Senior Software Engineer, Plaid

• Strong software engineering skills including systems design, APIs, and building reliable backend services (Go or Python preferred)
• Production experience with batch and streaming data pipelines and orchestration tools such as Airflow or Spark
• Experience building or operating real-time scoring and online feature-serving systems, including feature stores and low-latency model inference
• Experience integrating model outputs into product flows (APIs, feature flags) and measuring impact through experiments and product metrics
• Experience with model lifecycle and operations: model registries, CI/CD for models, reproducible training, offline & online parity, monitoring and incident response

Desirable

• The Network Enablement team’s mission is to amplify Plaid’s network effects by fostering trust and sharing intelligence with data partners
• We build Trust & Fraud Insights (real-time Protect model scoring, two-way APIs/webhooks, and investigation tooling), Bank Intelligence (ML driven retention and account-primacy metrics and scalable batch pipelines), and the ml/data foundations (graph and sequence-embedding models plus unified feature pipelines and feature-store patterns)
• We own productionization and reliability for data partner facing ML — low-latency scoring, offline↔online parity, observability and drift detection, PII-safe handling and auditability — and collaborated closely with MLE, DS, Data Platform, Fraud, Foundational Modeling, Product, and Privacy to scale network intelligence
• On this team you will build and operate the ML infrastructure and product services that enable trust and intelligence across Plaid’s network
• You’ll own feature engineering, offline training and batch scoring, online feature serving, and real-time inference so model outputs directly power partner-facing fraud & trust products and bank intelligence features
• You will integrate inference into product logic (APIs, feature flags, backend flows), build reproducible pipelines and model CI/CD, and ensure observability, reproducibility, and compliance as you scale our network capabilities
• You’ll partner with Product, ML/Data Platform, Fraud, Foundational Modeling, MLE, DS, and Privacy to ship auditable, reliable ML solutions that move product KPIs
• Embed model inference into Network Enablement product flows and decision logic (APIs, feature flags, backend flows)
• Define and instrument product + ML success metrics (fraud reduction, retention lift, false positives, downstream impact)
• Design and run experiments and rollout plans (backtesting, shadow scoring, A/B tests, feature-flagged releases) to validate product hypotheses
• Build and operate offline training pipelines and production batch scoring for bank intelligence products
• Ship and maintain online feature serving and low-latency model inference endpoints for real-time partner/bank scoring
• Implement model CI/CD, model/version registry, and safe rollout/rollback strategies
• Monitor model/data health: drift/regression detection, model-quality dashboards, alerts, and SLOs targeted to partner product needs
• Ensure offline and online parity, data lineage, and automated validation / data contracts to reduce regressions
• Optimize inference performance and cost for real-time scoring (batching, caching, runtime selection).Ensure fairness, explainability and PII-aware handling for partner-facing ML features; maintain auditability for compliance
• Partner with platform and cross-functional teams to scale the ML/data foundation (graph features, sequence embeddings, unified pipelines)
• Mentor engineers and document team standards for ML productization and operations