# Senior Machine Learning Engineer at Attain

- Company: Attain
- What the company does: Attain helps food distributors boost revenue, digitize orders, manage invoices, and collect payments by offering stores a branded online experience to buy inventory from. Backed by Y Combinator.
- Company website: https://joinattain.com/
- Type: Startups (AI role)
- Level: Senior
- Location: Chicago, IL
- Work setup: On-site
- Posted: 2026-09-02
- Apply by: 2026-10-17
- Apply: https://job-boards.greenhouse.io/attain/jobs/6179241004
- Page: https://www.1752.vc/careers/jobs/attain-senior-machine-learning-engineer/

## About the role

Attain is seeking a Senior 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.

## What they're looking for

- 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)

Tags: Consumer Technology