<div class="datj"><header class="topbar"><div class="topbar-in"><a class="back" href="/"><span class="back-arrow">←</span>All roles</a><nav class="topbar-actions"><a class="link-quiet" href="https://app.dataannotation.tech/users/sign_in">Sign in</a><a class="btn btn-primary" href="https://app.dataannotation.tech/worker_signup?worker_src=ai_jobs&utm_source=webflow_jobby&utm_medium=web&utm_campaign=ai_training_jobs&utm_content=role_topbar_machine-learning-engineer&utm_role=machine-learning-engineer&projects=PROG_SA">Apply now</a></nav></div></header><main class="wrap"><div class="rd-layout"><article><div class="rd-title"><h1>Machine Learning Engineer</h1></div><section class="rd-section"><h2>Overview</h2><p>Models are surprisingly bad at reasoning about themselves: training dynamics, evaluation design, data pipelines, and deployment trade-offs. Plausible-sounding ML advice is often subtly wrong.</p><p>As a Machine Learning Engineer you'll stress-test how models reason about ML systems and write the answers a strong practitioner would give, shaping how the next generation handles your field.</p></section><section class="rd-section"><h2>What you’ll actually do</h2><ul><li><b>Write prompts</b> that probe how models reason about training, evaluation, debugging, and productionizing ML systems.</li><li><b>Review AI output</b> for subtle errors: leaky evaluations, wrong loss formulations, and misdiagnosed training failures.</li><li><b>Write the correct solution</b> when the model falls short, grounded in real practitioner experience.</li></ul></section><section class="rd-section"><h2>Roles this fits</h2><p>Common backgrounds: ML Engineer, MLOps Engineer, Applied Scientist.</p></section><section class="rd-section"><h2>What we look for</h2><ul><li>Hands-on experience training, evaluating, or deploying models professionally or in serious personal work.</li><li>Comfort with the modern ML stack; most tasks assume Python and PyTorch or JAX.</li><li>Clear written English: your explanations are the training signal.</li><li>No degree required. We care about what you can do, not where you learned it.</li></ul></section><section class="rd-section"><h2>How it works</h2><div class="rd-steps"><div class="rd-step"><span class="rd-step-n"></span><h3>Apply</h3></div><div class="rd-step"><span class="rd-step-n"></span><h3>Qualify</h3></div><div class="rd-step"><span class="rd-step-n"></span><h3>Work & get paid</h3></div></div></section><section class="rd-section"><h2>Compensation</h2><p>$75 – $150+/hr depending on task difficulty and specialization. Many contributors add $10k–$100k+ a year; some make it their full-time income.</p></section><section class="rd-section"><h2>About DataAnnotation</h2><p>DataAnnotation is where 100k+ experts train the world’s leading AI models. $150M+ paid to contributors to date, and the average contributor stays 5+ years. Flexible, remote, and always project-available.</p></section></article><aside class="rail"><span class="rail-label">Hourly rate</span><span class="rate rail-rate">$75 – $150+ <small>/ hour</small></span><div class="rail-meta"><div class="rail-row"><span>Location</span><span>Remote</span></div><div class="rail-row"><span>Commitment</span><span>Flexible hours</span></div><div class="rail-row"><span>Type</span><span>Independent contractor</span></div><div class="rail-row"><span>Payouts</span><span>Weekly</span></div></div><a class="btn btn-primary btn-lg rail-btn" href="https://app.dataannotation.tech/worker_signup?worker_src=ai_jobs&utm_source=webflow_jobby&utm_medium=web&utm_campaign=ai_training_jobs&utm_content=role_rail_machine-learning-engineer&utm_role=machine-learning-engineer&projects=PROG_SA">Apply now</a></aside></div><section class="similar"><div class="proof-head"><h2>Similar roles</h2><a class="see-all" href="/">View all roles →</a></div><div class="grid"><a class="card" href="/job-board/data-scientist"><h2>Data Scientist</h2><div class="card-foot"><span class="rate">$75 – $150+ <small>/ hr</small></span><span class="hired">92 hired recently</span></div></a></div></section></main><footer class="footer"><div class="footer-in"><p>© DataAnnotation</p><div class="footer-links"><a href="https://www.dataannotation.tech/">dataannotation.tech</a><a href="https://www.dataannotation.tech/faqs">FAQs</a><a href="https://www.dataannotation.tech/trust-safety">Trust & Safety</a></div></div></footer></div>