ML Ops Engineer
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Job Summary
WHO WE ARE
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 industrys 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 .
The Role
Were looking for a skilledML Engineer / Data Scientistwith3 years of software or applied ML experienceto design build and improve machine learning solutions in a dynamic cloud environment primarily onAWS.This role sits at the intersection ofdata science and engineering: 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 worknot just notebooks and not just infrastructure.
Who you are:
- Strong foundation inmachine learningstatistics andexperiment design.
- Experience building models forreal business or product problems not only academic benchmarks.
- Comfortable working withstructured and unstructured data: feature engineering dataset construction labeling quality leakage checks and train/validation/test discipline.
- Able to compare approaches with clearmetrics error analysis and sound judgment about tradeoffs (accuracy latency cost maintainability).
- Interest inmodern ML including classical ML deep learning andLLM / GenAI workflowswhere relevant (fine-tuning RAG evaluation prompt/versioning).
- Proficient inPythonand able to writeclean modular testablecode.
- Experience developing and deploying ML solutions in acloud environment especiallyAWS.
- Comfortable moving from prototype to production: packaging models building inference paths monitoring performance and iterating after launch.
- Independent engineer who can own work fromproblem framing experimentation implementation rollout.
- Excellentwritten and spoken English.
- Enjoy working closely with engineers product partners and other data scientists.
- Clear communicator who can explain methods results and limitations to technical and non-technical audiences.
- Masters degreein Science or Engineering (Computer Science Mathematics Physics Statistics or similar)or equivalent practical experience.
Nice to have:
- Experience withscikit-learnPyTorchTensorFlowXGBoost or similar modeling stacks.
- Familiarity withML experiment trackingand reproducibility (e.g. MLflow W&B).
- Experience withSQL data warehouses/lakes and pipeline tools such asAirflowdbt orSpark.
- Exposure tofeature stores embedding pipelines orvector searchfor retrieval-based systems.
- Experience buildingHTTP/gRPC APIsor lightweight services around model inference.
- Working knowledge ofDocker basic orchestration and CI/CD (e.g.GitLab CI).
- Experience inagileremote andasyncteam environments.
- Publications patents Kaggle/competition results or open-source ML contributions.
What you might like about this role:
- Hands-on modeling workwith room to explore benchmark and improve real systems.
- Collaboration onML patent submissionsand participation inweekly ML / research paper reviewmeetings.
- Amulticultural engineering-focused teamwith strong peer support.
- High trust and autonomyclear goals freedom in how to reach them.
- Internal product impact: meaningful projects that improve developer and user experience not endless maintenance tickets.
- Short approval cyclesand solid product partnership.
- Ahealthy meeting policyand emphasis on protecting focus time.
- Flexible hours remote/home office options and a calm engineers-only office when on-site.
- Competitive compensation includingstock options.
Were hiring across multiple levels. Title scope and compensation depend on experiencefrom strong applied ML generalists to senior people who can lead modeling direction and mentor others.
Were especially interested in candidates who aretechnically strong intellectually curious and motivated by difficult ambiguous problemswhere good data science and solid engineering both matter.
PEOPLE & CULTURE AT ZETA
Zeta considers applicants for employment without regard to and does not discriminate on the basis of an individuals 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.
Were 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: IN THE NEWS!
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About Company
Zeta Global empowers businesses with cutting-edge data-driven marketing solutions. Harness the potential of AI-driven insights and customer engagement.
