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AI/Machine Learning Engineer Intern – Remote USA | Visa + Salary Insights

Job Overview

This AI/Machine Learning Engineer Intern role at SentinelOne is a Remote opportunity designed for PhD-level candidates looking to build production-grade AI systems. Positioned at the intersection of cybersecurity and AI engineering, this role focuses on developing LLM-powered backend systems and real-world AI applications. Ideal candidates bring strong Python engineering, experience with LLMs or RAG systems, and a product mindset. This is not a research-only role but a hands-on engineering position delivering scalable AI solutions.

🌍 Work Eligibility & Location

This role is strictly Remote within the United States, which limits global accessibility despite being remote. Candidates outside the US may face eligibility barriers unless independently authorized to work. No clear visa sponsorship signals are provided, making this less accessible for international applicants. However, within the US, it offers strong flexibility and access to a top-tier AI security company.

💰 Salary Intelligence

The compensation sits at the higher end for technical internships, especially for AI-focused roles. At $31/hour, it reflects the expectation of advanced academic experience such as a PhD program. While not comparable to full-time AI engineering salaries, it is competitive for internships in AI + cybersecurity, particularly given the exposure to production systems and enterprise-grade infrastructure.

📊 Role Breakdown

This role is heavily weighted toward production AI engineering rather than experimentation. Approximately 40% of the work involves backend development using Python, building scalable services that support AI-driven features. Another 30% focuses on LLM integration and agentic workflows, including implementing retrieval-augmented generation (RAG) and ensuring reliability in real-world conditions. Around 20% is dedicated to system integration, connecting APIs and handling failure scenarios such as rate limits and service degradation. The remaining 10% involves evaluation systems, including benchmarking and feedback loops to improve AI outputs. Key actions include designing APIs, deploying AI services, and collaborating cross-functionally with product and research teams. This structure makes the role highly practical and aligned with real-world AI deployment challenges.

🧩 Required Skills & Fit

📈 Difficulty & Competitiveness

This role is highly competitive due to its requirement for PhD-level candidates and hands-on experience with LLMs in production. Even though classified as Entry-Level, the expectations align closer to early-career specialists. The combination of AI systems engineering and cybersecurity context significantly raises the bar, making it difficult for candidates without strong project portfolios.

🚀 Career Impact

This role offers significant career acceleration by combining AI engineering with enterprise cybersecurity systems. You gain exposure to real-world deployment challenges, which is critical for transitioning into high-paying AI engineering roles. The brand association and technical depth can strongly improve your positioning for future roles in AI infrastructure, LLM platforms, and applied AI.

📋 Key Responsibilities

You will design and build backend services using Python to support AI-driven applications. A major part of the role involves developing LLM-powered features, including agentic workflows and RAG pipelines. You will integrate APIs and internal systems, ensuring resilience under failure conditions like rate limits. Collaboration is critical, requiring you to work with product managers and researchers to translate ideas into deployable systems. Additionally, you will contribute to evaluation frameworks, building benchmarks and feedback loops to improve AI reliability and output quality.

🎯 Application Strategy

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🧠 Fit & Positioning Analysis

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📅 Application Signals

This opportunity shows moderate urgency as part of a structured internship program, but early application is critical due to high competition. Given the niche requirement of PhD candidates with LLM experience, the applicant pool is smaller but highly qualified. Applying early with a strong portfolio significantly improves success probability.

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✅ Job Source & Verification

This job listing is sourced directly from the official SentinelOne careers page, ensuring high source credibility and accuracy. All compensation and role details are based on the employer’s published information. Last updated: April 2026, making this a current and verified opportunity for applicants.