Icon Ventures is hiring: Senior Machine Learning Engineer, Personalization and R
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 mission 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 Machine Learning Engineer on the Personalization & Recommendations team, you will design, build, and optimize large-scale retrieval, ranking and recommendation systems that directly shape how learners discover and engage with Quizlet.
You’ll bring strong expertise in modern recommender systems — from deep learning–based retrieval and embeddings to multi-task ranking and evaluation — and contribute to the evolution of Quizlet’s personalization capabilities.
Additionally, you will work at the intersection of machine learning, product, and scalable systems, ensuring our recommendations are performant, responsible, and aligned with learner outcomes, privacy, and fairness.
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.
In this role, you will
Design and implement personalization models across candidate retrieval, ranking, and post-ranking layers, leveraging user embeddings, contextual signals and content features
Develop scalable retrieval and serving systems using architectures such as Two-Tower models, deep ranking networks, and ANN-based vector search for real-time personalization
Build and maintain model training, evaluation, and deployment pipelines, ensuring reliability, training–serving consistency, observability, and robust monitoring
Partner with Product and Data Science to translate learner objectives (engagement, retention, mastery) into measurable modeling goals and experiment designs
Advance evaluation methodologies, contributing to offline metric design (e.g., NDCG, CTR, calibration) and supporting rigorous A/B testing to measure learner and business impact
Collaborate with platform and infrastructure teams to optimize distributed training, inference latency, and serving cost in production environments
Stay informed on industry and research trends, evaluating opportunities to meaningfully apply them within Quizlet’s ecosystem.
Mentor junior and mid-level engineers, supporting technical growth, experimentation rigor, and responsible ML practices
Champion collaboration, inclusion, curiosity, and data-driven problem solving, contributing to a healthy and productive team culture
What you bring to the table
5+ years of experience in applied machine learning or ML-heavy software engineering, with a strong focus on personalization, ranking, or recommendation systems
Demonstrated impact improving key metrics such as CTR, retention, or engagement through recommender or search systems in production
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
Deep understanding of retrieval and ranking architectures, such as Two-Tower models, deep cross networks, Transformers, or MMoE, and the ability to apply them to real-world problems
Experience with large-scale embedding models and vector search, including FAISS, ScaNN, or similar systems.
Proficiency in experiment design and evaluation, connecting offline metrics (AUC, NDCG, calibration) with online A/B test outcomes to drive product decisions
Clear, effective communication, collaborating well with product managers, data scientists, engineers, and cross-functional partners
A growth and mentorship mindset, helping elevate team quality in modeling, experimentation, and reliability.
Commitment to responsible and inclusive personalization, ensuring our systems respect learner privacy, fairness, and diverse goals
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 $$183,360 - $$248,000, 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 401k 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!
To All Recruiters and Placement Agencies
At this time, Quizlet does not accept unsolicited agency resumes and/or profiles.
Please do not forward unsolicited agency resumes to our website or to any Quizlet employee. Quizlet will not pay fees to any third-party agency or firm nor will it be responsible for any agency fees associated with unsolicited resumes. All unsolicited resumes received will be considered the property of Quizlet.
#LI-FT
#J-18808-Ljbffr
In Summary: As a Senior Machine Learning Engineer on the Personalization & Recommendations team, you will design, build, and optimize large-scale retrieval, ranking and recommendation systems that directly shape how learners discover and engage with Quizlet . You’ll collaborate closely with product managers, data scientists, platform engineers, and fellow ML engineers .
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 escolares secundarios 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 alumnos a través de múltiples enfoques y diversas herramientas. Impulsamos las recomendaciones y los sistemas de búsqueda en múltiples superficies, desde la alimentación doméstica y los resultados de investigación hasta los modos de estudio adaptativos. Nuestra misión es hacer que Quizlet se sienta a medida única para cada alumno combinando el aprendizaje automático de vanguardia, infraestructura escalable e ideas de ciencia del aprendizaje. Colaborará estrechamente con gerentes de productos, científicos de datos, ingenieros de plataformas y otros ingenieros ML para ofrecer vías de aprendizaje personalizadas que conducen al compromiso, satisfacción y resultados mensurables de aprendizajes. Sobre el papel como Ingeniero Superior de Aprendizaje Automático en el equipo de Personalización & Recomendaciones, diseñará, construirá y optimizará sistemas de recopilación a gran escala, clasificación y recomendaciones de privacidad que moldean directamente cómo descubren y interactúan los estudiantes con Quizlet. Traerá una fuerte experiencia en sistemas modernos Desde retomar tareas basadas en aprendizaje profundo y diseño entramados de producto hasta evaluaciones y evaluación medibles. Para ayudar a fomentar la colaboración en equipo, requerimos que los empleados estén en la oficina un mínimo de tres días por semana: lunes, miércoles y jueves basados en GPU y búsqueda vectorial para personalización distribuida en tiempo real. En este papel, usted diseñará e implementará modelos de personalización a través de las capas de recuperación de candidatos, clasificación y post-ranking, aprovechando las incorporaciones del usuario, señales contextuales y características de contenido Desarrollar sistemas escalables de extracción y servicio utilizando arquitecturas como modelos Two-Tower, redes de clasificación profunda y ANN basadas en búsquedas de vector para personalización en tiempo Real Construir y mantener tuberías de implementación de modelo, garantizar fiabilidad, entrenamiento servando coherencia, observabilidad y robusta monitorización de datos con el socio para traducir habilidades de aprendizaje (aprendizaje, retención, maestría) Los métodos experimentales avanzados de investigación y capacitación se centrarán en solucionar problemas relacionados al desarrollo de equipos de conocimientos básicos, así como desarrollos o capacidades técnicas de trabajo, análisis y evaluación, uso de tecnologías tecnológicas, ingenieros de software, gestión de aplicaciones inteligentes y tecnología, mejora de recursos humanos, etc. 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 $ 183,360 - $ 248,000 , dependiendo del lugar y la experiencia, así como opciones de acciones de la compañía 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% dependientes PPO, Dental, VSP Choice) Plan 401k patrocinado por el empleador junto a empresa Acceso a LinkedIn Learning y otros recursos para apoyar el crecimiento profesional Permisión familiar pagada, FSA, HSA, Beneficios de viajeros y beneficios de bienestar ¡Por qué participar en programas voluntarios elegidos Unirse a Quizlet? 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 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 tener éxito 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. Mujeres, personas de color, miembros de la comunidad LGBTQ+, individuos con discapacidades y veteranos son fuertemente alentados a solicitar. ¡Ven a unirnos! A todas las agencias de reclutamiento y colocación En este momento, Quizlet no requiere solicitudes insolidas y/o solicitud. Por favor, no acepte ningún cargo por ninguna agencia asociada a nuestro sitio web ni cargos forwards.
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 mission 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 Machine Learning Engineer on the Personalization & Recommendations team, you will design, build, and optimize large-scale retrieval, ranking and recommendation systems that directly shape how learners discover and engage with Quizlet.
You’ll bring strong expertise in modern recommender systems — from deep learning–based retrieval and embeddings to multi-task ranking and evaluation — and contribute to the evolution of Quizlet’s personalization capabilities.
Additionally, you will work at the intersection of machine learning, product, and scalable systems, ensuring our recommendations are performant, responsible, and aligned with learner outcomes, privacy, and fairness.
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.
In this role, you will
Design and implement personalization models across candidate retrieval, ranking, and post-ranking layers, leveraging user embeddings, contextual signals and content features
Develop scalable retrieval and serving systems using architectures such as Two-Tower models, deep ranking networks, and ANN-based vector search for real-time personalization
Build and maintain model training, evaluation, and deployment pipelines, ensuring reliability, training–serving consistency, observability, and robust monitoring
Partner with Product and Data Science to translate learner objectives (engagement, retention, mastery) into measurable modeling goals and experiment designs
Advance evaluation methodologies, contributing to offline metric design (e.g., NDCG, CTR, calibration) and supporting rigorous A/B testing to measure learner and business impact
Collaborate with platform and infrastructure teams to optimize distributed training, inference latency, and serving cost in production environments
Stay informed on industry and research trends, evaluating opportunities to meaningfully apply them within Quizlet’s ecosystem.
Mentor junior and mid-level engineers, supporting technical growth, experimentation rigor, and responsible ML practices
Champion collaboration, inclusion, curiosity, and data-driven problem solving, contributing to a healthy and productive team culture
What you bring to the table
5+ years of experience in applied machine learning or ML-heavy software engineering, with a strong focus on personalization, ranking, or recommendation systems
Demonstrated impact improving key metrics such as CTR, retention, or engagement through recommender or search systems in production
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
Deep understanding of retrieval and ranking architectures, such as Two-Tower models, deep cross networks, Transformers, or MMoE, and the ability to apply them to real-world problems
Experience with large-scale embedding models and vector search, including FAISS, ScaNN, or similar systems.
Proficiency in experiment design and evaluation, connecting offline metrics (AUC, NDCG, calibration) with online A/B test outcomes to drive product decisions
Clear, effective communication, collaborating well with product managers, data scientists, engineers, and cross-functional partners
A growth and mentorship mindset, helping elevate team quality in modeling, experimentation, and reliability.
Commitment to responsible and inclusive personalization, ensuring our systems respect learner privacy, fairness, and diverse goals
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 $$183,360 - $$248,000, 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 401k 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!
To All Recruiters and Placement Agencies
At this time, Quizlet does not accept unsolicited agency resumes and/or profiles.
Please do not forward unsolicited agency resumes to our website or to any Quizlet employee. Quizlet will not pay fees to any third-party agency or firm nor will it be responsible for any agency fees associated with unsolicited resumes. All unsolicited resumes received will be considered the property of Quizlet.
#LI-FT
#J-18808-Ljbffr
In Summary: As a Senior Machine Learning Engineer on the Personalization & Recommendations team, you will design, build, and optimize large-scale retrieval, ranking and recommendation systems that directly shape how learners discover and engage with Quizlet . You’ll collaborate closely with product managers, data scientists, platform engineers, and fellow ML engineers .
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 escolares secundarios 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 alumnos a través de múltiples enfoques y diversas herramientas. Impulsamos las recomendaciones y los sistemas de búsqueda en múltiples superficies, desde la alimentación doméstica y los resultados de investigación hasta los modos de estudio adaptativos. Nuestra misión es hacer que Quizlet se sienta a medida única para cada alumno combinando el aprendizaje automático de vanguardia, infraestructura escalable e ideas de ciencia del aprendizaje. Colaborará estrechamente con gerentes de productos, científicos de datos, ingenieros de plataformas y otros ingenieros ML para ofrecer vías de aprendizaje personalizadas que conducen al compromiso, satisfacción y resultados mensurables de aprendizajes. Sobre el papel como Ingeniero Superior de Aprendizaje Automático en el equipo de Personalización & Recomendaciones, diseñará, construirá y optimizará sistemas de recopilación a gran escala, clasificación y recomendaciones de privacidad que moldean directamente cómo descubren y interactúan los estudiantes con Quizlet. Traerá una fuerte experiencia en sistemas modernos Desde retomar tareas basadas en aprendizaje profundo y diseño entramados de producto hasta evaluaciones y evaluación medibles. Para ayudar a fomentar la colaboración en equipo, requerimos que los empleados estén en la oficina un mínimo de tres días por semana: lunes, miércoles y jueves basados en GPU y búsqueda vectorial para personalización distribuida en tiempo real. En este papel, usted diseñará e implementará modelos de personalización a través de las capas de recuperación de candidatos, clasificación y post-ranking, aprovechando las incorporaciones del usuario, señales contextuales y características de contenido Desarrollar sistemas escalables de extracción y servicio utilizando arquitecturas como modelos Two-Tower, redes de clasificación profunda y ANN basadas en búsquedas de vector para personalización en tiempo Real Construir y mantener tuberías de implementación de modelo, garantizar fiabilidad, entrenamiento servando coherencia, observabilidad y robusta monitorización de datos con el socio para traducir habilidades de aprendizaje (aprendizaje, retención, maestría) Los métodos experimentales avanzados de investigación y capacitación se centrarán en solucionar problemas relacionados al desarrollo de equipos de conocimientos básicos, así como desarrollos o capacidades técnicas de trabajo, análisis y evaluación, uso de tecnologías tecnológicas, ingenieros de software, gestión de aplicaciones inteligentes y tecnología, mejora de recursos humanos, etc. 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 $ 183,360 - $ 248,000 , dependiendo del lugar y la experiencia, así como opciones de acciones de la compañía 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% dependientes PPO, Dental, VSP Choice) Plan 401k patrocinado por el empleador junto a empresa Acceso a LinkedIn Learning y otros recursos para apoyar el crecimiento profesional Permisión familiar pagada, FSA, HSA, Beneficios de viajeros y beneficios de bienestar ¡Por qué participar en programas voluntarios elegidos Unirse a Quizlet? 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 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 tener éxito 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. Mujeres, personas de color, miembros de la comunidad LGBTQ+, individuos con discapacidades y veteranos son fuertemente alentados a solicitar. ¡Ven a unirnos! A todas las agencias de reclutamiento y colocación En este momento, Quizlet no requiere solicitudes insolidas y/o solicitud. Por favor, no acepte ningún cargo por ninguna agencia asociada a nuestro sitio web ni cargos forwards.