# Staff Engineer, ML/AI Platform

**Company:** [Attentive](https://hotfix.jobs/companies/attentive)
**Location:** Remote
**Role:** ML Engineering
**Salary:** $260k – $310k/yr
**Experience:** 5+ years
**Skills:** Python, Ray, MLflow, Metaflow, Spark, Kubeflow, Argo, Ml Platform, MLOps, Agentic Infrastructure, Model Lifecycle Management, Inference Systems, Data Pipelines, Champion/Challenger Testing
**Posted:** 2026-05-19

> Staff-level IC building and scaling the ML/AI platform infrastructure that enables training, deployment, and serving of models and agentic systems at massive scale. Focus on high-leverage architecture decisions and technical leadership across Attentive’s AI organization.

## Job Description

## What You’ll Accomplish

- **Setting Technical Direction** — Architect ML platform strategy spanning data pipelines, training infrastructure, and serving layers using cutting-edge tooling like Ray, MLFlow, Metaflow, Argo, and Spark.
- **Uplevel and Innovate Core AI & ML Stack** — Build and operate production-grade, low-latency ML serving layers with robust model lifecycle systems including champion/challenger testing, automated rollouts, versioning, and rollback capabilities.
- **Uplevel and Innovate Core AI & ML Stack** — Define and drive Attentive’s agentic stack.
- **Technical Leadership** — Provide ML infrastructure perspective in high-level discussions about Attentive’s AI strategy spanning multiple quarters and teams.
- **Technical Mentorship** — Mentor platform and ML engineers, actively championing team members.
- **Being the “Glue”** — Build universal interfaces, architectures, and patterns—like data access layers and prediction serving APIs—that bridge platform capabilities with product needs to streamline high-priority ML work across the organization.

## Your Expertise

- Experience to know what works, what doesn’t, and why in AI and ML systems.
- 5+ years focused specifically on ML Platform/MLOps, with deep understanding of gold-standard practices and best-in-class tooling.
- Proven track record of owning and building core components of ML platforms using tools like Spark, Ray, MLFlow, Kubeflow, or Metaflow.
- Built and operated a high-throughput agentic stack (MCP / data infrastructure, context store, orchestration, and prompt layer).
- Strong expertise in Python for both batch processing and online service frameworks.
- Experience designing and operating online and offline inference systems, understanding the critical differences and tradeoffs between them.

## Sample Projects

- Design and implement inference pipelines with champion/challenger shadow testing and automated model promotion.
- Lead and scale Attentive’s agentic stack from the ground up.
- Scale real-time feature streaming to handle low-latency, high-volume reinforcement learning workloads.
- Build a universal data access layer and prediction serving interface that powers ML capabilities across Attentive’s product suite.

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**Apply:** https://hotfix.jobs/jobs/d406b9e5-e071-449a-8a70-13aa71807e96
**Canonical:** https://hotfix.jobs/jobs/d406b9e5-e071-449a-8a70-13aa71807e96