Senior Machine Learning Engineer

What you'll do

• Lead design and production deployment of multimodal ML models for creative scoring system Finetouch
• Deliver improved creative scoring models with better predictive accuracy and expanded multimodal signal coverage
• Establish end-to-end MLOps pipelines for training, fine-tuning, deployment, and monitoring using MLflow, Kubeflow, Ray Train
• Scale distributed training and inference to reduce training time and inference cost using Ray, PyTorch Distributed, and cloud infrastructure
• Build and operate APIs, embedding services, and model endpoints for real-time scoring consumption with SLAs
• Deploy real-time monitoring, drift detection, and alerting for model reliability with runbooks and on-call ownership

Requirements

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

Tech stack