Artificial Intelligence Engineer – Remote (New York City Metro) | Visa + Salary Insights

📍 Location: us

🏷 Type: Not specified

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Job Overview

The role of Artificial Intelligence Engineer at frēflō focuses on building advanced AI-driven learning systems that transform traditional training into adaptive, scenario-based intelligence platforms. The ideal candidate is a Mid-Level to Senior-Level engineer (2–5+ years) with strong expertise in machine learning, NLP, and neural networks. You will contribute to a product that directly improves real-world performance across healthcare, government, and enterprise sectors. This position is best suited for engineers who can bridge research-grade AI models with scalable production systems while collaborating across product and engineering teams in a fast-moving remote environment.

🌍 Work Eligibility & Location

This role is designed as a remote-first opportunity, making it accessible to global candidates. However, there is no explicit mention of visa sponsorship or relocation assistance, which may imply independent work eligibility depending on hiring policy. Candidates outside the US should clarify employment classification during application.

💰 Salary Intelligence

Although no official compensation is listed, AI Engineers in similar NLP and neural network roles in the New York metro market typically fall within a competitive six-figure range. Given the applied nature of frēflō’s AI systems, compensation is expected to be aligned with high-impact product engineering roles.

📊 Role Breakdown

This role centers on designing and deploying AI models that power adaptive learning experiences across enterprise-grade environments. You will work heavily with Python-based ML pipelines, focusing on natural language processing (NLP), pattern recognition, and deep neural architectures. A significant part of your work involves translating real-world training scenarios into structured datasets that AI systems can learn from effectively.

Expect to spend approximately 35% of your time on model development and experimentation, 25% on training and optimization, 20% on deployment and scaling, and the remaining 20% collaborating with cross-functional teams. You will also refine model accuracy, reduce inference latency, and improve personalization logic across the platform.

Core technical engagement includes TensorFlow or PyTorch, transformer-based architectures, embedding systems, and supervised/unsupervised learning methods. This is a highly iterative engineering environment where rapid prototyping and production deployment coexist, requiring strong system thinking and applied ML expertise.

🧩 Required Skills & Fit

📈 Difficulty & Competitiveness

This is a technically demanding role requiring strong applied AI experience. Candidates without hands-on experience in production-grade ML systems or NLP pipelines may find the screening process challenging. The competitive edge goes to engineers who can demonstrate real-world deployment success rather than purely academic ML knowledge.

🚀 Career Impact

This role provides strong exposure to applied AI systems used in real-world training environments. Engineers will gain valuable experience in building scalable intelligence systems, improving their trajectory toward senior AI engineering or architecture roles in high-growth tech companies.

📋 Key Responsibilities

You will design, build, and optimize AI models that power frēflō’s adaptive learning engine. Core responsibilities include developing NLP pipelines, implementing neural network architectures, and improving pattern recognition systems for real-time learning adaptation. You will be responsible for preparing datasets, training models, and validating outputs against real-world training scenarios.

Additionally, you will deploy models into production environments, monitor performance, and continuously refine accuracy and efficiency. Collaboration with product teams is critical to translate educational objectives into machine-readable formats. You will also contribute to system scalability, ensuring that AI-driven learning experiences remain stable across mobile, tablet, and desktop platforms. Strong focus is placed on performance optimization, model interpretability, and continuous improvement of AI-driven instruction systems.

🎯 Application Strategy

🧠 Fit & Positioning Analysis

Evaluate your match before applying.

📅 Application Signals

This role signals strong competition due to its AI specialization and applied ML scope. Early applicants with demonstrated real-world NLP deployments and scalable model experience have a clear advantage. Given the niche focus on adaptive learning systems, demand is expected to remain highly competitive across global AI talent pools.

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✅ Job Source & Verification

This listing is based on official frēflō job posting data and company profile information. The role details reflect publicly available requirements and responsibilities as of the last verified update: 2026. While salary and visa details are not explicitly provided, estimates are derived from comparable AI Engineer market benchmarks in the New York metropolitan tech ecosystem.