Machine Learning Engineer – New York / Remote (US) | Visa + Salary Insights

📍 Location: us

🏷 Type: Not specified

Job Intelligence

📍 More in this location:
Browse AI jobs in us

🏷 Similar roles:

🌐 Explore all jobs:
View all AI job listings

Table of Contents

Job Overview

This role targets a Senior-Level Machine Learning Engineer specializing in LLM systems, adversarial ML, and production-grade pipelines. You will join a high-impact AI safety company building threat detection and moderation systems used by top-tier organizations. The ideal candidate brings 3–8+ years of experience deploying ML systems end-to-end, with strong exposure to real-world noisy data, LLM fine-tuning, and scalable infrastructure. This is not a research-only role—it demands hands-on ownership of systems that operate under real production pressure.

🌍 Work Eligibility & Location

🌍 Visa Sponsorship: Not explicitly stated
✈️ Relocation Support: Not specified
🏠 Remote Type: Fully Remote (US-based)
⏰ Timezone Requirement: Likely US timezones
🌐 Country Restrictions: Must be eligible to work in the US
🗣️ Language Requirement: English

This opportunity is structured as a Remote (US-only) role, meaning global applicants will face limitations unless they already have US work authorization. The absence of clear visa sponsorship signals suggests prioritization of domestic candidates. However, remote flexibility still offers strong appeal for US-based engineers seeking autonomy and distributed collaboration.

💰 Salary Intelligence

💰 Official Salary: $150,000 – $250,000
📊 Estimated Range: $170,000 – $240,000
📈 Level: Senior-Level

The compensation sits in the top percentile for applied ML roles, particularly for engineers working on LLM safety and moderation systems. The upper band reflects candidates with strong ownership of production systems and deep experience in scaling ML pipelines. Combined with bonuses and benefits, this package is highly competitive within the AI safety niche.

📊 Role Breakdown

This role is heavily execution-focused, with approximately 40% dedicated to building and deploying ML systems, including multi-stage classification pipelines optimized for low latency and high throughput. Around 25% involves LLM integration and fine-tuning using tools like OpenAI, Claude, and LLaMA, particularly for moderation and semantic understanding tasks. Another 20% is spent on data engineering and feedback loops, incorporating human-in-the-loop systems to continuously refine model performance. The remaining 15% focuses on evaluation, error analysis, and system optimization, including precision/recall trade-offs and failure mode analysis.

You will operate across the full ML lifecycle—data ingestion, training, deployment, monitoring, and iteration—with a strong emphasis on robustness and real-world reliability. Technologies include Python, NLP pipelines, semantic embeddings, vector search systems, and cloud platforms like AWS/GCP.

🧩 Required Skills & Fit

✅ Must: 3–8+ years building and deploying ML systems in production
✅ Must: Strong experience with LLMs, fine-tuning, and prompt engineering
✅ Must: Proficiency in Python, NLP pipelines, embeddings, and vector search
➕ Bonus: Experience with real-time ML pipelines or large-scale moderation systems
➕ Bonus: Knowledge of multimodal ML (vision, OCR, audio) or agentic systems

📈 Difficulty & Competitiveness

⚡ Level: High
📊 Experience barrier: 3–8+ years
🧠 Skill complexity: Advanced ML + LLM systems
🌍 Competition: Global senior AI talent

This is a high-difficulty, senior-level role requiring proven production experience. Candidates must demonstrate not only theoretical ML knowledge but also real-world deployment expertise. The combination of LLMs, adversarial data handling, and system scalability significantly raises the bar. Expect competition from experienced engineers in top AI companies and startups.

🚀 Career Impact

⭐ Impact Rating: ⭐⭐⭐⭐☆
🏢 Brand value: Emerging AI safety leader
📚 Skill growth: LLM systems + production ML
🚀 Future opportunities: AI safety, ML leadership roles

This role delivers strong career acceleration in AI safety and applied LLM engineering. You will gain hands-on exposure to high-stakes production systems, positioning you for future roles in AI infrastructure, safety research, or ML leadership. The experience is particularly valuable for engineers targeting frontier AI labs or security-focused AI teams.

📋 Key Responsibilities

You will design, build, and deploy ML systems focused on classification, moderation, and threat detection. This includes developing multi-stage pipelines and integrating LLMs into production workflows. You will implement feedback loops using human-in-the-loop evaluation to improve model performance over time. A critical part of the role is analyzing errors, reducing false positives, and optimizing precision/recall trade-offs.

Additionally, you will collaborate with researchers and engineers to generate training data, test edge cases, and ensure system robustness. You will work extensively with Python, cloud platforms, NLP tooling, and embedding systems to deliver scalable and reliable ML infrastructure.

🎯 Application Strategy

🎯 Best apply method: Direct company site with tailored resume
🔥 Highlight: Production ML systems you have deployed
🔥 Highlight: LLM experience (fine-tuning, prompt engineering)
❌ Avoid: Academic-only or research-heavy profiles without deployment
❌ Avoid: Generic resumes lacking measurable impact

🧠 Application Optimization (Adaptive)

This section is personalized by seniority.

How to use: Paste into ChatGPT, Claude, or Gemini

You are a senior technical recruiter.

Role:
Machine Learning Engineer focused on LLM systems, adversarial data, and production ML pipelines in AI safety.

Candidate:
[Paste CV]

Optimize for this role.

Focus:

Signal strength
Role alignment
Missing high-impact elements

🧠 Fit & Positioning Analysis

Evaluate your match before applying.

Act as a hiring panel.

Evaluate:

Match score
Strengths
Gaps
Positioning improvements

📅 Application Signals

This role shows high urgency due to its focus on production deployment and immediate system ownership. The combination of remote flexibility and high salary will drive strong competition from experienced ML engineers. Early application with a highly tailored profile significantly improves success probability.

🚀 Resume Optimization for This Role

⚡ Takes less than 2 minutes — optimize specifically for this position before applying

🎯 0/5 completed

Tailoring your CV to this exact role significantly increases your chances.

Production ML Pipelines: Demonstrate end-to-end ML deployment with measurable latency and throughput metrics

LLM Integration: Highlight fine-tuning, prompt engineering, and OpenAI or LLaMA implementations

Moderation Systems: Showcase abuse detection or classification systems with precision recall improvements

Embedding Systems: Detail semantic search or vector database usage with real-world datasets

Feedback Loops: Quantify human-in-the-loop systems improving model accuracy or reducing errors

✅ Ready to apply — your profile is aligned with this role

Next Step: Tailor your CV to this role and apply through the official page below

🔗 Apply for this Job

Apply on Company Site

✅ Job Source & Verification

This job listing is based on a direct employer posting, ensuring high source credibility. Role details, responsibilities, and compensation have been extracted and structured from the original description. Last updated: April 2026.