Machine Learning Engineer

Chicago, IL;
New York, NY;
Redwood City, CA

About Attain

Built for consumers and companies, alike.

Klover's engineering team powers one of the fastest-growing fintech platforms in the U.S., supporting over one million active users each month. Our systems process and move more than $1.5 billion annually, enabling real-time access to financial tools, rewards, and services that help people improve their day-to-day lives.

As part of this team, you'll help design, build, and scale the systems that underpin Klover's core products and platform. You'll work on high-impact, production-grade systems that prioritize reliability, security, and performance, and that integrate with a broad ecosystem of internal and external services. The work you do will directly shape how users interact with Klover's products, access their money, and experience transparent, low-fee financial services.

Klover engineers collaborate closely with colleagues across backend, frontend, data science, and product teams to deliver scalable, high-quality solutions for a rapidly growing user base. You'll have the opportunity to work with modern technologies and architectures while helping define and evolve the next generation of inclusive, data-powered financial products—building systems and interfaces that emphasize reliability, privacy, and performance at scale.

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.

• Chicago, IL: 4 days in-office; 1 day remote

• 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…