Senior Machine Learning Engineer

The Opportunity

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.

The Daily To-Do

• 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
• 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
• 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
• 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
• Deploy real-time monitoring, drift detection, and alerting so production model degradation is caught before it affects creative decisions
• 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
• Document architecture, decisions, and runbooks so the platform outlives any single contributor

Qualifications

• 5+ years in ML engineering or MLOps, with shipped production systems involving LLMs, VLMs, or multimodal architectures
• Expert in Python and PyTorch (or TensorFlow), plus distributed training frameworks (Ray, PyTorch Lightning, Horovod)
• Hands-on with MLOps tooling: MLflow, Weights & Biases, Kubeflow, Argo, or Airflow for orchestration, experiment tracking, and automated retraining
• Cloud-native ML deployment on AWS (SageMaker), GCP (Vertex AI), or Azure ML, with infrastructure-as-code (Terraform, Helm)
• Production fluency with Docker, Kubernetes, and CI/CD patterns for ML
• Strong SQL, data pipeline, and feature store design for scalable experimentation
• Preferred: experience with vector databases, embedding pipelines, and real-time retrieval systems, plus a background in creative scoring, aesthetic modeling, or ad performance prediction