Icon Ventures is hiring: Senior Staff Machine Learning Engineer, Personalization
Icon Ventures, San Francisco, CA, United States
About Quizlet:
At Quizlet, our mission is to help every learner achieve their outcomes in the most effective and delightful way. Our $1B+ learning platform serves tens of millions of students every month, including two-thirds of U.S. high schoolers and half of U.S. college students, powering over 2 billion learning interactions monthly.
We blend cognitive science with machine learning to personalize and enhance the learning experience for students, professionals, and lifelong learners alike. We’re energized by the potential to power more learners through multiple approaches and various tools.
Let’s Build the Future of Learning
Join us to design and deliver AI-powered learning tools that scale across the world and unlock human potential.
About the Team:
The Personalization & Recommendations ML Engineering team builds the core intelligence behind how Quizlet matches learners with content, activities, and experiences that best fit their goals. We power recommendation and search systems across multiple surfaces, from home feed and search results to adaptive study modes.
Our team's objective is to make Quizlet feel uniquely tailored for every learner by combining cutting‑edge machine learning, scalable infrastructure, and insights from learning science.
You’ll collaborate closely with Product Managers, Data Scientists, Platform Engineers, and fellow ML engineers to deliver personalized learning pathways that drive engagement, satisfaction, and measurable learning outcomes.
About the Role:
As a senior technical leader on the Personalization & Recommendations team, you’ll not only architect cutting‑edge personalization systems but also guide the strategic direction of Quizlet’s AI‑driven learner experience, mentoring peers and influencing decisions across the company. In this role, you’ll architect and implement large‑scale retrieval, ranking and recommendation systems that directly shape the learner experience. You’ll bring modern RecSys expertise (from deep learning‑based retrieval and embeddings to multi‑task ranking and reinforcement learning) and help evolve Quizlet’s personalization stack.
You’ll help define and deliver systems that learn from billions of interactions while respecting learner privacy, fairness and integrity.
We’re happy to share that this is an onsite position in our San Francisco office. To help foster team collaboration, we require that employees be in the office a minimum of three days per week : Monday, Wednesday, and Thursday and as needed by your manager or the company. We believe that this working environment facilitates increased work efficiency, team partnership, and supports growth as an employee and organization.
In this role, you will:
Work closely with other senior leaders to define and drive the long‑term technical vision for personalization and recommendations across multiple Quizlet surfaces, ensuring alignment between modeling strategy, platform capabilities, and product roadmaps
Communicate complex modeling trade‑offs and recommendations to diverse audiences (from senior leadership to cross‑functional partners) influencing decisions through clear reasoning, data, and empathy
Architect and build large‑scale personalization models across candidate retrieval, ranking, and post‑ranking layers, leveraging user embeddings, contextual signals, and content features to power adaptive learning experiences
Develop scalable retrieval and serving systems using modern architectures such as Two‑Tower, deep ranking, and ANN‑based vector search for real‑time personalization at global scale
Lead model training, evaluation, and deployment pipelines for retrieval and ranking systems, ensuring training‑serving consistency, reliability, and robust monitoring
Partner closely with Product and Data Science to translate learning objectives (e.g., engagement, retention, and mastery) into measurable modeling goals and experimentation frameworks
Advance evaluation methodologies by refining offline metrics (e.g., NDCG, CTR, calibration) and online A/B testing to rigorously measure learner impact and model performance
Collaborate with platform and infrastructure teams to optimize distributed training, inference latency, and cost‑efficient serving in production environments
Stay at the forefront of personalization and RecSys research, bringing relevant advances from top conferences (KDD, WSDM, SIGIR, RecSys, NeurIPS) into applied production systems
Mentor and coach engineers and applied scientists, fostering technical excellence, reproducibility, and responsible AI practices across the organization
Champion a culture of collaboration, inclusivity, and experimentation, helping elevate Quizlet’s AI craft and ensuring personalization systems serve learners equitably and effectively
What you bring to the table:
12+ years of experience in applied machine learning or ML‑heavy engineering, with deep expertise in personalization, ranking, or recommendation systems
Proven ability to shape technical direction across multiple teams or disciplines, balancing long‑term architectural vision with near‑term product and business priorities
Exceptional communication and storytelling skills — able to distill complex technical concepts into clear narratives for executives, product partners, and non‑technical audiences
Demonstrated leadership through influence, guiding teams through ambiguity, aligning stakeholders around measurable goals, and ensuring accountability for impact
Experience mentoring senior engineers and applied scientists, leading technical working groups, and driving cross‑team innovation and standardization
Track record of measurable impact, improving key online metrics such as CTR, retention, and engagement through recommender, ranking, or search systems in production
Deep technical understanding of modern retrieval and ranking architectures (e.g., Two‑Tower, deep cross networks, GNNs, MMoE, Transformers) and multi‑stage RecSys pipelines.
Strong hands‑on skills in Python and PyTorch, with expertise in data and feature engineering, distributed training and inference on GPUs, and familiarity with modern MLOps practices — including model registries, feature stores, monitoring, and drift detection
Experience with large‑scale embedding models and vector search systems (FAISS, ScaNN, or similar), including training, serving, and optimization at scale
Expertise in experimentation and evaluation, connecting offline metrics (AUC, NDCG, calibration) with online A/B results to drive confident, data‑informed decisions
Commitment to collaboration and inclusion, fostering a culture that values diverse perspectives, constructive debate, and shared ownership of results
Bonus points if you have:
Publications or open‑source contributions in RecSys, search, or ranking
Familiarity with reinforcement learning for recommendations or contextual bandits
Experience with hybrid RecSys systems blending collaborative filtering, content understanding, and LLM‑based reasoning
Prior work in consumer or EdTech applications with personalization at scale
Compensation, Benefits & Perks:
Quizlet is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. Salary transparency helps to mitigate unfair hiring practices when it comes to discrimination and pay gaps. Total compensation for this role is market competitive, including a starting base salary of $242,240 - $344,000, depending on location and experience, as well as company stock options
Collaborate with your manager and team to create a healthy work‑life balance
20 vacation days that we expect you to take!
Competitive health, dental, and vision insurance (100% employee and 75% dependent PPO, Dental, VSP Choice)
Employer‑sponsored 401(k) plan with company match
Access to LinkedIn Learning and other resources to support professional growth
Paid Family Leave, FSA, HSA, Commuter benefits, and Wellness benefits
40 hours of annual paid time off to participate in volunteer programs of choice
Why Join Quizlet?
Massive reach: 60M+ users, 1B+ interactions per week
Cutting‑edge tech: Generative AI, adaptive learning, cognitive science
Strong momentum: Top‑tier investors, sustainable business, real traction
Mission‑first: Work that makes a difference in people’s lives
Inclusive culture: Committed to equity, diversity, and belonging
We strive to make everyone feel comfortable and welcome!
We work to create a holistic interview process, where both Quizlet and candidates have an opportunity to view what it would be like to work together, in exploring a mutually beneficial partnership.
We provide a transparent setting that gives a comprehensive view of who we are!
In Closing:
At Quizlet, we’re excited about passionate people joining our team—even if you don’t check every box on the requirements list. We value unique perspectives and believe everyone has something meaningful to contribute. Our culture is all about taking initiative, learning through challenges, and striving for high‑quality work while staying curious and open to new ideas. We believe in honest, respectful communication, thoughtful collaboration, and creating a supportive space where everyone can grow and succeed together.”
Quizlet’s success as an online learning community depends on a strong commitment to diversity, equity, and inclusion.
As an equal opportunity employer and a tech company committed to societal change, we welcome applicants from all backgrounds. Women, people of color, members of the LGBTQ+ community, individuals with disabilities, and veterans are strongly encouraged to apply. Come join us!
#J-18808-Ljbffr
In Summary: The Personalization & Recommendations ML Engineering team builds the core intelligence behind how Quizlet matches learners with content, activities, and experiences that best fit their goals . You’ll help define and deliver systems that learn from billions of interactions while respecting learner privacy, fairness and integrity .
En Español: En Quizlet, nuestra misión es ayudar a cada aprendiz a lograr sus resultados de la manera más efectiva y deliciosa. Nuestra plataforma de aprendizaje $1B+ sirve a decenas de millones de estudiantes todos los meses, incluidos dos tercios de los alumnos de secundaria estadounidenses y la mitad de los estudiantes universitarios de Estados Unidos, impulsando más de 2 mil millones de interacciones de aprendizajes mensuales. Mezclamos ciencia cognitiva con el aprendizaje automático para personalizar y mejorar la experiencia del aprendizaje tanto para estudiantes, profesionales como aprendices de toda la vida. Estamos energizados por el potencial de potenciar a más estudiantes a través de múltiples enfoques y diversas herramientas. Impulsamos las recomendaciones y los sistemas de búsqueda en múltiples superficies, desde el feed doméstico y los resultados de la búsquedas hasta los modos de estudio adaptativos. El objetivo de nuestro equipo es hacer que Quizlet se sienta especialmente diseñado para cada alumno mediante la combinación de aprendizaje automático de vanguardia, infraestructura escalable e información sobre ciencia del aprendizaje. Colaborará estrechamente con gerentes de productos, científicos de datos, ingenieros de plataformas y otros ingenieros ML para ofrecer vías personalizadas que impulsen la participación, satisfacción y resultados mensurables de aprendizajes. Para ayudar a fomentar la colaboración en el equipo, exigimos que los empleados estén en la oficina un mínimo de tres días por semana: lunes, miércoles y jueves y según lo necesite su gerente o la empresa. Creemos que este entorno de trabajo facilita una mayor eficiencia del trabajo, la asociación en equipo y apoya el crecimiento como empleado y organización. En esta función, trabajará estrechamente con otros líderes senior para definir e impulsar la visión técnica a largo plazo de personalización y recomendaciones en múltiples superficies de Quizlet, asegurando la alineación entre estrategia de modelado, capacidades de plataforma y hoja de ruta de productos Comunicar transacciones complejas de modelización y recomiendas a diversos públicos (desde liderazgo superior hasta socios interfuncionales) influyendo en las decisiones mediante razonamientos claros, datos y empatía Arquitecto y construir modelos de personalización a gran escala a través de captación de candidatos, clasificación y post-ranqueo campañas, aprovechando los embebedidos de usuarios, señales contextuales y características de contenido para mejorar la capacidad de aprendizaje desarrollando sistemas de recuperación escalenable y avanzada de conocimiento y desarrollo de procesos de investigación y capacitación tecnológicos, así como métodos de evaluación de rendimiento y formación aplicados a nivel mundial, entrenamiento técnico, análisis de resultados y gestión de proyectos de equipos de inteligencia artificial (R&D & amp;C), mejora de la calidad de sus competencias y su desempeño a nivel de negocio, reconocer los objetivos de la tecnología y la creación de un sistema de estudios más alta calificación y la información (MQUI); Celebramos la diversidad y nos comprometemos a crear un entorno inclusivo para todos los empleados. La compensación total para este papel es competitiva en el mercado, incluido un salario inicial de $242,240 - $344,000, dependiendo de la ubicación y experiencia, así como opciones de acciones de la empresa Colaborar con su gerente y equipo para crear un equilibrio saludable entre vida laboral y personal 20 días de vacaciones que esperamos que tome! Competitivo seguro de salud, odontología y visión (100% empleado y 75% dependentes PPO, Dental, VSP Choice) Plan 401k patrocinado por el empleador junto a una pareja de empresas Acceso a LinkedIn Learning y otros recursos para apoyar el crecimiento profesional Con licencia familiar pagada, FSA, HSA, Beneficios de viajeros y beneficios de bienestar ¡Unirse 40 horas de tiempo libre anual remunerado para participar en programas voluntarios de elección. Valoramos las perspectivas únicas y creemos que todo el mundo tiene algo significativo para contribuir. Nuestra cultura consiste en tomar iniciativa, aprender a través de los desafíos y esforzarse por un trabajo de alta calidad mientras se mantiene curioso y abierto a nuevas ideas. Creemos en una comunicación honesta, respetuosa, colaboración reflexiva y crear un espacio de apoyo donde todos puedan crecer y triunfar juntos. El éxito de Quizlet como comunidad de aprendizaje online depende del fuerte compromiso con la diversidad, equidad e inclusión. Como empleador de igualdad de oportunidades y empresa tecnológica comprometida con el cambio social, damos la bienvenida a solicitantes de todos los orígenes.
At Quizlet, our mission is to help every learner achieve their outcomes in the most effective and delightful way. Our $1B+ learning platform serves tens of millions of students every month, including two-thirds of U.S. high schoolers and half of U.S. college students, powering over 2 billion learning interactions monthly.
We blend cognitive science with machine learning to personalize and enhance the learning experience for students, professionals, and lifelong learners alike. We’re energized by the potential to power more learners through multiple approaches and various tools.
Let’s Build the Future of Learning
Join us to design and deliver AI-powered learning tools that scale across the world and unlock human potential.
About the Team:
The Personalization & Recommendations ML Engineering team builds the core intelligence behind how Quizlet matches learners with content, activities, and experiences that best fit their goals. We power recommendation and search systems across multiple surfaces, from home feed and search results to adaptive study modes.
Our team's objective is to make Quizlet feel uniquely tailored for every learner by combining cutting‑edge machine learning, scalable infrastructure, and insights from learning science.
You’ll collaborate closely with Product Managers, Data Scientists, Platform Engineers, and fellow ML engineers to deliver personalized learning pathways that drive engagement, satisfaction, and measurable learning outcomes.
About the Role:
As a senior technical leader on the Personalization & Recommendations team, you’ll not only architect cutting‑edge personalization systems but also guide the strategic direction of Quizlet’s AI‑driven learner experience, mentoring peers and influencing decisions across the company. In this role, you’ll architect and implement large‑scale retrieval, ranking and recommendation systems that directly shape the learner experience. You’ll bring modern RecSys expertise (from deep learning‑based retrieval and embeddings to multi‑task ranking and reinforcement learning) and help evolve Quizlet’s personalization stack.
You’ll help define and deliver systems that learn from billions of interactions while respecting learner privacy, fairness and integrity.
We’re happy to share that this is an onsite position in our San Francisco office. To help foster team collaboration, we require that employees be in the office a minimum of three days per week : Monday, Wednesday, and Thursday and as needed by your manager or the company. We believe that this working environment facilitates increased work efficiency, team partnership, and supports growth as an employee and organization.
In this role, you will:
Work closely with other senior leaders to define and drive the long‑term technical vision for personalization and recommendations across multiple Quizlet surfaces, ensuring alignment between modeling strategy, platform capabilities, and product roadmaps
Communicate complex modeling trade‑offs and recommendations to diverse audiences (from senior leadership to cross‑functional partners) influencing decisions through clear reasoning, data, and empathy
Architect and build large‑scale personalization models across candidate retrieval, ranking, and post‑ranking layers, leveraging user embeddings, contextual signals, and content features to power adaptive learning experiences
Develop scalable retrieval and serving systems using modern architectures such as Two‑Tower, deep ranking, and ANN‑based vector search for real‑time personalization at global scale
Lead model training, evaluation, and deployment pipelines for retrieval and ranking systems, ensuring training‑serving consistency, reliability, and robust monitoring
Partner closely with Product and Data Science to translate learning objectives (e.g., engagement, retention, and mastery) into measurable modeling goals and experimentation frameworks
Advance evaluation methodologies by refining offline metrics (e.g., NDCG, CTR, calibration) and online A/B testing to rigorously measure learner impact and model performance
Collaborate with platform and infrastructure teams to optimize distributed training, inference latency, and cost‑efficient serving in production environments
Stay at the forefront of personalization and RecSys research, bringing relevant advances from top conferences (KDD, WSDM, SIGIR, RecSys, NeurIPS) into applied production systems
Mentor and coach engineers and applied scientists, fostering technical excellence, reproducibility, and responsible AI practices across the organization
Champion a culture of collaboration, inclusivity, and experimentation, helping elevate Quizlet’s AI craft and ensuring personalization systems serve learners equitably and effectively
What you bring to the table:
12+ years of experience in applied machine learning or ML‑heavy engineering, with deep expertise in personalization, ranking, or recommendation systems
Proven ability to shape technical direction across multiple teams or disciplines, balancing long‑term architectural vision with near‑term product and business priorities
Exceptional communication and storytelling skills — able to distill complex technical concepts into clear narratives for executives, product partners, and non‑technical audiences
Demonstrated leadership through influence, guiding teams through ambiguity, aligning stakeholders around measurable goals, and ensuring accountability for impact
Experience mentoring senior engineers and applied scientists, leading technical working groups, and driving cross‑team innovation and standardization
Track record of measurable impact, improving key online metrics such as CTR, retention, and engagement through recommender, ranking, or search systems in production
Deep technical understanding of modern retrieval and ranking architectures (e.g., Two‑Tower, deep cross networks, GNNs, MMoE, Transformers) and multi‑stage RecSys pipelines.
Strong hands‑on skills in Python and PyTorch, with expertise in data and feature engineering, distributed training and inference on GPUs, and familiarity with modern MLOps practices — including model registries, feature stores, monitoring, and drift detection
Experience with large‑scale embedding models and vector search systems (FAISS, ScaNN, or similar), including training, serving, and optimization at scale
Expertise in experimentation and evaluation, connecting offline metrics (AUC, NDCG, calibration) with online A/B results to drive confident, data‑informed decisions
Commitment to collaboration and inclusion, fostering a culture that values diverse perspectives, constructive debate, and shared ownership of results
Bonus points if you have:
Publications or open‑source contributions in RecSys, search, or ranking
Familiarity with reinforcement learning for recommendations or contextual bandits
Experience with hybrid RecSys systems blending collaborative filtering, content understanding, and LLM‑based reasoning
Prior work in consumer or EdTech applications with personalization at scale
Compensation, Benefits & Perks:
Quizlet is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. Salary transparency helps to mitigate unfair hiring practices when it comes to discrimination and pay gaps. Total compensation for this role is market competitive, including a starting base salary of $242,240 - $344,000, depending on location and experience, as well as company stock options
Collaborate with your manager and team to create a healthy work‑life balance
20 vacation days that we expect you to take!
Competitive health, dental, and vision insurance (100% employee and 75% dependent PPO, Dental, VSP Choice)
Employer‑sponsored 401(k) plan with company match
Access to LinkedIn Learning and other resources to support professional growth
Paid Family Leave, FSA, HSA, Commuter benefits, and Wellness benefits
40 hours of annual paid time off to participate in volunteer programs of choice
Why Join Quizlet?
Massive reach: 60M+ users, 1B+ interactions per week
Cutting‑edge tech: Generative AI, adaptive learning, cognitive science
Strong momentum: Top‑tier investors, sustainable business, real traction
Mission‑first: Work that makes a difference in people’s lives
Inclusive culture: Committed to equity, diversity, and belonging
We strive to make everyone feel comfortable and welcome!
We work to create a holistic interview process, where both Quizlet and candidates have an opportunity to view what it would be like to work together, in exploring a mutually beneficial partnership.
We provide a transparent setting that gives a comprehensive view of who we are!
In Closing:
At Quizlet, we’re excited about passionate people joining our team—even if you don’t check every box on the requirements list. We value unique perspectives and believe everyone has something meaningful to contribute. Our culture is all about taking initiative, learning through challenges, and striving for high‑quality work while staying curious and open to new ideas. We believe in honest, respectful communication, thoughtful collaboration, and creating a supportive space where everyone can grow and succeed together.”
Quizlet’s success as an online learning community depends on a strong commitment to diversity, equity, and inclusion.
As an equal opportunity employer and a tech company committed to societal change, we welcome applicants from all backgrounds. Women, people of color, members of the LGBTQ+ community, individuals with disabilities, and veterans are strongly encouraged to apply. Come join us!
#J-18808-Ljbffr
In Summary: The Personalization & Recommendations ML Engineering team builds the core intelligence behind how Quizlet matches learners with content, activities, and experiences that best fit their goals . You’ll help define and deliver systems that learn from billions of interactions while respecting learner privacy, fairness and integrity .
En Español: En Quizlet, nuestra misión es ayudar a cada aprendiz a lograr sus resultados de la manera más efectiva y deliciosa. Nuestra plataforma de aprendizaje $1B+ sirve a decenas de millones de estudiantes todos los meses, incluidos dos tercios de los alumnos de secundaria estadounidenses y la mitad de los estudiantes universitarios de Estados Unidos, impulsando más de 2 mil millones de interacciones de aprendizajes mensuales. Mezclamos ciencia cognitiva con el aprendizaje automático para personalizar y mejorar la experiencia del aprendizaje tanto para estudiantes, profesionales como aprendices de toda la vida. Estamos energizados por el potencial de potenciar a más estudiantes a través de múltiples enfoques y diversas herramientas. Impulsamos las recomendaciones y los sistemas de búsqueda en múltiples superficies, desde el feed doméstico y los resultados de la búsquedas hasta los modos de estudio adaptativos. El objetivo de nuestro equipo es hacer que Quizlet se sienta especialmente diseñado para cada alumno mediante la combinación de aprendizaje automático de vanguardia, infraestructura escalable e información sobre ciencia del aprendizaje. Colaborará estrechamente con gerentes de productos, científicos de datos, ingenieros de plataformas y otros ingenieros ML para ofrecer vías personalizadas que impulsen la participación, satisfacción y resultados mensurables de aprendizajes. Para ayudar a fomentar la colaboración en el equipo, exigimos que los empleados estén en la oficina un mínimo de tres días por semana: lunes, miércoles y jueves y según lo necesite su gerente o la empresa. Creemos que este entorno de trabajo facilita una mayor eficiencia del trabajo, la asociación en equipo y apoya el crecimiento como empleado y organización. En esta función, trabajará estrechamente con otros líderes senior para definir e impulsar la visión técnica a largo plazo de personalización y recomendaciones en múltiples superficies de Quizlet, asegurando la alineación entre estrategia de modelado, capacidades de plataforma y hoja de ruta de productos Comunicar transacciones complejas de modelización y recomiendas a diversos públicos (desde liderazgo superior hasta socios interfuncionales) influyendo en las decisiones mediante razonamientos claros, datos y empatía Arquitecto y construir modelos de personalización a gran escala a través de captación de candidatos, clasificación y post-ranqueo campañas, aprovechando los embebedidos de usuarios, señales contextuales y características de contenido para mejorar la capacidad de aprendizaje desarrollando sistemas de recuperación escalenable y avanzada de conocimiento y desarrollo de procesos de investigación y capacitación tecnológicos, así como métodos de evaluación de rendimiento y formación aplicados a nivel mundial, entrenamiento técnico, análisis de resultados y gestión de proyectos de equipos de inteligencia artificial (R&D & amp;C), mejora de la calidad de sus competencias y su desempeño a nivel de negocio, reconocer los objetivos de la tecnología y la creación de un sistema de estudios más alta calificación y la información (MQUI); Celebramos la diversidad y nos comprometemos a crear un entorno inclusivo para todos los empleados. La compensación total para este papel es competitiva en el mercado, incluido un salario inicial de $242,240 - $344,000, dependiendo de la ubicación y experiencia, así como opciones de acciones de la empresa Colaborar con su gerente y equipo para crear un equilibrio saludable entre vida laboral y personal 20 días de vacaciones que esperamos que tome! Competitivo seguro de salud, odontología y visión (100% empleado y 75% dependentes PPO, Dental, VSP Choice) Plan 401k patrocinado por el empleador junto a una pareja de empresas Acceso a LinkedIn Learning y otros recursos para apoyar el crecimiento profesional Con licencia familiar pagada, FSA, HSA, Beneficios de viajeros y beneficios de bienestar ¡Unirse 40 horas de tiempo libre anual remunerado para participar en programas voluntarios de elección. Valoramos las perspectivas únicas y creemos que todo el mundo tiene algo significativo para contribuir. Nuestra cultura consiste en tomar iniciativa, aprender a través de los desafíos y esforzarse por un trabajo de alta calidad mientras se mantiene curioso y abierto a nuevas ideas. Creemos en una comunicación honesta, respetuosa, colaboración reflexiva y crear un espacio de apoyo donde todos puedan crecer y triunfar juntos. El éxito de Quizlet como comunidad de aprendizaje online depende del fuerte compromiso con la diversidad, equidad e inclusión. Como empleador de igualdad de oportunidades y empresa tecnológica comprometida con el cambio social, damos la bienvenida a solicitantes de todos los orígenes.