Machine Learning Engineer - Applied ML & Research
Super - Croatia
Posted Jun 12, 2026
Benefits
- Parental leave
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- Non-birth-parent leave
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- Family-building benefits
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- Fertility benefits: Not verified
- Adoption assistance: Not verified
- Surrogacy assistance: Not verified
- Mental health support
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- Relocation assistance
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- Childcare support
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- Learning budget
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Market context
- U.S. role benchmark (BLS OEWS)
- $116,543 U.S. median for this role
- Projected growth (BLS Employment Projections)
- +9.8% - Much faster than average
Matched to SOC 15-1252 - Software Engineering aggregate by role bucket.
Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.
Role
Schedule
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- Weekend work
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Application
- Cover letter
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- Assessment
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- Deadline
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Where they hire
State eligibility is not yet verified.
About this role
Machine Learning Engineer - Applied ML & Research Croatia We are on a mission to pioneer the world's next era of play. As we grow across Europe and Latin America, we're building The Playstack - the technology powering the next generation of sports, gaming, and fan experiences. Join us, and help make it the most widely used platform in the world! From operations, to marketing, to product, we are looking for talented people who will shape how millions of customers play, watch, and connect every day. As a Machine Learning Engineer in our Applied ML & Research team at Super, you will drive the development of cutting-edge machine learning solutions that power critical features across our online gaming platforms. Your work will directly impact platform security, user experience, and large-scale data-driven decision-making for hundreds of thousands of users daily. You will lead by example, contribute high-quality code, and help shape the ML roadmap through cross-functional collaboration. What the role involves Partner with product and engineering teams to identify and execute machine learning use cases that deliver measurable impact. Design, build, and iterate on machine learning solutions (e.g., classifiers, regressors, ranking/retrieval, and rule-based components). Contribute across the ML lifecycle: data exploration, feature engineering, training, evaluation, deployment, and monitoring. Implement reliable training and inference pipelines, and help improve reproducibility, testing, and observability. Communicate model behaviour, trade-offs, and results clearly to both technical and non-technical stakeholders. Contribute to team standards: code quality, documentation, experimentation hygiene, and responsible ML practices. What we are looking
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