Machine Learning Engineer

Attain is seeking a Senior/Staff Machine Learning Engineer to own our production ML systems and build out the MLOps platform infrastructure that powers our suite of B2C financial services. This role will be highly hands-on and infrastructure-first, focused on designing, building, and operating the pipelines, platforms, and tooling that take models from experiment to reliable production service across our app portfolio—and on keeping those systems healthy, performant, and cost-effective once they're live.

You will work on the systems and infrastructure behind our high-impact predictive models, including the pipelines, feature infrastructure, model-serving, CI/CD, and observability that keep them reproducible, automated, monitored, and fast in production. Day to day, this means building the platform and automation that let us move fast without sacrificing performance—streamlining retraining and rollouts, tuning systems for speed and efficiency, and building the metrics and alerting that give us confidence to ship—while enabling data scientists to deploy and iterate on models quickly and safely. The ideal candidate combines strong software and platform engineering fundamentals with practical MLOps experience building and operating production ML systems from scratch, and treats modern AI tooling as a first-class part of how the work gets done—directing coding agents to write, test, and ship infrastructure code, with the judgment to know when to verify their work.

Build, deploy, and operate the production ML systems at the core of our EWA product, with a focus on reliability, performance, and fast, high-quality execution

Build and improve the pipelines and serving infrastructure behind our predictive models across consumer decisioning, fraud, churn, transaction intelligence, and other business-critical use cases

Own the production side of the model lifecycle: feature pipelines, deployment, CI/CD, monitoring, and automated retraining

Build and maintain reusable modeling pipelines, feature engineering systems, model-serving infrastructure, and production-quality code, deployed via Terraform and CI/CD into our GCP + Kubernetes environment

Instrument models and pipelines with monitoring, alerting, and automated retraining—defining the metrics and dashboards (e.g., Prometheus/Grafana) that surface drift and degradation and give us confidence to ship

Direct AI coding agents as a force multiplier to write, test, and ship infrastructure and pipeline code—and apply strong judgment about when to trust their output and when to verify it yourself

Automate manual, repetitive steps in the ML lifecycle so the team can move faster without sacrificing reliability

Partner with data scientists to give them fast, safe paths to deploy, iterate on, and retrain models in production

Collaborate with analysts, platform engineers, product managers, and business stakeholders to deliver ML systems with quality, efficiency, and precision

Identify new areas where platform improvements, automation, and MLOps tooling can improve product velocity and business outcomes

At Attain, we are passionate about finding people to continuously help us grow our organization. We encourage you to apply, even if your experience doesn’t match every detail on the job description. If we don’t see something that immediately fits, we will keep your resume on file for future opportunities.