Join Sardine as a Senior Applied AI Research Scientist to design, pretrain, adapt, and deploy foundation models and deep learning solutions on rich behavioral fraud datasets. Responsibilities include scoping experiments, owning evaluation and monitoring, taking models from data prep through production serving, and partnering with engineering, legal/compliance, and client teams. Role is remote within the United States or Canada and requires 4+ years of applied machine learning experience with at least one foundation model put into real traffic.

Senior individual contributor role owning ingestion, feature platforms, model training/serving, and production reliability for fraud and KYC onboarding. Requires designing streaming and batch pipelines (Beam/Flint/Spark), operating BigQuery-based warehouses, productionizing models (Vertex AI/Kubeflow, boosted-tree models), ensuring feature correctness and governance (PII, encryption, residency), and mentoring engineers and data scientists. Remote within United States or Canada; 8+ years experience expected.

Sardine is hiring a Forward Deployed AI Engineer (remote, US or Canada) to embed with clients, translate operational challenges into technical architectures, and architect, train, fine-tune, evaluate, and deploy AI agents and production ML pipelines focused on fraud prevention and AML automation. Requires 5+ years in data science or applied AI and proficiency in Python and SQL.

Sardine seeks a Machine Learning Engineer to own model serving, real-time inference, feature retrieval, and deployment pipelines for fraud detection under a sub-250ms latency budget. The role requires production ownership (monitoring, drift detection, retraining, incident response, on-call), strong Python and solid software engineering practices, experience with Kubernetes/containers and a major cloud (primarily GCP), and working across Python and Go. Remote within the United States or Canada.

Hands-on role to design, prototype, and productionize machine learning and graph-analytics solutions for fraud and AML use cases. Responsibilities include metrics definition and tracking, EDA and cohort analysis, dashboard automation, and close client-facing collaboration. Requires 7+ years in data science or related quantitative roles with strong Python/R and SQL experience and familiarity with BI tools (Tableau, Sigma, Metabase).