Senior Software Engineer - Network Enablement (Applied ML)

Plaid

🔍 Observed

$180K - $270K USD

San Francisco First seen 3 weeks ago Last seen 3 hours, 22 minutes ago Lever

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Job Description

AI Summary

• Build and operate ML infrastructure for real-time fraud detection and trust scoring, including offline training pipelines, online feature serving, and low-latency inference endpoints that power partner-facing products. • Design and execute experiments (A/B tests, backtesting, shadow scoring) to validate product hypotheses and measure impact on fraud reduction, retention, and downstream KPIs. • Own productionization and reliability of ML systems including model CI/CD, observability, drift detection, PII-safe handling, and compliance across Plaid's network of 12,000 financial institutions. • Collaborate cross-functionally with Product, ML/Data Platform, Fraud, Foundational Modeling, and Privacy teams to scale network intelligence and ship auditable, reliable ML solutions. • Work on feature engineering, batch scoring pipelines, and integrating model inference into product logic (APIs, feature flags, backend flows) for bank intelligence and fraud prevention features.

We believe that the way people interact with their finances will drastically improve in the next few years. We’re dedicated to empowering this transformation by building the tools and experiences that thousands of developers use to create their own products. Plaid powers the tools millions of people rely on to live a healthier financial life. We work with thousands of companies like Venmo, SoFi, several of the Fortune 500, and many of the largest banks to make it easy for people to connect their financial accounts to the apps and services they want to use. Plaid’s network covers 12,000 financial institutions across the US, Canada, UK and Europe. Founded in 2013, the company is headquartered in San Francisco with offices in New York, Washington D.C., London and Amsterdam.

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

Responsibilities: 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. Qualifications: Must-haves: 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. Nice to have : Experience in fraud, risk, or marketing intelligence domains. Experience with feature-store products (Tecton / Chronon / Feast / internal) and unified pipelines. Experience with graph frameworks, graph feature engineering, or sequence embeddings. Experience optimizing inference at scale (Triton/ONNX/quantization, batching, caching).

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Job Information

Company:
Plaid
Location:
San Francisco
Job Type:
Full-Time
Experience Level:
Senior
Source:
Lever
Status:
Active

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