Staff Applied Machine Learning Scientist
MediumMedium’s mission is to deepen understanding of the world and spread ideas that matter. We are building the best place for reading and writing on the internet—a place where today’s smartest writers, thinkers, experts, and storytellers can share big, interesting ideas; a place where ideas are judged on the value they provide to readers, not the fleeting attention they can attract for advertisers.
Role Overview
One of Medium’s core operating principles is “humans first,” but sometimes to do that we need a little help from machine learning—which is why Medium is looking for an experienced Staff Applied ML Scientist to join our team. First and foremost, you’ll own Medium’s recommendation systems, which serves millions of readers every day by parsing through an ocean of posts and curating perfect literary pearls for each of them. Under the hood, it’s a sophisticated and modern ML pipeline, fueled by a two-tower model and separate deep retrieval and ranking stages.
Beyond recommendations, you’ll also develop ML models to protect our readers from the burgeoning world of AI slop and spam, explore and evangelize new applications for machine learning across Medium’s business, and leverage machine learning to ensure that Medium always puts humans first.
Key Responsibilities
- You’ll help us apply machine learning thoughtfully across Medium - starting with recommendations, but extending far beyond them. You’ll look for places where ML can make the reading and writing experience more personal, relevant, and human.
- Experiment with ideas like smarter post discovery that helps writers find their natural audience.
- Explore opportunities for personalization, quality detection, topic modeling, or even AI-assisted editorial curation - all grounded in improving understanding, not chasing engagement for its own sake.
- Work closely with design, product, and engineering partners to translate ambiguous user needs into well-framed ML problems.
- Drive the research. Lead with curiosity and precision. You’ll design and interpret experiments, bring statistical rigor to our experimentation, and keep a critical eye on things like bias and spurious correlation in our thinking.
- Bring organizational leverage. Work across teams to ensure that ML improvements are well-integrated into the product, not off to the side. You’ll regularly influence decision-making through cross-functional collaboration, helping product and engineering leaders spot where ML can create leverage and where it shouldn’t.
- Own and continuously improve our recommendation systems. Evolve our two-tower retrieval and ranking stack, refine our feature set, and push on model quality, latency, and interpretability.
- Find new and innovative ways to use ML techniques to better serve our community of readers and writers. This might mean smarter spam and slop detection, writer quality modeling, or intelligent routing of human moderation. The goal: keep Medium a place where humans thrive, not bots.
- Positively contribute to the broader culture and data ecosystem at Medium. Mentor others, document your work with clarity, and help raise the bar for how we think about, design, and deploy ML systems. Share learnings generously and make the people and systems around you better.
- Attend Medium’s twice-yearly, in-person offsites (hosted in locations around the U.S.).
Skills, Knowledge and Expertise
- You’ve been designing and building software for at least 7 years, with at least 3 years focused on architecting and shipping consumer-facing ML models.
- You have experience integrating ML into end-user products (recommendation, ranking, personalization, moderation). You have a proven track record of developing and deploying ML models that deliver measurable business and user impact, not just theoretical gains.
- You embody the “applied” in applied ML: You enjoy the research, but love seeing models ship, move metrics, and make people’s experiences better.
- You’re fluent in Python and ML libraries such as TensorFlow, HuggingFace Transformers, and scikit-learn. You’re comfortable taking models from notebook to production. It’s a huge added bonus if you have experience with Apache Spark for distributed or large-scale training.
- You’re an excellent collaborator, able to translate between data, product, design, and engineering worlds, helping non-ML partners see what’s possible (and what’s not). You’re excited to be “the voice of ML” in business and product conversations.
- You’re skilled at identifying and evangelizing high-leverage ML opportunities across the organization, from recommendation systems to new personalization or quality signals.
- You have hands-on experience with modern model architectures and techniques e.g., feature interaction modeling, advanced negative sampling and bias correction techniques, and efficient large-scale candidate retrieval.
- (Bonus) You’re curious about content discovery, publishing, or online communities, and have a soft spot for writing, ideas, and helping great work get found.
Nice to Haves
- Experience with modern recommender systems.
- A passion for online writing, publishing, or long-form content.
- Familiarity with Medium, as a reader, writer, or both!
Benefits
In addition to the new skills you'll pick up, here's what else you'll enjoy by working at Medium:
- Working with a fully distributed team: We’re fully remote and have teammates across the U.S. & France.
- Healthcare benefits covered at 100% for employees and 70% for dependents.
- Generous parental leave policy.
- Mental health support through Talkspace.
- Financial wellness support through Northstar.
- Stipends for co-working, professional development, wifi, and a one-time home office bonus.
- Unlimited PTO and standard company holidays.
- A discounted Medium membership!
At Medium, inclusion, diversity, and equity are core to how we work and what we build. We believe a more inclusive team creates a better product—one that’s intuitive, thoughtful, and serves a wide range of users. We strive to foster a supportive, fun, and challenging environment where people from all backgrounds can thrive.
We welcome applicants from all walks of life and strongly encourage those from historically underrepresented groups to apply. Our hiring process is built to ensure fairness and equal opportunity for everyone. Even if your experience doesn’t match every requirement, we’d still love to hear from you—your perspective and passion matter more than checking every box.
Learn more about our mission, operating principles, and culture:
Read about our story here.
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