Machine Learning & AI Engineer – Remote (US) | Salary & Hiring Insights

📍 Location: us

🏷 Type: Not specified

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Job Overview

This role is for a Machine Learning & AI Engineer building production-grade intelligent systems for a large-scale, mission-driven organization focused on personalization, marketing intelligence, and consumer experience platforms. The company operates at national scale, processing billions of interactions across digital ecosystems. The ideal candidate is a mid-level engineer (4–6 years experience) with strong Python, SQL, and ML system design expertise, capable of turning AI prototypes into scalable production services. You will work closely with product, data science, and engineering teams in squad-based delivery structures focused on measurable business impact.

🌍 Work Eligibility & Location

This is a fully Remote opportunity designed for distributed engineering teams across the United States. While global applicants may attempt, operational alignment strongly favors US-based professionals due to collaboration across product and marketing squads. No explicit visa sponsorship details are provided, making eligibility primarily dependent on work authorization within the US market.

💰 Salary Intelligence

The compensation is highly competitive for a mid-level AI/ML role, especially considering the scope of production systems handling large-scale personalization and recommendation workloads. The dual salary structure (hourly and annualized range) indicates flexible contract-to-hire positioning, with strong upside for engineers who demonstrate production impact and scalability expertise.

📊 Role Breakdown

This position focuses on building and scaling AI-driven personalization systems that operate across massive datasets and real-time consumer interactions. You will design machine learning pipelines, develop feature engineering frameworks, and deploy models that directly influence customer engagement and revenue outcomes. A significant part of the role involves productionizing models using Python and SQL, ensuring systems are optimized for performance at billions-of-event scale. You will also work on AI-assisted development workflows, including prompt engineering and agent-based system design where applicable. Around 40% of your work will focus on data and feature engineering, 30% on ML system deployment and scaling, and 30% on cross-functional collaboration with product and analytics teams. The role requires strong engineering maturity to bridge experimentation and production reliably.

🧩 Required Skills & Fit

📈 Difficulty & Competitiveness

This is a technically demanding role due to its focus on scaling AI systems across enterprise-level data volumes. Candidates must demonstrate not just ML knowledge but also production engineering maturity. The combination of personalization systems, marketing intelligence, and real-time decisioning increases competitiveness significantly, making strong system design skills essential.

🚀 Career Impact

This role offers strong career acceleration for engineers targeting advanced AI infrastructure and personalization systems. Exposure to large-scale ML deployment, real-time decisioning, and AI-driven product ecosystems positions you for future roles in Staff Engineer or AI Platform Architect tracks.

📋 Key Responsibilities

You will build and maintain machine learning pipelines that power personalization and recommendation systems at scale. Responsibilities include designing AI-driven services, deploying models into production APIs, and optimizing inference performance for large datasets. You will work with Python-based ML stacks and SQL-driven data systems to extract insights and engineer features. A key responsibility is transforming prototypes into scalable systems that support billions of interactions. You will also collaborate with stakeholders to translate business requirements into technical AI solutions, ensuring alignment with marketing and product goals. Continuous optimization, monitoring, and iterative model improvement are central to the role.

🎯 Application Strategy

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🧠 Fit & Positioning Analysis

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📅 Application Signals

This role is highly competitive due to its intersection of AI engineering, personalization systems, and large-scale data infrastructure. Early applicants with strong production ML experience have a clear advantage. The company prioritizes engineers who can independently ship scalable systems. Delayed applications reduce visibility in fast-moving hiring pipelines where strong candidates are evaluated quickly across engineering squads.

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✅ Job Source & Verification

This listing is derived from a structured enterprise job description typical of large-scale AI-driven organizations. Details reflect validated responsibilities in machine learning engineering, production AI systems, and personalization platforms. Information was last updated based on the provided job specification and aligns with current industry standards for mid-level AI engineering roles in the US market.