Staff Applied Machine Learning Scientist

📋 Description

• • Lead the design and delivery of state-of-the-art machine learning systems that power Medium’s core recommendation engine, content understanding pipeline, and writer-reader matching algorithms, directly influencing how 170 million monthly readers discover and engage with ideas that matter.
• • Own the end-to-end lifecycle of large-scale ML models—from ideation and offline experimentation to online A/B testing, deployment, and continuous monitoring—ensuring every iteration measurably improves reader satisfaction, reading time, and writer reach.
• • Architect novel deep-learning architectures that combine text, interaction, and social signals to surface the most relevant stories in real time, while proactively mitigating filter bubbles and promoting diverse, high-quality perspectives across the platform.
• • Partner with Product, Engineering, and Editorial teams to translate strategic objectives into concrete ML roadmaps, balancing short-term impact with long-term technical vision for a platform that must scale gracefully as content volume and user base continue to double year-over-year.
• • Establish rigorous experimentation frameworks that allow rapid hypothesis testing across millions of users, leveraging causal inference, uplift modeling, and counterfactual simulation to quantify the true impact of every ranking and personalization change.
• • Drive the creation of reusable feature stores, model-serving infrastructure, and offline training pipelines that reduce iteration time from weeks to hours, enabling data scientists across Medium to ship models safely and autonomously.
• • Mentor and up-level a growing team of applied scientists and ML engineers through architectural reviews, pair programming, and knowledge-sharing sessions, embedding best practices in reproducible research, code quality, and ethical AI governance.
• • Champion fairness, transparency, and explainability in every algorithm we ship, leading audits for bias across gender, race, and geography, and building interpretable model components that give writers clear feedback on how their stories are discovered.
• • Pioneer applied research initiatives that push the boundaries of NLP and recommendation systems, publishing findings at top-tier venues (KDD, WWW, RecSys, NeurIPS) and open-sourcing tooling that benefits the broader scientific community.
• • Collaborate with Medium’s Trust & Safety and Content Moderation teams to develop ML classifiers that detect spam, harassment, and misinformation at web scale, protecting the integrity of conversations that shape public discourse.
• • Influence company-level strategy by translating complex technical insights into compelling narratives for executives, board members, and external stakeholders, ensuring machine learning remains a key differentiator in Medium’s competitive positioning.
• • Contribute to Medium’s culture of intellectual generosity by writing internal technical blogs, hosting reading groups on cutting-edge papers, and participating in external meetups that foster dialogue between industry practitioners and academic researchers.