Senior Machine Learning Engineer

Know where you belong!

Match Group is a leading provider of dating products across the globe. Our portfolio includes Tinder, Hinge, Match, Meetic, PlentyOfFish, OkCupid, The League, HER, and others, each designed to spark meaningful connections for singles worldwide. Creating a sense of belonging doesn’t stop at our products - it’s the foundation of every team we hire.

When it comes to dating, the connection starts online, but the real magic happens once you meet in real life (IRL). We think the same is true for creating the best platforms, so we work together IRL 3 days/week.

Everyone and Everywhere (E&E) is one of the world's leading online dating groups, comprising a large and prestigious dating portfolio (OkCupid, Match.com, Meetic, PlentyOfFish, Affinity, …)with millions of daily active members. We are based in Vancouver with a strong worldwide collaboration with other brands within the Match Group (NASDAQ: MTCH) such as Tinder and

Hinge.

As a Senior Machine Learning Engineer, you will work as part of our ML/AI Squad, working across the organization on strategic and challenging projects to support E&E companies to achieve their business objectives.

Working alongside Data Scientists, Data Engineers, BI Architects, and Product Managers, you will lead the next generation of ML/AI agent and Agentic-based solutions that will have a direct positive impact on our millions of daily users.

Additionally, you will be solving complex problems that keep us awake at night, such as how to keep winning the war on Fraud, how to improve our matching algorithms and search, and how to get a fundamental understanding of what makes our users happy and successful.

How you’ll make an impact

Explore, design, and build AI prototypes capable of significantly altering our users’ experiences

Work in close collaboration with Product Owner (Product Manager, Stakeholders) and Data Scientists to capture product requirements and design ML/AI based solutions, build and deliver in production ML/AI solutions, test new algorithms, provide feedback on their performance, accuracy, and scalability, and alsoalso suggest alternatives.

Contributing to LLM-based initiatives to support business growth and dating user experience

Write optimized code that is performant, scalable, testable, maintainable, and observable

Design scalable solutions to productionize machine learning models

Develop tools and pipelines for tracking models and experiments

Design and implement data pipelines that ingest vast amounts of real-time data to power models and generate accurate results

Manage ML models/Agent lifecycle, including versioning, training, retraining, deploying, and monitoring

Be responsible for maintaining and operating algorithms, code, and models in production and monitor models in production to make sure that they are functioning and report on any anomalies

Research/Investigate tools and technologies that would enable us to build and release faster

Lay out best practices for the ML engineering functional role

Evangelize a data-driven culture and participate in a highly collaborative environment. We want you to share your expertise!

We may be a Match if

B.S., M.S or PhD in computer science (or a scientific discipline coupled with substantial engineering experience)

Minimum of 5+ years post-graduation experience as a Machine Learning Engineer, Software Engineer with algorithm experience, Data Scientist, or similar.

Minimum of 3+ years of experience building and productionalizing ML pipelines and solutions within a commercial application in collaboration with a product owner and deploying and operating LLM solutions to support business feature development

Minimum of 3+ years of experience supporting Data Scientists in the development and evaluation of ML models and their productionalization

Significant experience in cutting-edge deep learning techniques and associated tools (such as PyTorch/Tensorflow)

Excellent programming skills with a history of deployment to production, and you have a proven track record of scaling and productionizing machine learning solutions and models

Solid understanding of mathematical modeling and statistics: inference, Bayesian methods, graphical models, network theory, likelihood estimation, Monte-Carlo methods, and sampling theory

Understanding of how to optimize machine learning models (parallelization, batching techniques, etc)

Ability to rapidly acquire and adopt new knowledge and techniques but also thinks creatively about problems and is not afraid to go “outside the box”

Proficient with standard SQL and relational databases

Familiarity with Python and associated data science/machine learning packages

Comfortable working independently on large projects (demonstrated via industry or academic experience) or as part of a diverse team of different skills as necessary

Ability to see the “big picture” and how your work relates to E&E’s entire business - and the ability to prioritize your research work accordingly

Experience with GCP or other cloud providers