ML Engineer - Life Sciences (Early Talent)

<div class="content-intro"><p><strong>About Nebius:</strong></p> <p>Nebius is leading a new era in cloud infrastructure for the global AI economy. We are building a full-stack AI cloud platform that supports developers and enterprises from data and model training through to production deployment, without the cost and complexity of building large in-house AI/ML infrastructure.</p> <p>Built by engineers, for engineers. From large-scale GPU orchestration to inference optimization, we own the hard problems across compute, storage, networking and applied AI.</p> <p>Listed on Nasdaq (NBIS) and headquartered in Amsterdam, we have a global footprint with R&D hubs across Europe, the UK, North America and Israel. Our team of 1,500+ includes hundreds of engineers with deep expertise across hardware, software and AI R&D.</p></div><div id="content" class="highlighter-context page view" data-inline-comments-target="true" data-testid="page-content-only"> <div class="_19itglyw _vchhusvi _r06hglyw _19pkidpf _2hwx1wug _otyr1epz _18u01wug _1bsb1osq"> <div id="main-content" class="wiki-content css-pqj8wn e5xcnr80" data-testid="pageContentRendererTestId" data-vc="pageContentRendererTestId" data-test-appearance="full-page"> <div class="renderer-overrides"> <div class="css-3qfej8"> <div class="ak-renderer-wrapper is-full-page css-pw7jst"> <div class="css-1fyp1kw"> <div class="ak-renderer-document"> <p><strong>Summary:</strong></p> <p><strong>Location</strong>: Amsterdam<br><strong>Duration</strong>: 3-6 months<br><strong>Start date</strong>: June-August 2026 <br><strong>Compensation</strong>: Paid<br><strong>Eligibility</strong>: Current University student (Computer Science or related field), Recent Graduate or Early Career specialist<br><strong>Work authorization</strong>: Permitted to work in the job’s location</p> <p data-renderer-start-pos="28" data-local-id="1f27e5a244f5"><strong>About the role</strong></p> <p data-renderer-start-pos="28" data-local-id="1f27e5a244f5">Biological AI models (protein folding, protein design, and large foundation models) are powerful but heavy and expensive to run. This project focuses on making them faster and more efficient at inference time without significantly hurting biological quality.</p> <p data-renderer-start-pos="288" data-local-id="4b1234ac6e87">You will work on profiling bottlenecks, applying model compression and architectural optimizations, and building efficient inference pipelines. Th