Senior Machine Learning Engineer, Klover
Financial services & cash advance app
Be an early applicant
Financial services & cash advance app
101-200 employees
Be an early applicant
101-200 employees
Company mission
To give everyone access to modern financial services by leveraging their most valuable asset, their data.
Role
Who you are
- 5+ years of direct experience as a Machine Learning Engineer, ML Platform Engineer, MLOps Engineer, Applied Scientist or similar role building and operating production ML systems
- Strongly preferred: degree in STEM field such as Computer Science, Statistics, Economics, Mathematics, Engineering, Physics, Operations Research, or a related quantitative field
- Demonstrated ability to apply critical thinking, abstract reasoning, and sound engineering judgment to complex, ambiguous technical and business problems
- Strong expertise deploying, serving, monitoring, and operating ML models in production—including feature engineering systems, training/serving parity, retraining, and model performance diagnostics
- Experience building low-latency online model serving (e.g., gRPC/microservices, ideally with a service mesh such as Istio) for real-time decisioning
- Hands-on MLOps experience: pipelines, CI/CD for ML, containerization (Docker), orchestration (Kubernetes), infrastructure-as-code (e.g., Terraform), and workflow schedulers (e.g., Airflow)
- Experience with model versioning, reproducibility, and safe progressive rollout (shadow, canary, champion-challenger) of models in production
- Demonstrated fluency directing AI coding agents (e.g., Claude Code, Cursor, or similar) to build, operate, and debug real ML systems—with experienced judgment on verifying their work
- A track record of replacing manual, repetitive ML workflows with durable automation
- Experience building the infrastructure behind high-impact applied ML use cases such as credit decisioning, risk modeling, fraud, churn, or consumer behavior modeling
- Familiarity with model explainability, auditability, and the compliance considerations of regulated decisioning (a plus for credit/fintech contexts)
- Strong software and platform engineering fundamentals
- Strong Python coding skills, with the ability to build pipelines, services, and production-quality tooling from scratch; experience with a systems or backend language such as Go or Rust is a plus
- Experience with distributed computing and GPU-accelerated workloads (e.g., Spark, Ray, Dask, or distributed training/inference), including scaling data and model pipelines across clusters
- Strong SQL skills and experience with cloud data warehouses and operational databases (e.g., BigQuery, Spanner), including working with large, messy, real-world datasets
- Experience with observability tools such as Prometheus, Grafana, or Datadog
- Experience with cloud computing services or platforms; GCP preferred
- Willingness to roll up your sleeves and wear multiple hats across engineering, infrastructure, and ML execution based on business needs
- Strong written and verbal communication skills, including the ability to explain technical topics to both technical and non-technical audiences
What the job involves
- 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
Our take
Klover, a subsidiary of Attain, offers users interest free cash advances as well as a growing range of financial services and budgeting tools on its free-to-use app. Rather than charging users a fee, Klover leverages their bank transaction data to tailor targeted advertising.
It’s worth keeping in mind that while Klover essentially pays users for their data, it does not share or sell personal information – a key factor in its model.
With heavy investment from Mercato Partners and the hiring of former Pinterest VP of Sales and Partnerships Meredith Guerriero, Klover is growing impressively, and has ambitions to one day become a full service online bank.
Steph
Company Specialist at Welcome to the Jungle
Company
Funding (last 2 of 3 rounds)
Nov 2022
$25m
SERIES B
Aug 2021
$60m
SERIES A
Total funding: $89m
Company HQ
River North, Chicago, IL
Leadership
Brian Mandelbaum
(Co-Founder & CEO)Founded and was CEO of Clearstream.tv (acquired by Engine Group in 2015), then became Global CEO of re-branded EMX Digital. Is a <Managing Partner at Mayne Capital and Board Member at B2B insurance data and software company Fize. Former VP, Digital Strategy at Saatchi & Saatchi.
Dominic Bennett
(Co-Founder & CTO)Co-founded EngageHD, which was acquired by Clearstream.tv in 2014, then became CTO of Clearstream. Spent six years at advertising services company Turn, including a spell as Senior Vice President, following seven years as VP, Analytics & Engineering at Claria.
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