Senior Machine Learning Engineer
About the role
Own the evolution of Finetouch, Kargo's creative scoring system, by 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.
Responsibilities
- Ship the next generation of Finetouch. Deliver a measurably improved version of the creative scoring model — 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 for Creative Sciences. Establish end-to-end pipelines (training, fine-tuning, deployment, monitoring) on MLflow/Kubeflow/Ray.
- Train so model iterations move from notebook to production in days, not weeks, with full reproducibility.
- Scale distributed training and inference. Reduce training time and inference cost on multimodal/VLM workloads through Ray, PyTorch Distributed, and right-sized cloud infrastructure — enabling larger models and faster experimentation cycles.
- Expose Finetouch as a platform. 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.
- Operationalize model reliability. Deploy real-time monitoring, drift detection, and alerting so production model degradation is caught before it affects creative decisions, with clear runbooks and on-call ownership.
Requirements
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
Background in creative scoring, aesthetic modeling, or ad performance prediction
Skills
- Core Technical Capabilities Required:
- Core Technical Capabilities Preferred:
Competencies
- Research-to-Production Judgment
- Systems Thinking at Scale
- Cross-Functional Translation
- Operational Ownership
Pay
The anticipated base salary range for this position is $150,000—$175,000 USD.
Schedule
Not specified
Benefits
Not specified
Benefits
Not specified
Application Instructions
Not specified
EEO Statement
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, veteran status, or any other characteristic protected by applicable local, state, or federal law. All qualified applicants will receive consideration for employment.