detection, autogenerated tagging solutions, RAGs
Partner with product, editorial, and engineering stakeholders to translate business requirements into robust technical solutions
Strategically prioritize initiatives and technical workstreams to deliver the highest-impact and most time-sensitive outcomes, while proactively identifying, communicating, and mitigating risks to ensure successful execution
Champion engineering best practices across code quality, testing, CI/CD, observability, and incident response
Mentor and coach engineers, fostering a culture of ownership, collaboration, and continuous improvement
Contribute to technical documentation and promote knowledge sharing across teams
Requirements
Bachelor’s degree in computer science, Information Systems, Statistics, Math, or comparable field of study, and/or equivalent work experience
7+ years of software engineering experience
5+ years of hands‑on experience developing and deploying machine learning systems in production
Expertise in data science, deep learning algorithms, or statistical methods to solve real‑world engineering problems
Comfortable operating at all levels of the predictive stack, including data collection, data analysis, feature engineering, batch training and low‑latency online serving
Experience designing and developing backend microservices for large-scale distributed systems using REST