Senior Machine Learning Engineer
On-site · London, England, United Kingdom or Chicago, Illinois, United States
Job Summary
Senior Machine Learning Engineer role leading the design and production deployment of multimodal ML models (LLMs, VLMs) to quantify creative quality and predict ad performance. Responsibilities include standing up end-to-end MLOps pipelines (training, fine-tuning, deployment, monitoring) on platforms like MLflow/Kubeflow/Ray Train, scaling distributed training and inference, optimizing for cost and latency on cloud infrastructure, and exposing Finetouch APIs and model endpoints with documented SLAs. Required skills include expert Python and PyTorch (or TensorFlow), experience with distributed training, MLOps tooling, cloud deployment (AWS SageMaker, GCP Vertex AI, Azure ML), Docker/Kubernetes, CI/CD, and strong data pipeline/feature store design. Preferred: vector databases, embedding pipelines, real-time retrieval, and familiarity with creative scoring or ad performance prediction. Core behaviors emphasize production-grade decision making, systems thinking at scale, cross-functional translation, and operational ownership.
Required 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)
- Distributed training frameworks (Ray, PyTorch Lightning, Horovod)
- MLOps tooling (MLflow, Weights & Biases, Kubeflow, Argo, Airflow)
- Cloud-native ML deployment on AWS Sagemaker, GCP Vertex AI, or Azure ML
- Infrastructure-as-code (Terraform, Helm)
- Docker, Kubernetes, and CI/CD for ML
- SQL, data pipelines, feature stores
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