Staff Machine Learning Engineer - Applied ML & Research

Benefits

Parental leave

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Non-birth-parent leave

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Family-building benefits

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• 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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Salary

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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

Role function

Engineering From the posting source checked Jun 20, 2026

Seniority

Staff Plus From the posting source checked Jun 20, 2026

Schedule

Shift type

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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

Staff Machine Learning Engineer - Applied ML & Research Netherlands 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 Staff Machine Learning Engineer in the Applied ML & Research team, you'll drive the development of 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. This role blends hands-on technical work with strategic thinking - you'll lead by example, contribute high-quality code, and help shape the ML roadmap through cross-functional collaboration. What the role involves Identify high-impact ML opportunities and influence stakeholders to prioritise and support these initiatives Design and develop scalable machine learning models - including classifiers, regressors, and rule-based systems - to solve real-world problems Own the full ML lifecycle: from data exploration and feature engineering to model training, evaluation, and deployment Translate complex technical concepts into clear insights for both technical and non-technical stakeholders Set and guide technical direction across ML projects, ensuring alignment with technical best practices and business goals Mentor junior engineers and

Read the full description at job-boards.eu.greenhouse.io. FewerJobs shows a preview and links to the original posting.