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YubHub-native raw fields carry `x-` prefix.","jobs":[{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_586b9fef-509"},"title":"Senior Software Engineer - Network Enablement (Applied ML)","description":"<p>We believe that the way people interact with their finances will drastically improve in the next few years. We&#39;re dedicated to empowering this transformation by building the tools and experiences that thousands of developers use to create their own products.</p>\n<p>On this team, you will build and operate the ML infrastructure and product services that enable trust and intelligence across Plaid&#39;s network. You&#39;ll own feature engineering, offline training and batch scoring, online feature serving, and real-time inference so model outputs directly power partner-facing fraud &amp; trust products and bank intelligence features.</p>\n<p><strong>Responsibilities</strong></p>\n<ul>\n<li>Embed model inference into Network Enablement product flows and decision logic (APIs, feature flags, backend flows).</li>\n<li>Define and instrument product + ML success metrics (fraud reduction, retention lift, false positives, downstream impact).</li>\n<li>Design and run experiments and rollout plans (backtesting, shadow scoring, A/B tests, feature-flagged releases) to validate product hypotheses.</li>\n<li>Build and operate offline training pipelines and production batch scoring for bank intelligence products.</li>\n<li>Ship and maintain online feature serving and low-latency model inference endpoints for real-time partner/bank scoring.</li>\n<li>Implement model CI/CD, model/version registry, and safe rollout/rollback strategies.</li>\n<li>Monitor model/data health: drift/regression detection, model-quality dashboards, alerts, and SLOs targeted to partner product needs.</li>\n<li>Ensure offline and online parity, data lineage, and automated validation / data contracts to reduce regressions.</li>\n<li>Optimize inference performance and cost for real-time scoring (batching, caching, runtime selection).</li>\n<li>Ensure fairness, explainability and PII-aware handling for partner-facing ML features; maintain auditability for compliance.</li>\n<li>Partner with platform and cross-functional teams to scale the ML/data foundation (graph features, sequence embeddings, unified pipelines).</li>\n<li>Mentor engineers and document team standards for ML productization and operations.</li>\n</ul>\n<p><strong>Qualifications</strong></p>\n<ul>\n<li>Must-haves:</li>\n<li>Strong software engineering skills including systems design, APIs, and building reliable backend services (Go or Python preferred).</li>\n<li>Production experience with batch and streaming data pipelines and orchestration tools such as Airflow or Spark.</li>\n<li>Experience building or operating real-time scoring and online feature-serving systems, including feature stores and low-latency model inference.</li>\n<li>Experience integrating model outputs into product flows (APIs, feature flags) and measuring impact through experiments and product metrics.</li>\n<li>Experience with model lifecycle and operations: model registries, CI/CD for models, reproducible training, offline &amp; online parity, monitoring and incident response.</li>\n<li>Nice to have:</li>\n<li>Experience in fraud, risk, or marketing intelligence domains.</li>\n<li>Experience with feature-store products (Tecton / Chronon / Feast / internal) and unified pipelines.</li>\n<li>Experience with graph frameworks, graph feature engineering, or sequence embeddings.</li>\n<li>Experience optimizing inference at scale (Triton/ONNX/quantization, batching, caching).</li>\n</ul>\n<p><strong>Additional Information</strong></p>\n<p>Our mission at Plaid is to unlock financial freedom for everyone. 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