ML Ops Engineer
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<p><strong>WHO WE ARE </strong></p>
<p>Zeta Global (NYSE: ZETA) is the AI-Powered Marketing Cloud that leverages advanced artificial intelligence (AI) and trillions of consumer signals to make it easier for marketers to acquire, grow, and retain customers more efficiently. Through the Zeta Marketing Platform (ZMP), our vision is to make sophisticated marketing simple by unifying identity, intelligence, and omnichannel activation into a single platform – powered by one of the industry’s largest proprietary databases and AI. Our enterprise customers across multiple verticals are empowered to personalize experiences with consumers at an individual level across every channel, delivering better results for marketing programs. Zeta was founded in 2007 by David A. Steinberg and John Sculley and is headquartered in New York City with offices around the world. To learn more, go to <a href="https://www.zetaglobal.com">www.zetaglobal.com</a>.</p>
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<p><strong>The Role</strong></p>
<p>We’re looking for a skilled <strong>ML Engineer / Data Scientist</strong> with <strong>3+ years of software or applied ML experience</strong> to design, build, and improve machine learning solutions in a dynamic cloud environment, primarily on <strong>AWS</strong>.This role sits at the intersection of <strong>data science and engineering</strong>: exploring data, developing models, running rigorous experiments, and bringing the best approaches into production with a reliable, reproducible workflow. If strong Python skills, curiosity about hard modeling problems, and collaborative work in multicultural teams are a fit, this is a chance to do meaningful, end-to-end ML work—not just notebooks, and not just infrastructure.</p>
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<p><strong>Who you are:</strong></p>
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<li>Strong foundation in <strong>machine learning, statistics</strong> and <strong>experiment design</strong>.</li>
<li>Experience building models for <strong>real business or product problems</strong>, not only academic benchmarks.</li>
<li>Comfortable working with <strong>structured and unstructured data</strong>: feature engineering, dataset construction, labeling quality, leakage checks, and train/validation/test discipline.</li>
<li>Able to compare approaches with clear <strong>metrics</strong>, error analysis, and sound judgment about tradeoffs (accuracy, latency, cost, maintainability).</li>
<li>Interest in <strong>modern ML</strong>, including classical ML, deep learning, and <strong>LLM / GenAI workflows</strong> where relevant (fine-tuning, RAG, evaluation, prompt/versioning).</li>
<li>Proficient in <strong>Python</strong> and able to write <strong>clean, modular, testable code</strong>.</li>
<li>Experience developing and deploying ML solutions in a <strong>cloud environment</strong>, especially AWS.</li>
<li>Comfortable moving from prototype to production: packaging models, building inference paths, monitoring performance, and iterating after launch.</li>
<li>Independent engineer who can own work from <strong>problem framing → experimentation → implementation → rollout</strong>.</li>
<li>Excellent <strong>written and spoken English</strong>.</li>
<li>Enjoy working closely with engineers, product partners, and other data scientists.</li>
<li>Clear communicator who can explain methods, results, and limitations to technical and non-technical audiences.</li>
<li><strong>Master’s degree</strong> in Science or Engineering (Computer Science, Mathematics, Physics, Statistics, or similar), <strong>or equivalent practical experience</strong>.</li>
</ul>
<p><strong>Nice to have:</strong></p>
<ul>
<li>Experience with <strong>scikit-learn, PyTorch, TensorFlow, XGBoost</strong>, or similar modeling stacks.</li>
<li>Familiarity with <strong>ML experiment tracking</strong> and reproducibility (e.g. MLflow, W&B).</li>
<li>Experience with <strong>SQL</strong>, data warehouses/lakes, and pipeline tools such as <strong>Airflow, dbt, or Spark</strong>.</li>
<li>Exposure to <strong>feature stores</strong>, embedding pipelines, or <strong>vector search</strong> for retrieval-based systems.</li>
<li>Experience building <strong>HTTP/gRPC APIs</strong> or lightweight services around model inference.</li>
<li>Working knowledge of <strong>Docker</strong>, basic orchestration, and CI/CD (e.g. <strong>GitLab CI</strong>).</li>
<li>Experience in <strong>agile</strong>, <strong>remote</strong> and <strong>async</strong> team environments.</li>
<li>Publications, patents, Kaggle/competition results, or open-source ML contributions.</li>
</ul>
<p><strong>What you might like about this role:</strong></p>
<ul>
<li><strong>Hands-on modeling work</strong> with room to explore, benchmark, and improve real systems.</li>
<li>Collaboration on <strong>ML patent submissions</strong> and participation in weekly ML / research paper review meetings.</li>
<li>A <strong>multicultural, engineering-focused team</strong> with strong peer support.</li>
<li><strong>High trust and autonomy</strong>—clear goals, freedom in how to reach them.</li>
<li><strong>Internal product impact</strong>: meaningful projects that improve developer and user experience, not endless maintenance tickets.</li>
<li><strong>Short approval cycles</strong> and solid product partnership.</li>
<li>A <strong>healthy meeting policy </strong>and emphasis on protecting focus time.</li>
<li><strong>Flexible hours</strong>, remote/home office options, and a calm, engineers-only office when on-site.</li>
<li><strong>Competitive compensation</strong>, including stock options.</li>
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<p>We’re hiring across <strong>multiple levels</strong>. Title, scope, and compensation depend on experience—from strong applied ML generalists to senior people who can lead modeling direction and mentor others.</p>
<p>We’re especially interested in candidates who are <strong>technically strong, intellectually curious, and motivated by difficult, ambiguous problems</strong> where good data science and solid engineering both matter.</p>
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<p><strong>PEOPLE & CULTURE AT ZETA</strong></p>
<p>Zeta considers applicants for employment without regard to, and does not discriminate on the basis of an individual’s sex, race, color, religion, age, disability, status as a veteran, or national or ethnic origin; nor does Zeta discriminate on the basis of sexual orientation, gender identity or expression. </p>
<p>We’re committed to building a workplace culture of trust and belonging, so everyone feels invited to bring their whole selves to work. We provide a forum for employees to celebrate, support and advocate for one another. Learn more about our commitment to diversity, equity and inclusion here: <a id="menur45rm" class="fui-Link ___1q1shib f2hkw1w f3rmtva f1ewtqcl fyind8e f1k6fduh f1w7gpdv fk6fouc fjoy568 figsok6 f1s184ao f1mk8lai fnbmjn9 f1o700av f13mvf36 f1cmlufx f9n3di6 f1ids18y f1tx3yz7 f1deo86v f1eh06m1 f1iescvh fhgqx19 f1olyrje f1p93eir f1nev41a f1h8hb77 f1lqvz6u f10aw75t fsle3fq f17ae5zn" href="https://zetaglobal.com/blog/a-look-into-zetas-ergs/" target="_blank">https://zetaglobal.com/blog/a-look-into-zetas-ergs/</a> </p>
<p><strong>ZETA IN THE NEWS!</strong></p>
<p><a href="https://zetaglobal.com/press/?cat=press-releases">https://zetaglobal.com/press/?cat=press-releases</a> </p>
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<p><span data-teams="true">#LI-NP1</span></p>
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