> Markdown version of [/jobs/ext/3690041-machine-learning-engineer](https://www.wearedevelopers.com/jobs/ext/3690041-machine-learning-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Machine Learning Engineer

- **Company:** Vercel Inc.
- **Location:** San Francisco, CA, United States (Remote available)
- **Experience:** Expert
- **Salary:** $208,000.0 - $312,000.0
- **Contract:** Permanent contract
- **Skills:** JavaScript (Programming Language), Python (Programming Language), Machine Learning, Open Source Technology, Software Engineering, TypeScript, Feature Store, Large Language Models, Prompt Engineering, Production Code, Machine Learning Operations
- **Published:** October 11, 2026
- **Apply:** https://startup.jobs/machine-learning-engineer-trust-safety-vercel-10363662

## About the Role

* 5+ years of software engineering experience building and operating production systems at scale
 * Wrote production code in Python, plus JavaScript/TypeScript or Go
 * Built and maintained ML infrastructure, such as training and serving pipelines, feature stores, or evaluation tooling
 * Integrated LLMs into production systems, including prompt engineering and evaluation

## Description

We are looking for a Machine Learning Engineer on our Trust & Safety Engineering team, to build and operate the production systems that detect and stop abuse on the platform at internet scale. This is an engineering-first role, roughly 80% writing and shipping production code (services, pipelines, and infrastructure) and 20% modeling, taking detection and classification approaches from prototype to hardened systems. This is a hybrid role based in San Francisco or New York City, with three days a week in the office., * Design, build, and operate production systems that detect and disrupt abusive behavior across the platform, with an emphasis on reliability, scalability, and observability

 * Build and maintain machine learning (ML) infrastructure, including training pipelines, feature pipelines, serving infrastructure, and evaluation frameworks
 * Ship large language model (LLM) and classical ML approaches for abuse detection and classification, turning prototypes into maintainable code
 * Own systems end to end, from integrating a model into the platform through deployment, monitoring, and iteration
 * Work with security, product, and infrastructure teams to turn abuse patterns and detection logic into production systems