Remote AI/ML Engineering Manager - Platform & Product

Rula; United States; Remote; USD 180,000 - 260,000; Full time

Benefits offered by this job

Remote-first

Transparent pay ranges

Health benefits

401(k)

Time-off policy

Parental leave

EAP

Dept stipend

Home office stipend

Wellness stipend

Job summary

Rula is seeking an Engineering Manager for AI/ML to lead a high-performing AI-native team. You will guide senior engineers to rapidly deliver features such as transcript summarization and search relevance while shaping core AI/ML infrastructure like feature stores and observability platforms.

You will balance rapid execution with rigorous safety, ensuring models scale safely and governance is maintained across deployments.

Qualifications

• 8+ years in software engineering and ML, with distributed backend design in cloud environments
• 3+ years engineering management, hiring and leading Senior/Staff engineers
• 3+ years leading core ML infrastructure (feature stores, MLOps, deployment, observability)
• 2+ years delivering production AI features including NLP / transcript summarization / search
• Experience delivering both user features and backend platform work across releases

Responsibilities

• Lead AI/ML engineering team to deliver user-facing features like transcript summarization and search relevance
• Architect and scale core ML infrastructure (feature store, observability, pipelines)
• Balance execution speed with rigorous safety and clinical considerations
• Drive cross-functional collaboration to align product and platform initiatives
• Ensure reliable delivery across multiple production release cycles

Skills

Software engineering & ML leadership

Engineering management

ML infrastructure

LLM integration & NLP

Release management

Tools

Feature stores

MLOps pipelines

Model deployment

AI observability

Job description

Rula is seeking an Engineering Manager for AI/ML to lead a high-performing AI-native team. You will guide senior engineers to rapidly deliver features such as transcript summarization and search relevance while shaping core AI/ML infrastructure like feature stores and observability platforms.

You will balance rapid execution with rigorous safety, ensuring models scale safely and governance is maintained across deployments.