WHAT WE DO MATTERS:

At The Knot Worldwide, we champion celebration - and that starts with celebrating our people. Our employees are passionate dreamers, thoughtful doers, and lifelong learners who power meaningful moments for millions around the world. We’re united by authentic connection, shared purpose, and a deep commitment to the global community we serve. Here, flexibility and belonging go hand in hand with high performance. Driven by our core values, we believe the best ideas come from empowered teams: those who consistently collaborate with intention to design solutions, spark ideas, and drive impact. Our people are at the heart of our success.

ABOUT THE ROLE AND OUR TEAM:

The machine learning science team at The Knot Worldwide is at the forefront of building and implementing algorithms that optimize end-to-end customer journey. We focus on building scalable, reusable ML capabilities that maximize engagement, relevance, and business value across key touchpoints like Search, Personalization, and beyond.

As Director of Machine Learning Science, you will lead high-impact teams of applied scientists, setting the vision and strategy for ML across the organization. You’ll drive innovation in search, personalization and real-time optimization, working closely with product and engineering to translate customer needs into ML-driven solutions. You’ll also play a key role in shaping the broader scientific community, serving as a trusted leader and expert across the business.

RESPONSIBILITIES:

Lead and mentor a high-performing Machine Learning Science team focused on delivering impactful ML/AI solutions that drive business outcomes.
Partner closely with cross-functional engineering and product teams to define, prioritize, and execute high-impact initiatives.
Drive research and development of machine learning models, including semantic search, personalized and real-time recommendations, and agentic learning approaches.
Define and execute a mid- and long-term vision for scalable ML architecture across key domains such as personalization, search, and real-time performance optimization.
Recruit, develop, and retain a diverse team of top-tier scientific talent, elevating the capabilities and influence of The Knot’s Machine Learning Science organization.
Actively contribute to the broader technical community by sharing insights through internal knowledge-sharing sessions, technical documentation, and external publications or blog posts.

SUCCESSFUL CANDIDATES HAVE:

M.S. or Ph.D. (Ph.D. strongly preferred) in Computer Science, Machine Learning, Statistics, or a related quantitative field.
7–10 years of hands-on experience in areas such as personalization, ranking systems, search technologies, natural language processing (NLP), deep learning, and other core machine learning methodologies.
Deep expertise in semantic search and embedding-based retrieval techniques.
Strong foundation in statistical modeling, experimental design, hypothesis testing, and optimization.
Proficient in Python, with experience developing and deploying ML-powered systems in production environments.
Proven track record of managing science and engineering teams building complex, real-time, distributed AI/ML systems.
Exceptional technical communication skills, with the ability to convey complex concepts clearly and effectively to both technical and non-technical audiences.
Familiarity with cloud-native platforms such as Google Cloud Platform (GCP), AWS, and Vertex AI is a plus.

WORK MODEL:

This role is Together@TKWW-eligible and based near one of our office hubs. You’ll be expected to work in the office two days a week as part of our hybrid work model.

#LI-Hybrid #management-track

At The Knot Worldwide, we believe you are more than a resume and invite you to go for it, take the leap of faith, and apply for this job. Together, we have an incredible opportunity to make it even easier for our customers to plan life’s most meaningful moments and for our small business owners to grow and scale. We would love to have you with us on our journey.

WHAT WE LOVE ABOUT YOU:

Commit to our customers: You act as one team on behalf of our customers. You lead with head and heart, and build what matters for life's most meaningful moments.
Raise the bar: You define "great" and work backwards. You don't just accept how it's been done, but boldly define how it should be. You are unafraid to innovate, learn, and keep moving forward toward our shared vision.
Be all in: You believe in our mission and take ownership of your work. You debate openly to reach the best outcomes, speaking with clarity and care, embracing diverse perspectives, then commit fully.
Celebrate impact: You measure success by the outcomes you create. You hold yourself accountable to delivering value, while recognizing progress and the lessons learned along the way. You love to win, together.

WHAT YOU LOVE ABOUT US:

We believe in a wide range of holistic offerings to support our employees so that they can live our values day in and day out. From mental wellbeing, physical health and financial planning, to engaging perks and discounts, we are in the business of celebrating and supporting the Moments that Matter, both in and out of the office.. We offer flexible vacation, generous parental leave, and prioritize initiatives that support the growth, development, and happiness of our people.

Together@TKWW is our approach to hybrid work. It’s designed to support how we work best: combining the flexibility we value with meaningful opportunities to connect in person. Whether a role is Together@TKWW-eligible or not, we believe in purposeful moments to come together, build strong relationships, and drive bold ideas forward.

For Together@TKWW-eligible roles, this includes regular in-office time to foster collaboration and connection.
For roles not eligible for in-office expectations, we support connection through virtual collaboration and intentional gatherings.

To facilitate in-person collaboration, we have office spaces in Barcelona, Spain; Delhi, India; Galway, Ireland; London, England; New York, NY; and Washington, D.C.

US Notice: The Knot Worldwide provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, sex, national origin, age, or disability. In addition to federal law requirements, The Knot Worldwide complies with applicable state and local laws governing nondiscrimination in employment in every location in which the company has facilities. The Knot Worldwide expressly prohibits any form of workplace harassment based on race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, or veteran status.

Privacy Notice: The Knot Worldwide (TKWW) processes your personal data to evaluate your application, based on the legal ground of taking steps prior to entering into a potential employment contract. Only the data strictly necessary for this purpose is collected. In some cases, your data may also be processed based on TKWW’s legitimate interests (e.g., to improve recruitment practices or manage candidate pools), to which you can object at any time. You have the right to access, rectify, or delete your data, and to object to certain uses. To learn more about your rights, please consult our The Knot Worldwide (TKWW) processes your personal data to evaluate your application, based on the legal ground of taking steps prior to entering into a potential employment contract. Only the data strictly necessary for this purpose is collected. In some cases, your data may also be processed based on TKWW’s legitimate interests (e.g., to improve recruitment practices or manage candidate pools), to which you can object at any time. You have the right to access, rectify, or delete your data, and to object to certain uses. To learn more about your rights, please consult our Privacy Policy. If you wish to file a complaint, you may contact the competent data protection authority. If you wish to file a complaint, you may contact the competent data protection authority.

The Knot Worldwide jobs

Director, Machine Learning Science

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