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Job Description: Artificial Intelligence Engineer
Elite Recruiter · United States (Remote)

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Meet the hiring team

Juan Sebastian Moreno Sanchez  
 3rd
| Talent Acquisition Manager | Head Hunting Manager | HR | Business Development | Key Account Management | Organizational Strategy | Leadership | Strategic Partnerships | Revenue Growth | Closer High Ticket| +12K 👤
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About the job
The Role
We are looking for a Senior Machine Learning / AI Engineer to join our core engineering team. You will work closely with our CTO and senior engineers to design, build, and scale end-to-end ML systems.
This is a high-impact role with significant autonomy, technical ownership, and influence over the future of our platform.


Compensation & Equity

Base salary: $250,000 – $350,000 USD
Equity: Meaningful equity packages available
Compensation is flexible for exceptional candidates who bring outsized impact




What You’ll Do

Design, build, and deploy end-to-end machine learning systems
Work deeply with Large Language Models (LLMs) and related architectures
Build and optimize small language models and applied AI systems
Contribute to system architecture, scalability, and performance
Collaborate closely with senior engineers, product stakeholders, and leadership
Translate complex technical concepts into clear written documentation
Technical docs
White papers
Patents (as applicable)
Balance technical excellence with practical, commercial outcomes
What We’re Looking For (Non-Negotiable)

Deep, hands-on experience with LLMs from the ground up
Not just fine-tuning or API usage
Strong understanding of end-to-end ML systems
Proven ability to take models from concept to production
A practical, product-oriented mindset
Strong written and verbal communication skills
Ability to work independently while being a strong team collaborator
Low ego, high ownership, and high accountability


Core Technical Skills (Top Priorities)
End-to-end experience with Large Language Models (LLMs)
 Building, training, fine-tuning, and deploying models from the ground up (not just APIs)
Strong hands-on ML engineering
 Taking models from research → production in real, commercial systems
Advanced proficiency in PyTorch or TensorFlow
 With deep understanding of modern transformer architectures
Applied NLP expertise
 Embeddings, tokenization, language model pipelines, and RAG systems
Distributed ML systems & architecture
 Designing scalable, reliable ML pipelines and inference systems
Strong Python engineering
 Writing clean, production-ready, well-tested code
GPU & performance awareness
 Experience optimizing ML workloads (CUDA / HPC knowledge is a plus)
Cloud & deployment experience
 Deploying ML systems on AWS, Azure, or GCP using modern MLOps practices
Research-to-production mindset
 Ability to translate complex ideas into practical, scalable solutions
Excellent technical writing
 Clear documentation, architecture specs, and white papers