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Senior Machine Learning Platform Engineer
| Company | Match Group |
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| Location | New York, NY, USA |
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| Salary | $204000 – $245000 |
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| Type | Full-Time |
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| Degrees | Bachelor’s |
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| Experience Level | Senior |
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Requirements
- 4+ years of experience, depending on education, as an ML, backend, data, or platform engineer developing and working with large scale, complex systems.
- 2+ years of experience working on a cloud environment such as GCP, AWS, Azure, and with dev-ops tooling such as Kubernetes
- 1+ year of experience leading projects with at least 1 other team member through completion.
- 1+ year of experience for Senior designing and developing online and production grade ML systems.
- A degree in computer science, engineering, or a related field.
Responsibilities
- Own or contribute to technical designs for the feature platform, training platform, serving platform, and underlying operational infrastructure that provides incremental delivery and impact.
- Develop, maintain, and enhance reusable frameworks for AI/ML model development and deployment while establishing and driving best practices in machine learning engineering and MLOps.
- Design, advocate, and implement for availability, scalability, operational excellence, and cost management while delivering incrementally.
- Collaborate closely with ML Engineers, Data Scientists, and Product Managers to understand their needs and identify opportunities to accelerate the AI/ML development and deployment process.
- Mentor and educate ML Engineers and Data Scientists on current and up and coming tools and technologies for ML operations through presentations and documentation.
- Help design and architect an AI platform that adheres to the principles of responsible AI and simplifies privacy compliance.
- Lead build vs buy discussions on technologies that would underpin the feature, training, and serving layers.
- Perform other job-related duties as assigned.
Preferred Qualifications
- Strong programming skills: Proficiency in languages like Python, Go, or Java.
- System design & architecture: Ability to design scalable and efficient ML systems.
- Cloud platform proficiency: The ability to utilize cloud environments such as GCP, AWS, or Azure.
- ML knowledge: A basic understanding of ML algorithms, techniques, and best practices.
- Data engineering knowledge: Skills in handling and managing large datasets including, data cleaning, preprocessing, and storage.
- Collaboration and communication skills: The ability to work effectively in a team and communicate complex ideas clearly with individuals from diverse technical and non-technical backgrounds.
- Strong written communication: The ability to communicate complex ideas and technical knowledge through documentation.
- Software leadership skills: A track record of leading projects through completion with quantifiable and measurable outcomes.
