Senior Machine Learning Engineer

About this role

<div class="content-intro"><p><strong>^{Who We Are}</strong><br>_{Kargo creates powerful moments of connection between brands and consumers to build businesses. Every day, our 600+ employees work to radically raise the bar on what agentic AI, CTV, eCommerce, social, and mobile can do to deliver unique ad experiences across the world’s most premium platforms. Taking a creative science approach to all we do, we continuously innovate solutions that outperform industry benchmarks and client expectations. Now 20+ years strong, Kargo has offices in NYC, Chicago, LA, &nbsp;Dallas, Sydney, Auckland, London and Waterford, Ireland.&nbsp;}</p> <p style="text-align: left;"><strong>^{<br>Who We Hire<br>}</strong>_{Techies who want to build the future. Creatives who want to design it better. Communicators to win business. Collaborators to build it. Data pros who turn numbers into insights. Product builders who turn ideas into innovations. Anyone eager to be on a team that doesn’t stop to ask what’s next, because they’re already building it.&nbsp;}</p></div><p><strong>The Opportunity</strong></p> <p>Kargo is hiring a senior machine learning engineer to own the evolution of Finetouch, our creative scoring system—leading the design and production deployment of multimodal ML models that quantify creative quality and predict ad performance. This role is the technical anchor for the Creative Sciences Platform, translating research in LLMs, VLMs, and multimodal learning into scalable, reliable systems that creative and product teams build on. Success means Finetouch becomes faster, smarter, and more trusted as the intelligence layer behind Kargo's creative analytics.</p> <p><strong>The Daily To-Do</strong></p> <ul> <li>Ship the next generation of Finetouch—delivering better predictive accuracy on creative performance, expanded multimodal signal coverage (visual + text + engagement), and validated lift over the current baseline</li> <li>Stand up production-grade MLOps pipelines—training, fine-tuning, deployment, monitoring—on MLflow/Kubeflow/Ray Train so model iterations move from notebook to production in days, not weeks</li> <li>Scale distributed training and inference on multimodal/VLM workloads through Ray, PyTorch Distributed, and right-sized cloud infrastructure—enabling larger models and faster experimentation cycles</li> <li>Build and operate the APIs, embedding services, and model endpoints that let Glossi and other Kargo creative platforms consume scoring in real time, with documented SLAs and integration patterns</li> <li>Deploy real-time monitoring, drift detection, and alerting so production model degradation is caught before it affects creative decisions</li> <li>Explain multimodal modeling tradeoffs to Product and Creative stakeholders in terms of business impact, partnering with Data Science and Platform Engineering as co-owners, not handoff points</li> <li>Document architecture, decisions, and runbooks so the platform outlives any single contributor</li> </ul> <p><strong>Qualifications</strong></p> <ul> <li>5+ years in ML engineering or MLOps, with shipped production systems involving LLMs, VLMs, or multimodal architectures</li> <li>Expert in Python and PyTorch (or TensorFlow), plus distributed training frameworks (Ray, PyTorch Lightning, Horovod)</li> <li>Hands-on with MLOps tooling: MLflow, Weights &amp; Biases, Kubeflow, Argo, or Airflow for orchestration, experiment tracking, and automated retraining</li> <li>Cloud-native ML deployment on AWS (SageMaker), GCP (Vertex AI), or Azure ML, with infrastructure-as-code (Terraform, Helm)</li> <li>Production fluency with Docker, Kubernetes, and CI/CD patterns for ML</li> <li>Strong SQL, data pipeline, and feature store design for scalable experimentation</li> <li>Preferred: experience with vector databases, embedding pipelines, and real-time retrieval systems, plus a background in creative scoring, aesthetic modeling, or ad performance prediction</li> </ul><div class="content-pay-transparency"><div class="pay-input"><div class="description"><p class="font-claude-response-body break-words whitespace-normal leading-[1.7]"><em>In accordance with applicable federal, state, and local pay transparency laws, the anticipated base salary range for this position is listed below. In addition to base salary, this role is eligible for variable compensation — either the Kargo Sales Incentive Plan (sales roles) or an annual discretionary bonus (all other roles). Actual compensation may vary based on factors such as geographic location, work experience, education, and skills.</em></p></div><div class="title">U.S Salary Range </div><div class="pay-range"><span>$150,000</span><span class="divider">—</span><span>$175,000 USD</span></div></div></div><div class="content-conclusion"><p><strong>Our Laurels</strong></p> <ul> <li>AdAge Best Places to Work&nbsp;</li> <li>ThinkLA Partner of the Year</li> <li>Built In Best Places to Work&nbsp;</li> <li>Cynopsis 2025 Top Women in Media - Jeannine Shao Collins</li> <li>Martech Breakthrough Awards - Best Overall Adtech Company</li> <li>Digiday Media Awards Best Event</li> <li>Cynopsis Media Impact Awards-Best CTV Platform</li> <li>Martech Breakthrough Awards-CTV Innovation</li> <li>Adweek Media Plan of the Year Awards - Best Use of Insights</li> </ul> <p><strong>Follow Our Lead</strong></p> <ul> <li>Big Picture:&nbsp; <a href="kargo.com">kargo.com</a></li> <li>The Latest:&nbsp; Instagram (@kargomobile) and LinkedIn (Kargo)</li> </ul> <p class="font-claude-response-body break-words whitespace-normal leading-[1.7]"><em>Kargo is an Equal Opportunity Employer. We are committed to building an inclusive and diverse workplace where all employees and applicants are treated with respect and dignity. We do not discriminate on the basis of race, color, ethnic origin, religion or belief, sex, sexual orientation, gender identity or expression, age, disability, marital or family status, national origin, vetera