Senior Machine Learning Engineer
Matchgroup · Vancouver, British Columbia
Skills in this posting
Extracted from the posting text by the instrument — the demand side, read literally.
The posting
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.
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
Excerpt from the original listing. The full, current text lives at the source. Read and apply there →
The PivotHop read
- What a machine learning engineer actually earnsmedian, seniority, by country
- What machine learning engineers do insteadevery measured route out
- Data Scientist → Machine Learning Engineer56% readiness
- All open machine learning engineer rolesthe full board
Where these skills also reach
Adjacent occupations measured from the same postings — readiness is what a machine learning engineer’s profile already covers.
- 60 open research scientist roles63% readiness from machine learning engineer
- 60 open ai engineer roles58% readiness from machine learning engineer
- 60 open solutions architect roles50% readiness from machine learning engineer
- 5 open conversation designer roles49% readiness from machine learning engineer
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