Staff Applied Machine Learning Scientist

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October 28

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Greenhouse Software

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SaaS • HR Tech • Enterprise

Greenhouse Software is a leading provider of hiring software solutions designed for people-first companies. The company offers a comprehensive recruitment and onboarding platform that enhances every aspect of the hiring process. Greenhouse Software focuses on structured hiring and diversity, equality, and inclusion, helping organizations improve their recruitment outcomes and create better experiences for candidates. Their tools are aimed at optimizing the efficiency and effectiveness of hiring teams, reducing biases, and leveraging automation to accelerate talent acquisition. Greenhouse Software partners with other technology providers to offer integrations and additional functionalities, making it a versatile choice for both small and large enterprises worldwide.

501 - 1000 employees

Founded 2012

☁️ SaaS

👥 HR Tech

🏢 Enterprise

📋 Description

• 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. • 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.).

🎯 Requirements

• 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.

🏖️ Benefits

• 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!